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Towards a paradigm shift in chronic low back pain? Identification of patient profiles to guide treatment Miranda van Hooff Towards a paradigm shift in chronic low back pain? Identification of patient profiles to guide treatment Miranda L. van Hooff 2 Colofon Towards a paradigm shift in chronic low back pain? Identification of patient profiles to guide treatment Beoordelingscommissie: Prof. dr. R.H.M.A. Bartels Dr. J. van Limbeek Prof. dr. J. Nijs Prof. dr. B.J. van Royen Prof. dr. M.W. van Tulder This thesis was prepared within the Department of Orthopaedics and the Department Research of Sint Maartenskliniek, Nijmegen, The Netherlands The publication of this thesis was financially supported by Sint Maartenskliniek, Nijmegen, The Netherlands, the Nederlandse Orthopaedische Vereniging and the Dutch Spine Society Cover design and layout by: burorub grafisch ontwerp, Nijmegen Printed by: Koninklijke Van der Most BV, Heerde ISBN: 978 90 9030218 8 (paperback version) ISBN: 978 90 826698 0 0 (digital version) © 2017 Miranda L. van Hooff, Deventer, The Netherlands ([email protected]) Except Chapter 07 © 2015 Wolters Kluwer Health Inc; Chapter 08 © 2016 Elsevier Inc. Open Access: Chapters 02-06 and 09 are distributed under the terms of Creative Commons Attribution Licence, which permits any use, distribution, and reproduction in any medium provided the original author(s) and the source are credited. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system of any nature, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the holder of the copyright. 3 VRIJE UNIVERSITEIT Towards a paradigm shift in chronic low back pain? Identification of patient profiles to guide treatment ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Geneeskunde op vrijdag 21 april 2017 om 11.45 uur in de aula van de universiteit, De Boelelaan 1105 door Miranda Lisette van Hooff geboren te Diepenveen 4 promotoren: prof.dr. M. de Kleuver prof.dr. R.W.J.G. Ostelo copromotor: dr. M. Spruit 5 Table of contents Chapter 01 General introduction 1.1 Chronic low back pain 1.2 Interventions for chronic low back pain 1.3 Outcomes of interventions for chronic low back pain 1.4 Aims and Outline of this thesis 9 9 11 12 16 Theme A: Introduction of a combined physical and psychological programme 25 Chapter 02 Daily functioning and self-management in patients with chronic low back pain after an intensive cognitive behavioural programme for pain management. European Spine Journal 2010; 19(9):1517-26 27 Chapter 03 A short, intensive cognitive behavioral program for pain management improves long term daily functioning, quality of life and reduces healthcare use in patients with Chronic Low Back Pain. European Spine Journal 2012; 21(7): 1257-64 43 Chapter 04 Predictive factors for successful clinical outcome one year after an intensive pain management programme for chronic low back pain. European Spine Journal 2014; 23(1):102-12 57 Theme B: Outcomes assessment 75 Chapter 05 Evidence and practice of spine registries – A systematic review and recommendations for future design of registries. Acta Orthopaedica 2015; 86(5): 534-44 77 Chapter 06 A proposed set of metrics for standardized outcome reporting in the management of low back pain. Acta Orthopaedica 2015; 86(5): 523-33 111 6.5 Appendix to Chapter 5 and 6. Guest editorial: Spinal disorders, quality based healthcare and spinal registers. Acta Orthopaedica 2015; 86(5): 521-32 130 Chapter 07 Validation of the Dutch Oswestry Disability Index version 2.1a: validation of a Dutch language version. Spine 2015; 40(2): E83-90 135 Chapter 08 Determination of the Oswestry Disability Index equivalent to a ‘satisfactory symptom state’ in patients undergoing surgery for degenerative disorders of the lumbar spine – A Spine Tango registrybased study. Spine J. 2016;16(10):1221-1230. 151 6 Theme C: Prediction of outcomes 167 Chapter 09 The Nijmegen decision tool for chronic low back pain. Development of a clinical decision tool for secondary or tertiary back care specialists. PLoS One 2014; 19(8): e104226 169 Chapter 10 Patient-reported factors partly predicted referral to spinal surgery in a consecutive cohort of 4,987 chronic low back pain patients. [Under review] 193 Chapter 11 Prognostic patient-reported profiles for referral to secondary or tertiary spine specialists – The Nijmegen Decision Tool for Chronic Low Back Pain. [Submitted] 215 Chapter 12 Summary & General discussion 247 Key points thesis 271 Dutch summary | Nederlandse samenvatting Acknowledgements | Dankwoord Curriculum Vitae Publications Theses Sint Maartenskliniek 277 285 291 295 303 7 Chapter 01 General introduction 01 02 03 04 05 06 07 08 09 10 11 12 8 01 02 03 04 05 06 07 08 09 10 11 12 Content 1.1 1.2 1.3 1.4 Chronic low back pain Interventions for chronic low back pain Outcomes of interventions for chronic low back pain Aims and Outline of this thesis General introduction 1.1 Chronic low back pain Prevalence and incidence Low back pain is a highly prevalent health condition globally and leads to individual suffering and a considerable burden for society. Recent research shows that, worldwide, low back pain is responsible for more years lived with disability than any other health condition [1,2]. Many people with low back pain have ongoing and recurrent complaints [3,4], and at a societal level there are substantial costs related to low back pain as a consequence of healthcare expenditure, disability insurance, and productivity loss caused by work absenteeism and work loss [5,6]. The mean global one-year point prevalence of low back pain is estimated to be 38.0% (±19.4) [7]. In the Netherlands, approximately 44% of the adult population experiences at least one episode of low back pain, and one in five (21%) report persistent back pain resulting in chronic low back pain (CLBP)[8,9], defined as low back pain lasting for more than three months [10] with substantial limitations in functional activities after one year [8,11]. Approximately 14% of the Dutch adult CLBP population is incapable of work [9]. Due to ageing and population growth, it is expected that the total number of CLBP-patients will increase. In the Netherlands in 2007 the total cost of back pain to Dutch society was estimated to be €3.5 billion, which equates to 0.6% of the gross national product. This amount includes indirect costs such as productivity loss and costs incurred by society due to morbidity and mortality, which represent the majority of these total costs [5]. In 2007, more than 500,000 patients with back pain were referred to secondary or tertiary spine care and more than 30,000 underwent a surgical intervention for which the related costs were estimated to be 23 million euro. However, these numbers are likely to be underestimates, because research shows that despite initiatives to try to discourage the use of expensive diagnostic imaging in early stages, there has been no change in the amount of ineffective referrals to secondary care [5,12]. Symptom or diagnosis? The CLBP population is heterogeneous and the term CLBP lacks diagnostic clarity. CLBP is an umbrella term, covering two main categories of lumbar spine disorders: degenerative and nondegenerative [13]. In clinical practice, an accurate patho-anatomical diagnosis of the cause of CLBP based on biomedical indicators can be made in only 10% of the patients (e.g. isthmic spondylolisthesis, lumbar spinal stenosis, lumbar disc herniation, scoliosis, spinal fracture, axial spondylarthropathy, neoplasm, Morbus Scheuermann) [14]. Hence, in the majority of patients, low back pain is a symptom referring to the location rather than reflecting a specific diagnosis [15,16]. Because in the vast majority of patients (90%) the aetiology is unknown [14], these patients are diagnosed as having degenerative low back pain and are often labelled as ‘non-specific’. However, the term ‘non-specific’ is thought to be meaningless [17] and therefore the term ‘degenerative lumbar spine disorder’ seems more appropriate (Figure 1.1). 9 01 10 01 General introduction Figure 1.1 Representation of the lumbar spine (A-B) and of a degenerative lumbar spine disorder (i.e. chronic low back pain; C) (A) (B)(C) Schematic representation of the lumbar spine (A); ‘normal’ lumbar spine (MRI; B); ‘degenerative’ lumbar spine (MRI; C) Aetiology and prognosis The traditional model of clinical practice incorporates diagnosis based on aetiology, prognosis, and treatment of the condition or disease [18]. Aetiology refers to the study of causality of diseases [19]: the relationship between cause and effect. To describe aetiology in CLBP, in Western medicine the biomedical model, derived from Louis Pasteur's germ theory of disease, has been the dominant force. Following this model, it is assumed that CLBP is fully accounted for by deviations from the norm of measurable biological (i.e. somatic or patho-anatomic) factors. However, in the vast majority of CLBP cases no patho-anatomical diagnosis can be made. The model is also exclusive, since any symptoms that cannot be explained in biological terms are excluded from consideration, and thereby it leaves no room for the social, psychological, and behavioural dimensions. Yet, these aspects are important in chronic diseases and have led to a paradigm shift from purely biological to the bio-psychosocial model, which was introduced by Engel in 1977. Its scope is determined by the historic function of the physician to establish whether the person soliciting help is "sick" or "well" — and if sick, why sick and in which ways sick — and then to develop a rational programme to treat the illness and restore and maintain health [20]. Studies on the prognosis of CLBP show frequent persistence of complaints [21] whilst an inverse relationship for the prognosis of a satisfactory outcome with symptom duration has been shown [22]. It is estimated that one to two years after the initial onset, 60-80% of patients consulting healthcare professionals still have pain and have not fully recovered [23,24]. Amongst patients with back complaints for more than three years, the chances of recovery are even smaller [22]. The literature demonstrates that persistence of CLBP is associated with pain, disability and psychological status at onset [22,25-27]. Many authors have emphasised bio-psychosocial influences on the development and persistence of CLBP [21,22,28,29]. However, the aetiology of CLBP remains largely unknown and the prognosis detrimental. Owing to this lack of diagnostic clarity, targeted interventions with successful outcomes remain a challenge. General introduction 1.2 Interventions for chronic low back pain The failure to identify underlying causes is one of the reasons why a plethora of invasive and non-invasive interventions exist for the same symptom [30]. Therefore, practice variation exists amongst healthcare providers, and considerable uncertainty exists amongst major stakeholders as to which interventions are (cost) effective. For example, the rates of lumbar spine surgery vary largely within and between countries [31-34]. Despite decades of research and improved quality of clinical trials, the treatments offered to patients produce inconsistent results [35-41] and rarely show more than a small to moderate overall benefit [6,35,40,42-44]. Table 1.1 shows an overview of recently published systematic reviews and showing effect sizes for the main surgical and non-surgical interventions underlining these facts. Surgical interventions Various surgical interventions exist for CLBP. In the context of this thesis the focus is on most commonly performed surgeries for CLBP. These are spinal fusion surgery (two or more vertebrae are permanently ‘fused’ together [spondylodesis]) with or without decompression (relief of a compressed spinal nerve root), discectomy (removal of a part of the intervertebral disc), and disc replacement (replacement of the intervertebral disc by an artificial spinal implant). Figure 1.2 shows an example of a commonly performed procedure for spinal fusion surgery. Fusion surgery with or without decompression could be beneficial for a selected group of patients with CLBP, i.e. those with a specific diagnosis with an underlying ‘biological cause’ and who previously failed non-operative treatment (e.g. lumbar spinal stenosis, isthmic spondylolisthesis) [40,50,51]. However, for fusion surgery with or without decompression the effect sizes are not large and the overall quality of evidence is low. Current scientific evidence does not support superiority of surgery for CLBP compared to high-intensity conservative interventions to reduce pain intensity and restore functional ability [52]. Therefore, it is debatable whether any surgical, invasive intervention such as disc replacement or fusion surgery should be performed in patients without a clear diagnosis of the cause of their CLBP [53]. A nationwide survey amongst Dutch spine surgeons showed that no professional consensus could be identified in decision making on the treatment strategy for chronic low back pain, even in the group ‘with presumed biological causes’ [54]. To improve outcomes it has been recommended to identify subgroups of patients for whom spinal fusion surgery is an effective treatment [53-55]. Indeed, in a recently released draft version of the Dutch national guideline for instrumented lumbar spine surgery [56], this is regarded as an important recommendation to improve surgical outcomes, which is the subject of the third part of this thesis (Theme C; Chapter 9-11). Non-surgical interventions Various non-surgical interventions for CLBP in secondary care exist (e.g. invasive pain treatment [injections], functional restoration programmes, back schools, cognitive behavioural therapy, multidisciplinary bio-psychosocial pain management programme). In the context of this thesis the focus is on multidisciplinary bio-psychosocial pain management programmes. These multidisciplinary programmes may have benefits comparable to surgery for back pain caused by degenerative spine disorder, as demonstrated in a recent 11-year follow-up study of surgical trials [41]. A recent Cochrane review of 42 randomised controlled trials (RCTs) including 6,858 patients with CLBP showed that the effects of multidisciplinary bio-psychosocial programmes were of modest magnitude compared to usual care (moderate 11 01 12 01 General introduction Figure 1.2 An example of lumbar spine surgery; decompression and posterior lumbar interbody fusion (PLIF): a female patient, 63 years of age, with degenerative spondylolisthesis L4-L5 who underwent lumbar spine surgery; decompression and fusion (PLIF procedure). (A) (B)(C) (A) Preoperative MRI: degenerative spondylolisthesis L4-L5 with severe spinal stenosis; (B) Pre-operative X-ray lateral view; (C) Post-operative X-ray lateral and anterior-posterior view: spondylolithetic slippage reduction, decompression and fusion with pedicle screws and PLIF cages. quality evidence) and physical treatments (low quality evidence) in reducing pain and disability in people with CLBP. For work outcomes, multidisciplinary programmes seem to be more effective than physical treatment but not more effective than usual care [44]. Very recently, the cost-effectiveness of a multidisciplinary bio-psychosocial pain management programme including minimal interventional procedures (e.g. radiofrequency denervation) for patients with CLBP was compared to the multidisciplinary programme alone (i.e. MinT study [57]). The preliminary 12-month results of the MinT study show that adding minimal interventional procedures is not more cost-effective [58]. At the Sint Maartenskliniek a new combined physical and psychological (CPP) pain management programme was introduced in October 2006 for patients with CLBP (Figure 1.3). The two-week intensive programme is provided outside the clinic in a hotel setting, and three main components can be distinguished: physical training, cognitive behavioural training including graded activity and graded exposure, and education. To monitor the individual progress and to evaluate the programme on a group level over time, participants are systematically followed for one year on patientrelevant indicators (i.e. routine outcome monitoring). More detailed information on the CPP programme is provided through the Internet [59]. A description of the treatment, including who might benefit, has been reported in several published articles [60-62] that belong to the first theme of this thesis (Theme A; Chapters 2-4). These results gave us the motivation to further study and to determine which patients from the larger population of people with CLBP should be referred to these multidisciplinary bio-psychosocial programmes, which is in line with the recommendations in international guidelines [55,63,64] and in the previously mentioned Cochrane review [44]. This is the subject of the third part of this thesis (Theme C; Chapter 9-11). 1.3 Outcomes of interventions for chronic low back pain An important mechanism to improve interventions and with that the delivery of healthcare, is to learn from practices proven to have the best outcomes in order to improve the overall ‘quality of care’ (i.e. benchmarking). The desired outcomes of interventions are the result of a high-quality (infra) structure and process [65], reflecting the end result of care [66], and are thought to matter most to patients and reflect all aspects of care [67,68]. The outcomes General introduction Figure 1.3 Non-surgical intervention; CPP programme as provided by RealHealth NL of (spine) interventions could be regarded as a proxy for quality of care. Routine outcome measurement by means of well-designed outcome registries is challenging, but has well-documented benefits. For example, asking providers to systematically measure and report their outcomes has been shown to improve performance of the care delivered [67]. Furthermore, understanding and comparing outcomes facilitates continuous learning and improvement of the provider’s own strategies through benchmarking and learning from best practices. This type of continuous improvement and informed decision-making could be an important driving force for improving healthcare delivery by refocusing the system on ‘value’ (i.e. patient-related outcomes relative to costs [67,68]), especially in the area of low back pain where the current global burden of the condition, the practice variation, and growth in associated healthcare costs are unsustainable. The ability to define real-world ‘effectiveness’ (i.e. outcomes) of healthcare delivery is of utmost importance and gives the opportunity to assess the value of delivered healthcare, which is gaining in importance particularly within the realm of spine care [69,70]. Measuring outcomes Human functioning and disability are central aspects of human life, and are key concerns in health and medicine. In numerous conditions, including CLBP, functioning or functional ability is not part of the disease process but is both a target and an outcome of health interventions. Therefore, the main health-related outcome domain used in the empirical and methodological studies of this thesis is functional ability. In CLBP, functional disability (i.e. disability) is characterised by pain of variable duration and by various activity limitations of daily life and participation restrictions. The health-related outcome domains that are relevant for patients with CLBP and clinicians include pain, functional ability, health-related quality of life [71-74], complications (including number of deaths) [73,74], repeat surgery (in spine surgery), work status, and analgesic use [74]. To evaluate these outcome domains over time and to achieve patient-centred care, patient-related outcomes such as patient-reported outcome measures (PROMs) are used. PROMs are used alongside the clinician-based measures (e.g. radiologic imaging, physical function tests) and aim to provide an objective measure of a subjective construct: that is, from the individual patient’s perspective and concerns in relationship to their health, healthcare and quality of life [75-77]. Well-designed PROMs have undergone rigorous testing and may be better validated, and as a result PROMs have greater 13 01 14 01 General introduction reproducibility than the so-called objective clinician-based measures [78,79]. PROMs provide a powerful, quantifiable and standardised research tool against which the effectiveness of healthcare interventions can be judged [77,80]. They facilitate comparison of results of different studies and also facilitate subsequent meta-analysis [79,81]. To evaluate individual patient care, PROMs can be used to support shared decision-making, communication and appropriate evaluation of individual treatment success [80]. To evaluate outcomes of interventions, generic or condition-specific PROMs can be used. Generic measures contain multiple concepts of health relevant to both patients and the general population, such as the MOS short-form 36 (SF-36) and the utility measure EuroQol 5 Dimensions (EQ5D) for CLBP. These measures support comparison of health between different patient groups, and between patient groups and the general population. Condition-specific measures are developed to evaluate the well-being of patients with a particular condition or disease. For example, in CLBP, functional ability is measured with condition-specific measures such as the Oswestry Disability Index and the Roland and Morris Disability Questionnaire [82]. However, limitations to the use of PROMs and the challenge to obtain sufficient follow-up responses are acknowledged. Some of these may be related to lack of data or lack of knowledge, and need further research. Such challenges include the myriad of (overlapping) PROMs used in evaluating interventions for CLBP [71], challenges of validation and understanding the association with long-term outcomes, and with that the predictive capacities of PROMs [83]. In this thesis, part B is dedicated to these challenges (Theme B; Chapter 5-7). Patient-related outcome measures (both PROMs and clinician-based) need to be methodologically sound, that is they need to be based on good quality criteria (i.e. clinimetric properties). These criteria include: content validity, internal consistency, criterion validity, construct validity, reproducibility (agreement and reliability), responsiveness, floor and ceiling effects, and interpretability [84]. Score interpretation is a major challenge to the incorporation of PROMs into all settings; a challenge which is further confounded by growing evidence that patients and clinicians differ in their judgment of important change [85,86]. A patient’s interpretation of beneficial change or acceptable symptom state will be informed by his or her own definition of a ‘good outcome’. The tradition of reporting the statistical significance of score change does not necessarily translate into clinically meaningful change by the patient, the healthcare professional or other stakeholder. In fact, treatment success can be conceptualised in two ways: 1) relevant change or improvement, and 2) reaching an acceptable state. With the first concept, the emphasis is on whether or not an individual has relatively improved after an intervention, often expressed as reaching a minimal clinically important change, whereas with the second, the emphasis is on whether or not the achieved outcome is acceptable from the patient’s perspective [87], often expressed as reaching an absolute value. In this thesis, a chapter is dedicated to these concepts (Theme B; Chapter 8). Collecting outcomes - Outcome registries In systematic reviews, results of RCTs are pooled, analysed, and interpreted. In evidence-based medicine, RCTs are considered the gold standard for assessing the efficacy of interventions. However, some barriers for RCTs in spinal disorders are acknowledged, specifically when surgical procedures are involved. Examples are surgeon preferences, patients’ reluctance to randomisation, difficulties in blinding, high cost, the need for long-term follow-up and consequently the often high losses to follow-up, as well as the problem with cross-over [88]. General introduction Well-designed observational cohort studies, reflecting daily clinical practice, can reliably produce results similar to those of RCTs [89-91]. Such studies could be performed by means of an outcome registry. An outcome registry is an organised system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves a predetermined scientific, clinical, or policy purpose(s) [92]. In spine care, various national registries exist, such as the recently started Dutch Spine Surgery Registry in the Netherlands. Examples of large spine registries are the Swedish Spine Register (SweSpine) [93], the Spine Tango Spine Surgery Registry of Eurospine (the Spine Society of Europe) [94,95], and the multicentre adult spinal deformity database in the United States [96]. Recently published studies showed that the results of spine registries seem to be in concordance with those of the published RCTs [91,97-99]. When well designed, registry data can be used to reliably identify optimal treatments, to understand variations in treatment, and to describe care patterns, including identifying appropriateness of care and disparities in the delivery of care. Furthermore, when introducing new health interventions in healthcare, outcome registries are needed to continuously monitor the quality of the healthcare delivered to improve health outcomes [100,101] and, ultimately, to increase the value of the care delivered (i.e. outcome per unit cost [67]) [67,68,70,101,102]. In this thesis the impact and methodology of spine outcome registries are studied (Theme B; Chapter 5). Predicting outcomes It is assumed that with more precise targeting of interventions, patient outcomes improve and so too the efficiency of the healthcare. Classifying the heterogeneous CLBP-population into more homogeneous subgroups, based on their profiles, would support patient triage and guide treatment, as much as any classification system in healthcare aims to do. To distinguish patient profiles, several outcome-based classifications for decision making exist and some aim to identify profiles based on patho-anatomy and biomedical indicators [103]. Some classifications are based on the prognostic course of back pain, e.g. those aimed at the risk of chronicity [104], whilst others aim to identify patients likely to respond favourably to particular interventions [105]. All of them have been developed and studied as a guide for non-surgical interventions applied in primary care [106]. Although these attempts have been made, and some of them seem to be successful [104], the optimal classification system still remains elusive and large numbers of patients consult secondary or tertiary spine care medical specialists to help them solve their CLBP-problem. Different patient profiles might be identified which are likely to benefit from different recommended interventions [39, 44,50,55,63,106]. Ideally, these profiles are based on aetiological and prognostic evidence and on evidence of indicators modifying the effects of interventions. As a challenge, the major recommendation for future research has been to focus on classification systems, which guide the right patient to appropriate surgical or nonsurgical interventions. The rationale is that this has the greatest potential for improving outcomes [106]. A research programme of the spine unit in Sint Maartenskliniek has focussed on the development of such a classification system, and the third theme of this thesis is dedicated to this challenge (Theme C; Chapter 9-11). 15 01 16 01 General introduction 1.4 Aims and Outline The ultimate aim of this thesis is to contribute to the body of knowledge on outcomes of interventions for CLBP and to identify outcome-based subgroups characterised by different patient profiles within the heterogeneous CLBP population. Outline of this thesis The thesis consists of three related themes (A-C) with a total of twelve chapters, including the General introduction and the Summary and General discussion. In Chapter 1, the General introduction, an overview of CLBP as the topic of this thesis is given and this sets the scene for the studies described in themes A-C, including (systematic) literature reviews, empirical, and methodological studies. The thesis follows the chronological framework of the research programme, which is the basis for this thesis. First, a combined physical and psychological (CPP) programme was evaluated and a subgroup of patients benefitting from this programme was identified (Theme A). Subsequently, methodological considerations were studied regarding outcomes and outcomes assessment (Theme B). Finally, through a literature search and a formal consensus procedure, indicators contributing to treatment outcome were identified. These indicators were used to develop a classification system for patient triage to surgical and non-surgical interventions (Theme C). THEME A: Introduction of a combined physical and psychological programme (Chapter 2-4) This theme includes empirical studies in which, amongst others, continuous outcome monitoring is performed after introduction of a new intervention for patients with CLBP —the intensive combined physical and psychological (CPP) programme as provided by RealHealth NL. The overall research question of this part is twofold: 1. Does the novel CPP programme for CLBP improve patient outcomes and reduce healthcare consumption? 2. Is it possible to identify a subgroup of patients that benefits most from the novel CPP programme so that selection criteria can be optimised? The specific study aims: • To evaluate the outcomes of a new intensive CPP pain management programme for CLBP patients and whether this is reflected in the use of healthcare services (Chapter 2). • To evaluate the stability of the two-year follow-up results of a short, intensive cognitive behavioural pain management programme. The emphasis is on evaluating daily functioning, the use of healthcare services, and the use of pain medication two years after the intervention (Chapter 3). • To determine the factors which predict a successful 1-year outcome from this new intervention with the goal of refining the selection criteria (Chapter 4). THEME B: Outcomes assessment (Chapter 5-8) This theme includes a literature study with a qualitative survey, and empirical and methodological studies to clarify the ambiguity of and recommendations to standardise outcomes assessment in secondary or tertiary level spine care. General introduction The overall research question of this part is threefold: 1. What is the current value and methodology of spine outcome registries in clinical practice? 2. Which patient-related outcome measures should be used for outcomes assessment for degenerative lumbar spine disorders? 3. Which criterion can be used to define a successful outcome of interventions for patients with degenerative lumbar spine disorders? The specific study aims: • To evaluate the available evidence for the effects of introducing and using spine registries on patient-related outcomes, and to provide a set of methodological recommendations to improve spine registries (Chapter 5). • To propose a set of patient-related outcomes for use in daily spine care and for research purposes (Chapter 6). • To translate and adapt a patient-reported outcome measure in the functional outcome domain, the Oswestry Disability Index (ODI; version 2.1a), into the Dutch language and to investigate the validity and internal consistency of the translation (Chapter 7). • To estimate the score on the ODI version 2.1a corresponding to a ‘patient acceptable symptom state’ (PASS) in patients undergoing surgery for degenerative disorders of the lumbar spine (Chapter 8). THEME C: Prediction of outcomes (Chapter 9-11) This theme includes a literature study and empirical studies in which the development and validation of a new triage tool is studied for secondary or tertiary spine care specialists. The overall research question of this theme: 1. Is it possible to develop a triage tool for CLBP, which enables valid and reliable identification of patient profiles that supports triage of the patients to a spine surgeon or to non-surgical specialists? The specific study aims: • To develop a decision tool for secondary or tertiary spine care specialists to decide which patients with CLBP should be seen by a spine surgeon or by non-surgical medical specialists (Chapter 9). • To provide preliminary insight into the factors considered by CLBP-experts when deciding whether or not to refer a patient to spinal surgery (Chapter 10). • To identify patient profiles which are associated with and predictive for treatment ‘response’ or ‘non-response’ when referred to either a surgical intervention or a nonsurgical intervention (Chapter 11). In the Summary & General discussion, Chapter 12, the results of the studies performed and described in the previous chapters are discussed and their implications for generalisability of the study results, for research, and for clinical practice are considered. In Key points thesis an overview of this thesis is shown by summarising per the themes ‘What is already known on this topic’ and ‘What this thesis adds’. 17 01 Degenerative spinal stenosis Zaina (2016) [49] (neck, thoracic, low back, or pelvic) Chronic spinal pain CLBP 0.09 Progression 1.93 Patient satisfaction at 2 yr prgm. vs Physical treatment Multidisc biopsychosocial prgm. vs Usual care Disability 0.25 0.46 1.04 Return to work Pain -0.23 Disability -0.21 Return to work Pain 1.87 Disability Multidisc biopsychosocial -0.68 Pain -0.51 -4.43 prgm. vs Phsyical treatment Improved ODI at 2 yr -6.16 4.27 improved ODI at 2 yr Improved ODI at 1 yr 6.22 Improved Back pain at 2 yr -1.57 4.69 Reoperation at 2 yr Improved ODI at 2 yr 4.41 0.44 Good result at 1.5 - 2 yr (surgeon-rated) Poor outcome at 1.5 - 2 yr (patient-reported) Failure at 2 yrs 0.76 0.28 Unchanged / worse at 2 yr (patient-reported) 0.26 2.43 0.09 Effect size (SMD) Not back to work at 2 yr Bad result at 10 yr Repeat surgery after 4 yr Outcome Multidisc biopsychosocial non-operative care Surgery vs Usual Disc replacement vs Fusion Surgery vs Conservative Fusion vs Decompression Decompression & Cognitive excercises Fusion vs Fusion vs Conservative Conservative (PT) Decompression vs Comparison 0.07 - 0.43 0.09 - 0.83 n.s. 0.73 - 1.47 -0.40 - -0.06 -0.37 - -0.04 1.39 - 2.53 -1.19 - -0.16 -1.04 - 0.01 -7.91 - -0.90 n.s. -15.02 - 2.67 1.36 - 2.76 1.85 - 6.68 0.18 - 10.26 n.s. -4.65 - 1.51 0.00 - 2.07 0.51 - 42.83 1.09 - 17.76 0.13 - 1.48 0.25 - 2.25 0.15 - 0.53 0.10 - 0.64 0.09 - 67.57 0.01 - 0.89 95% CI - small effect sizes Favours multidisc. prgm. Outcome: long term (≥ 1 yr) - moderate quality evidence - heterogeniety - small effect sizes Favours in both reviews: multidisc. prgm. Outcome: long term (≥ 1 yr) - low quality evidence Favours surgery (decompression & fusion) No firm conclusions drawn - not clinically relevant Favours disc replacement Favours surgery - effect sizes mostly n.s. or imprecise - small effect sizes Favours decompression & fusion - not clinically relevant Favours surgery - low quality evidence - heterogeniety - small effect sizes Favours surgery Remark CLBP, chronic low back pain; SMD, standardised mean difference; CI, confidence interval; PT, physical therapy; multidisc., multidisciplinary; prgm., programme; yr, year; ODI, Oswestry Disability Index O'Keefe (2016) [48] Kamper (2015) [44] CLBP CLBP with degenerative disc Jacobs (2012) [47] Non-surgical intervention Degenerative spinal stenosis Degenerative spinal stenosis Degenerative disc Lumbar spondylosis Diagnosis Kovacs (2011) [46] Gibson (2005) [45] Surgical intervention Author (year) 18 General introduction Table 1.1 Overview of effect sized for the surgical main surgical and non-surgical interventions 01 General introduction References 1. Hoy D, March L, Brooks P, Blyth F, Woolf A, Bain C et al. The global burden of low back pain: estimates from the Global Burden of Disease 2010 study. Ann.Rheum.Dis. 2014;73:968-974 2. Global Burden of Disease Study 2013 Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015;386:743-800 3. Henschke N, Maher CG, Refshauge KM, Herbert RD, Cumming RG, Bleasel J et al. Prognosis in patients with recent onset low back pain in Australian primary care: inception cohort study. BMJ 2008;337:a171 4. Stanton TR, Latimer J, Maher CG, Hancock M. Definitions of recurrence of an episode of low back pain: a systematic review. Spine (Phila Pa 1976.) 2009;34:E316-E322 5. Lambeek LC, van Tulder MW, Swinkels IC, Koppes LL, Anema JR, van Mechelen W. The trend in total cost of back pain in The Netherlands in the period 2002 to 2007. Spine (Phila Pa 1976.) 2011;36:1050-1058 6. 7. Foster NE. Barriers and progress in the treatment of low back pain. BMC.Med. 2011;9:108 Hoy D, Bain C, Williams G, March L, Brooks P, Blyth F et al. A systematic review of the global prevalence of low back pain. Arthritis Rheum. 2012;64:2028-2037 8. Picavet HS, Schouten JS. Musculoskeletal pain in the Netherlands: prevalences, consequences and risk groups, the DMC(3)-study. Pain 2003;102:167-178 9. Picavet HS. Aspecifieke lage rugklachten: Omvang en gevolgen [Article in Dutch]; 2005 10. Frymoyer JW. Predicting disability from low back pain. Clin.Orthop.Relat Res. 1992:101-109 11. Von Korff M, Saunders K. The course of back pain in primary care. Spine (Phila Pa 1976.) 1996;21:2833-2837 12. Srinivas SV, Deyo RA, Berger ZD. Application of "less is more" to low back pain. Arch.Intern.Med. 2012;172:10161020 13. Itz CJ, Willems PC, Zeilstra DJ, Huygen FJ. Dutch Multidisciplinary Guideline for Invasive Treatment of Pain Syndromes of the Lumbosacral Spine. Pain Pract. 2016;16:90-110 14. Haldeman S, Kopansky-Giles D, Hurwitz EL, Hoy D, Mark EW, Dagenais S et al. Advancements in the management of spine disorders. Best.Pract.Res.Clin.Rheumatol. 2012;26:263-280 15. Waddell G. The back pain revolution. 2nd ed. Churchill Linvingstone: Edinburgh; 2004 16. Bederman SS. Predicting prognosis in sick-listed low back pain patients: sneaking a peak inside the black box. Spine J. 2010;10:728-730 17. Waddell G. Subgroups within "nonspecific" low back pain. J.Rheumatol. 2005;32:395-396 18. Chauffard A. Address in Medicine, on medical prognosis:its methods, its evolution, its limitations:Delivered at the Seventeenth International Congress of Medicine. Br.Med.J. 1913;2:286-290 19. Oxford English Dictionary. Aetiology - definition [Internet]; 2016 [cited 2016 July 20]. Available from: http://www.oxforddictionaries.com/definition/english/aetiology 20. Engel GL. The need for a new medical model: a challenge for biomedicine. Science 1977;196:129-136 21. Hayden JA, Dunn KM, van der Windt DA, Shaw WS. What is the prognosis of back pain? Best.Pract.Res.Clin. Rheumatol. 2010;24:167-179 22. Dunn KM, Croft PR. The importance of symptom duration in determining prognosis. Pain 2006;121:126-132 23. Hestbaek L, Leboeuf-Yde C, Manniche C. Low back pain: what is the long-term course? A review of studies of general patient populations. Eur.Spine J. 2003;12:149-165 24. Costa LC, Maher CG, McAuley JH, Hancock MJ, Herbert RD, Refshauge KM et al. Prognosis for patients with chronic low back pain: inception cohort study. BMJ 2009;339:b3829 25. Picavet HS, Vlaeyen JW, Schouten JS. Pain catastrophizing and kinesiophobia: predictors of chronic low back pain. Am.J.Epidemiol. 2002;156:1028-1034 26. Pincus T, Burton AK, Vogel S, Field AP. A systematic review of psychological factors as predictors of chronicity/ disability in prospective cohorts of low back pain. Spine (Phila Pa 1976.) 2002;27:E109-E120 19 01 20 01 General introduction 27. Von Korff M, Miglioretti DL. A prognostic approach to defining chronic pain. Pain 2005;117:304-313 28. Truchon M. Determinants of chronic disability related to low back pain: towards an integrative biopsychosocial model. Disabil.Rehabil. 2001;23:758-767 29. Weiner BK. Spine update: the biopsychosocial model and spine care. Spine (Phila Pa 1976.) 2008;33:219-223 30. Haldeman S, Dagenais S. A supermarket approach to the evidence-informed management of chronic low back pain. Spine J. 2008;8:1-7 31. Cherkin DC, Deyo RA, Loeser JD, Bush T, Waddell G. An international comparison of back surgery rates. Spine (Phila Pa 1976.) 1994;19:1201-1206 32. Weinstein JN, Lurie JD, Olson PR, Bronner KK, Fisher ES. United States' trends and regional variations in lumbar spine surgery: 1992-2003. Spine (Phila Pa 1976.) 2006;31:2707-2714 33. Martin BI, Tosteson ANA, Lurie JD, Mirza SK, Goodney PR, Dzebisashvili N et al. Variation in the care of surgical conditions: Spinal stenosis. A Dartmouth atlas of healthcare series. 2014 34. Schoenfeld AJ, Harris MB, Liu H, Birkmeyer JD. Variations in Medicare payments for episodes of spine surgery. Spine J. 2014;14:2793-2798 35. Henschke N, Kuijpers T, Rubinstein SM, van Middelkoop M, Ostelo R, Verhagen A et al. Trends over time in the size and quality of randomised controlled trials of interventions for chronic low-back pain. Eur.Spine J. 2012;21:375-381 36. Fairbank J, Frost H, Wilson-MacDonald J, Yu LM, Barker K, Collins R. Randomised controlled trial to compare surgical stabilisation of the lumbar spine with an intensive rehabilitation programme for patients with chronic low back pain: the MRC spine stabilisation trial. BMJ 2005;330:1233 37. Mirza SK, Deyo RA. Systematic review of randomized trials comparing lumbar fusion surgery to nonoperative care for treatment of chronic back pain. Spine (Phila Pa 1976.) 2007;32:816-823 38. Brox JI, Nygaard OP, Holm I, Keller A, Ingebrigtsen T, Reikeras O. Four-year follow-up of surgical versus nonsurgical therapy for chronic low back pain. Ann.Rheum.Dis. 2010;69:1643-1648 39. van Middelkoop M, Rubinstein SM, Kuijpers T, Verhagen AP, Ostelo R, Koes BW et al. A systematic review on the effectiveness of physical and rehabilitation interventions for chronic non-specific low back pain. Eur. Spine J. 2011;20:19-39 40. Jacobs WC, Rubinstein SM, Willems PC, Moojen WA, Pellise F, Oner CF et al. The evidence on surgical interventions for low back disorders, an overview of systematic reviews. Eur.Spine J. 2013;22:1936-1949 41. Mannion AF, Brox JI, Fairbank JC. Comparison of spinal fusion and nonoperative treatment in patients with chronic low back pain: long-term follow-up of three randomized controlled trials. Spine J. 2013;13:1438-1448 42. Hall H, McIntosh G. Low back pain (chronic). Clin.Evid.(Online.);2008 43. Wand BM, O'Connell NE. Chronic non-specific low back pain - sub-groups or a single mechanism? BMC. Musculoskelet.Disord. 2008;9:11 44. Kamper SJ, Apeldoorn AT, Chiarotto A, Smeets RJ, Ostelo RW, Guzman J et al. Multidisciplinary biopsychosocial rehabilitation for chronic low back pain: Cochrane systematic review and meta-analysis. BMJ 2015;350:h444 45. Gibson JN, Waddell G. Surgery for degenerative lumbar spondylosis. Cochrane.Database.Syst.Rev. 2005:CD001352 46. Kovacs FM, Urrutia G, Alarcon JD. Surgery versus conservative treatment for symptomatic lumbar spinal stenosis: a systematic review of randomized controlled trials. Spine (Phila Pa 1976.) 2011;36:E1335-E1351 47. Jacobs W, Van der Gaag NA, Tuschel A, de Kleuver M, Peul W, Verbout AJ et al. Total disc replacement for chronic back pain in the presence of disc degeneration. Cochrane.Database.Syst.Rev. 2012:CD008326 48. O'Keeffe M, Purtill H, Kennedy N, Conneely M, Hurley J, O'Sullivan P et al. Comparative Effectiveness of Conservative Interventions for Nonspecific Chronic Spinal Pain: Physical, Behavioral/Psychologically Informed, or Combined? A Systematic Review and Meta-Analysis. J.Pain 2016 49. Zaina F, Tomkins-Lane C, Carragee E, Negrini S. Surgical versus non-surgical treatment for lumbar spinal stenosis. Cochrane.Database.Syst.Rev. 2016:CD010264 General introduction 50. Fourney DR, Andersson G, Arnold PM, Dettori J, Cahana A, Fehlings MG et al. Chronic low back pain: a heterogeneous condition with challenges for an evidence-based approach. Spine (Phila Pa 1976.) 2011;36:S1-S9 51. Glassman SD, Carreon LY, Anderson PA, Resnick DK. A diagnostic classification for lumbar spine registry development. Spine J. 2011;11:1108-1116 52. Mannion AF, Brox JI, Fairbank JC. Consensus at last! Long-term results of all randomized controlled trials show that fusion is no better than non-operative care in improving pain and disability in chronic low back pain. Spine J. 2016;16:588-590 53. Jacobs WC, Rubinstein SM, Koes B, van Tulder MW, Peul WC. Evidence for surgery in degenerative lumbar spine disorders. Best.Pract.Res.Clin.Rheumatol. 2013;27:673-684 54. Willems P, de BR, Oner C, Castelein R, de KM. Clinical decision making in spinal fusion for chronic low back pain. Results of a nationwide survey among spine surgeons. BMJ Open. 2011;1:e000391 55. National Institute for Health and Clinical Excellence. Low back pain: early management of persistent non-specific low back pain [Report]. NICE Clinical Guideline 88. London, National Collaborating Centre for Primary Care; 2009 56. CBO, Nederlandse Orthopaedische Vereniging. Concept Richtlijn Geinstrumenteerde spinaalchirurgie bij degeneratieve aandoeningen van de thoracolumbale wervelkolom [Report in Dutch]; 2015 57. Maas ET, Juch JN, Groeneweg JG, Ostelo RW, Koes BW, Verhagen AP et al. Cost-effectiveness of minimal interventional procedures for chronic mechanical low back pain: design of four randomised controlled trials with an economic evaluation. BMC.Musculoskelet.Disord. 2012;13:260 58. Ligtenberg G, de Groot IB, Staal PC. Anesthesiologische pijnbestrijdingstechnieken (radiofrequente denervatie) bij chronische aspecifieke lage rugklachten 2015 59. RealHealth. The RealHealth Institute [Internet; Website in Dutch]; 2015 [cited 2016 July 20]; Available from: https://realhealth.nl/ 60. van Hooff ML, van der Merwe JD, O'Dowd J, Pavlov PW, Spruit M, de Kleuver M et al. Daily functioning and self-management in patients with chronic low back pain after an intensive cognitive behavioral programme for pain management. Eur.Spine J. 2010;19:1517-1526 61. van Hooff ML, Ter Avest W, Horsting PP, O'Dowd J, de Kleuver M, van Lankveld W et al. A short, intensive cognitive behavioral pain management program reduces health-care use in patients with chronic low back pain: two-year follow-up results of a prospective cohort. Eur.Spine J. 2012;21:1257-1264 62. van Hooff ML, Spruit M, O'Dowd JK, van Lankveld W, Fairbank JC, van Limbeek J. Predictive factors for successful clinical outcome 1 year after an intensive combined physical and psychological programme for chronic low back pain. Eur.Spine J. 2014;23:102-112 63. Airaksinen O, Brox JI, Cedraschi C, Hildebrandt J, Klaber-Moffett J, Kovacs F et al. Chapter 4. European guidelines for the management of chronic nonspecific low back pain. Eur.Spine J. 2006;15 Suppl 2:S192-S300 64. Chou R, Loeser JD, Owens DK, Rosenquist RW, Atlas SJ, Baisden J et al. Interventional therapies, surgery, and interdisciplinary rehabilitation for low back pain: an evidence-based clinical practice guideline from the American Pain Society. Spine (Phila Pa 1976.) 2009;34:1066-1077 65. Donabedian A. Evaluating the quality of medical care. Milbank Mem.Fund.Q. 1966;44:Suppl-206 66. Campbell M, Fitzpatrick R, Haines A, Kinmonth AL, Sandercock P, Spiegelhalter D et al. Framework for design and evaluation of complex interventions to improve health. BMJ 2000;321:694-696 67. Porter ME. What is value in health care? N.Engl.J.Med. 2010;363:2477-2481 68. Porter ME, Larsson S, Lee TH. Standardizing Patient Outcomes Measurement. N.Engl.J.Med. 2016;374:504506 69. McGirt MJ, Parker SL, Asher AL, Norvell D, Sherry N, Devin CJ. Role of prospective registries in defining the value and effectiveness of spine care. Spine (Phila Pa 1976.) 2014;39:S117-S128 21 01 22 01 General introduction 70. McGirt MJ, Resnick D, Edwards N, Angevine P, Mroz T, Fehlings M. Background to understanding value-based surgical spine care. Spine (Phila Pa 1976.) 2014;39:S51-S52 71. Chapman JR, Norvell DC, Hermsmeyer JT, Bransford RJ, DeVine J, McGirt MJ et al. Evaluating common outcomes for measuring treatment success for chronic low back pain. Spine (Phila Pa 1976.) 2011;36:S54-S68 72. McCormick JD, Werner BC, Shimer AL. Patient-reported outcome measures in spine surgery. J.Am.Acad. Orthop.Surg. 2013;21:99-107 73. Chiarotto A, Deyo RA, Terwee CB, Boers M, Buchbinder R, Corbin TP et al. Core outcome domains for clinical trials in non-specific low back pain. Eur.Spine J. 2015;24:1127-1142 74. Clement RC, Welander A, Stowell C, Cha TD, Chen JL, Davies M et al. A proposed set of metrics for standardized outcome reporting in the management of low back pain. Acta Orthop. 2015:1-11 75. Fitzpatrick R, Davey C, Buxton MJ, Jones DR. Evaluating patient-based outcome measures for use in clinical trials. Health Technol.Assess. 1998;2:i-74 76. Ganz PA. What outcomes matter to patients: a physician-researcher point of view. Med.Care 2002;40:III11III19 77. Haywood KL. Patient-reported outcome I: measuring what matters in musculoskeletal care. Musculoskeletal.Care 2006;4:187-203 78. Pynsent PB. Choosing an outcome measure. J.Bone Joint Surg.Br. 2001;83:792-794 79. Poolman RW, Swiontkowski MF, Fairbank JC, Schemitsch EH, Sprague S, de Vet HC. Outcome instruments: rationale for their use. J.Bone Joint Surg.Am. 2009;91 Suppl 3:41-49 80. Skevington SM, Day R, Chisholm A, Trueman P. How much do doctors use quality of life information in primary care? Testing the trans-theoretical model of behaviour change Qual.Life Res. 2005;14:911-922 81. Bombardier C. Outcome assessments in the evaluation of treatment of spinal disorders: summary and general recommendations. Spine (Phila Pa 1976.) 2000;25:3100-3103 82. Roland M, Fairbank J. The Roland-Morris Disability Questionnaire and the Oswestry Disability Questionnaire. Spine (Phila Pa 1976.) 2000;25:3115-3124 83. Gossec L, Dougados M, Dixon W. Patient-reported outcomes as end points in clinical trials in rheumatoid arthritis. RMD.Open. 2015;1:e000019 84. Terwee CB, Bot SD, de Boer MR, van der Windt DA, Knol DL, Dekker J et al. Quality criteria were proposed for measurement properties of health status questionnaires. J.Clin.Epidemiol. 2007;60:34-42 85. Hewlett SA. Patients and clinicians have different perspectives on outcomes in arthritis. J.Rheumatol. 2003;30:877-879 86. Greenhalgh J, Long AF, Flynn R. The use of patient reported outcome measures in routine clinical practice: lack of impact or lack of theory? Soc.Sci.Med. 2005;60:833-843 87. Kvien TK, Heiberg T, Hagen KB. Minimal clinically important improvement/difference (MCII/MCID) and patient acceptable symptom state (PASS): what do these concepts mean? Ann.Rheum.Dis. 2007;66 Suppl 3:iii40-iii41 88. Jacobs WC, Kruyt MC, Verbout AJ, Oner FC. Spine surgery research: on and beyond current strategies. Spine J. 2012;12:706-713 89. Benson K, Hartz AJ. A comparison of observational studies and randomized, controlled trials. N.Engl.J.Med. 2000;342:1878-1886 90. Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N.Engl.J.Med. 2000;342:1887-1892 91. Weinstein JN, Lurie JD, Tosteson TD, Zhao W, Blood EA, Tosteson AN et al. Surgical compared with nonoperative treatment for lumbar degenerative spondylolisthesis. four-year results in the Spine Patient Outcomes Research Trial (SPORT) randomized and observational cohorts. J.Bone Joint Surg.Am. 2009;91:1295-1304 General introduction 92. Gliklich RE, Dreyer NA. Registries for Evaluating Patient Outcomes: A User's Guide. 2nd ed. Rockville: Agency for Healthcare Research and Quality; 2010 93. Stromqvist B, Jonsson B, Fritzell P, Hagg O, Larsson BE, Lind B. The Swedish National Register for lumbar spine surgery: Swedish Society for Spinal Surgery. Acta Orthop.Scand. 2001;72:99-106 94. Roder C, Chavanne A, Mannion AF, Grob D, Aebi M. SSE Spine Tango--content, workflow, set-up. www. eurospine.org-Spine Tango. Eur.Spine J. 2005;14:920-924 95. Melloh M, Staub L, Aghayev E, Zweig T, Barz T, Theis JC et al. The international spine registry SPINE TANGO: status quo and first results. Eur.Spine J. 2008;17:1201-1209 96. Scheer JK, Tang JA, Smith JS, Klineberg E, Hart RA, Mundis GM, Jr. et al. Reoperation rates and impact on outcome in a large, prospective, multicenter, adult spinal deformity database: clinical article. J.Neurosurg. Spine 2013;19:464-470 97. Tosteson AN, Tosteson TD, Lurie JD, Abdu W, Herkowitz H, Andersson G et al. Comparative effectiveness evidence from the spine patient outcomes research trial: surgical versus nonoperative care for spinal stenosis, degenerative spondylolisthesis, and intervertebral disc herniation. Spine (Phila Pa 1976.) 2011;36:2061-2068 98. Forsth P, Olafsson G, Carlsson T, Frost A, Borgstrom F, Fritzell P et al. A Randomized, Controlled Trial of Fusion Surgery for Lumbar Spinal Stenosis. N.Engl.J.Med. 2016;374:1413-1423 99. Staub LP, Ryser C, Roder C, Mannion AF, Jarvik JG, Aebi M et al. Total disc arthroplasty versus anterior cervical interbody fusion: use of the Spine Tango registry to supplement the evidence from randomized control trials. Spine J. 2016;16:136-145 100. Krumholz HM. Real-world imperative of outcomes research. JAMA 2011;306:754-755 101. Larsson S, Lawyer P, Garellick G, Lindahl B, Lundstrom M. Use of 13 disease registries in 5 countries demonstrates the potential to use outcome data to improve health care's value. Health Aff.(Millwood.) 2012;31:220-227 102. Rihn JA, Berven S, Allen T, Phillips FM, Currier BL, Glassman SD et al. Defining value in spine care. Am.J.Med. Qual. 2009;24:4S-14S 103. Nijs J, Apeldoorn A, Hallegraeff H, Clark J, Smeets R, Malfliet A et al. Low back pain: guidelines for the clinical classification of predominant neuropathic, nociceptive, or central sensitization pain. Pain Physician 2015;18:E333-E346 104. Hill JC, Whitehurst DG, Lewis M, Bryan S, Dunn KM, Foster NE et al. Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial. Lancet 2011;378:1560-1571 105. Hicks GE, Fritz JM, Delitto A, McGill SM. Preliminary development of a clinical prediction rule for determining which patients with low back pain will respond to a stabilization exercise program. Arch.Phys.Med.Rehabil. 2005;86:1753-1762 106. Fairbank J, Gwilym SE, France JC, Daffner SD, Dettori J, Hermsmeyer J et al. The role of classification of chronic low back pain. Spine (Phila Pa 1976.) 2011;36:S19-S42 23 01 24 01 25 THEME A: Introduction of a combined physical and psychological programme 26 01 27 Chapter 02 Daily functioning and self-management in patients with chronic low back pain after an intensive cognitive behavioural programme for pain management van Hooff ML van der Merwe JD O’Dowd J Pavlov PW Spruit M de Kleuver M van Limbeek J Published in: Eur Spine J. 2010;19(9):1517-26 28 Abstract Purpose: Chronic low back pain (CLBP) is associated with persistent or recurrent disability, which results in high costs for society. Cognitive behavioral treatments produce clinically relevant benefits for patients with CLBP. Nevertheless, no clear evidence for the most appropriate intervention is yet available. The purpose of this study is to evaluate the midterm effects in a cohort of patients with CLBP participating in an intensive pain management programme. Methods: The programme provided by RealHealth-Netherlands is based on cognitive behavioral principles and executed in collaboration with orthopedic surgeons. Main outcome parameters were daily functioning (Roland and Morris Disability Questionnaire and Oswestry Disability Questionnaire), self-efficacy (Pain Self-Efficacy Questionnaire) and quality of life (Short Form 36 Physical Component Score). All parameters were measured at baseline, last day of residential programme and at 1 and 12 months follow up. Repeated measures analysis was applied to examine changes over time. Clinical relevance was examined using minimal clinical important differences (MCID) estimates for main outcomes. To compare results with literature effect sizes (Cohens’ d) and Standardized Morbidity Ratios (SMR) were determined. Results: 107 patients with CLBP participated in this programme. Mean scores on outcome measures showed a similar pattern: improvement after residential programme and maintenance of results over time. Effect sizes were 0.9 for functioning, 0.8 for self-efficacy and 1.3 for physical functioning related quality of life. Clinical relevancy: 79% reached MCID on functioning, 53% on self-efficacy, and 80% on quality of life. Study results on functioning were found to be 36% better and 2% worse when related to previous research on, respectively, rehabilitation programmes and spinal surgery for similar conditions (SMRs 136% and 98%, respectively). Conclusions: The participants of this evidence-based programme learned to manage CLBP, improved in daily functioning and quality of life. The study results are meaningful and comparable with results for spinal surgery and even better than results from less intensive rehabilitation programmes. Daily functioning after a pain management programme 29 Introduction Chronic low back pain (CLBP) is a major health and economic problem in western industrialized countries and it is associated with persistent or recurrent disability, resulting in high costs for society [1-3]. In the Netherlands in 2003, the 1 year prevalence in the general population was estimated to be 44%, and caused 24.4% of all sick leave in the employed population [4]. Annually, 22,000 new orthopedic patients visit the orthopedic surgery department at the Sint Maartenskliniek Nijmegen, in The Netherlands. Around 27% (6,000) are patients with low back pain complaints. Ten percent have an indication for surgery and another 10% for invasive pain treatment (epidural block, nerve root block, or spinal cord stimulation). Therefore, approximately 80% (4,500) of the CLBP sufferers do not need invasive treatment. In literature there is no clear evidence that primary spinal fusion in patients with CLBP was any more beneficial than intensive rehabilitation [5,6]. Fairbank et al. [5] came this conclusion after they performed the MRC spine stabilization trial in which 349 patients were randomized to either a surgical stabilization (spinal fusion) or an intensive outpatient rehabilitation group (approximately 75h of exercises, supported by cognitive behavioral elements for 3 weeks). The conclusions have been sustained by Mirza and Deyo [7] , who conducted a systematic literature review of four randomized trials comparing lumbar fusion surgery to non-operative care for treatment in CLBP. Several non-surgical interventions are available for patients with CLBP [1,2,8,9]. The guidelines of the National Institute for Health and Clinical Excellence (NICE) [10] recommend cognitive behavioral therapy, exercise therapy, brief educational interventions, and multidisciplinary (bio-psycho-social) treatment. Recently the American Pain Society [11] published an evidence-based clinical practice guideline strongly recommending intensive interdisciplinary rehabilitation with a cognitive behavioral emphasis for patients with non-radicular, low back pain. Although such treatments can be effective, it is not yet known which type of patients benefits most from them [2,9,12]. In The Netherlands, Smeets et al. [9,13] recently performed a multicenter, randomized clinical trial among 172 patients with non-specific CLBP to determine the effectiveness of three often used approaches in non-surgical treatment. These outpatient treatments were based upon different theoretical models in CLBP: the active physical model, the cognitive behavioral model, and a combination of both; they were delivered three times a week during 10 weeks. He then compared these therapeutic modalities to a group of patients on the waiting list. Sixmonth and 1-year post-treatment results showed that all three active treatments were more effective than no treatment, but no treatment group was more clinically relevant than the other two [9]. The authors concluded that these results could, at least partially, be ascribed to the fact that exposure to the cognitive behavioral treatment components was only 78h, and not the 100h originally recommended in a Cochrane review by Guzman et al. [8]. In the Cochrane review, the authors concluded that intensive multidisciplinary, bio-psycho-social rehabilitation, improves CLBP and physical functioning whereas interventions of less than 100h do not yield an additional effect compared to non-multidisciplinary treatment or usual care. 02 30 02 Daily functioning after a pain management programme The intensive pain management programme provided by RealHealth NL is based upon cognitive behavioral principles, using a multidisciplinary bio-psycho-social approach, and is of a 100h duration. The programme follows the NICE guidelines [10] as well as the American Pain Society recommendations [11]. It is indicated for patients with CLBP for whom surgical or anaesthesiological pain intervention is not an option. The aim of this article is to evaluate the 1-year follow-up results of a programme based on the cognitive behavioral approach in patients with CLBP. We conducted a prospective cohort study and compared the results to outcomes of published research on interventions for CLBP. Materials and Methods Study design and Setting This is a prospective cohort study with a repeated measures structure to ensure a sufficient degree of internal control. Assessments were made pre-treatment, immediately posttreatment, and to assess whether the effect was sustained, 1 and 12 months after the treatment period. The results of this programme are compared to the outcomes of published research of Fairbank et al. [5] and Smeets et al. [9], on interventions for similar conditions. The pain management programme is part of the Sint Maartenskliniek Nijmegen, but is provided in a hotel facility outside the clinic. Patients Patients entered into the programme consecutively, after a multisciplinary team of RealHealth (consisting of a psychologist, a physiotherapist and an occupational therapist) screened the patients. These patients had no indication for invasive or anaesthesiological intervention, as confirmed by orthopedic surgeons of the outpatient department of Sint Maartenskliniek, and they were motivated to learn to change their pain behavior. The patients met the following criteria: low back pain for at least 6 months, no indication for surgical or pain treatment, had no intention of asking for medical treatment for the year following the 2-week programme, age between 20 and 65 years, absence from work for less than 2 years, were motivated to change behavior, were willing to follow the programme and to reside for 2 weeks in the hotel, were able to speak and read Dutch. Exclusion criteria were severe psychiatric disorders and poor physical condition. Treatment The cognitive behavioral programme has been developed by the RealHealth Institute in the United Kingdom and it is a specific pain management programme for patients suffering from CLBP. All sessions in the programme are delivered by the trainers of the multidisciplinary team of RealHealth; the same professionals who did the screening. The full programme comprises an assessment day for intake, the 10-day residential programme with 2 days of follow up, at 1 and 12 months post-treatment. It is based upon cognitive behavioral principles and involves a 100h of participant contact time delivered in a 2-week group-orientated residential setting; around 50h of cognitive behavioral training, 35h of physical activities, and a 15h of education. The main goal of the programme is to increase the patients’ ability for selfmanagement and self-efficacy, and with that to address the psychological impact of pain. The perspective is to give people with persistent pain the opportunity to develop techniques and Daily functioning after a pain management programme strategies which allows them to minimize the impact of their pain on their daily activities. This is achieved through a combination of a psychological emphasis and physical activities. In general this comprises: a stretch and exercise programme, education to define the cause and nature of chronic pain with particular emphasis on the distinction between pain and damage. Patients work on goals to enable them to return to an active lifestyle, using planning and pacing techniques. Cognitive behavioral therapy is given to promote understanding in the link between beliefs, fears, thoughts, and subsequently mood and pain, and to learn techniques to identify unhelpful patterns of thinking and to develop effective responses to challenges. Patients learn to develop a range of relaxation techniques to reduce pain, to aid sleep, and to increase their range and style of strategies to manage increases in pain (www.realhealth.org.uk/what-we-do/pmp). Outcome assessment: Procedure Participants provide information about medical history, pain (history, intensity, and duration), medication, general health status, sick leave from work, and disability compensation. Standardized and validated self-assessment questionnaires measuring low back pain-related disability, mood, self-management, pain catastrophizing, pain-related fear, and quality of life were also filled out; as described in the next paragraph. These questionnaires were completed on four occasions: at baseline, (intake within 1 week before start of programme), at the end of 2-week programme, and at 1 and 12 months post-treatment. The participants received no assistance in completing the assessment measures. For intake they were asked to bring the completed questionnaires with them; on the other occasions the participants completed the questionnaires at the treatment location. Outcome Measures I. Roland and Morris Disability Questionnaire (RMDQ) The RMDQ [14] is a valid, reliable, and responsive outcome measure for functional disability in patients with LBP [9,15,16]. This measure is generally used in studies evaluating conservative treatments on patients suffering from CLBP. The total (sum) score ranges between 0 (no disability) and 24 (maximal disability). The minimal clinical important difference (MCID) for clinical relevance is considered to be 5 points [17], although Smeets et al. [9,13] based their study results on a MCID of 2 points. II. Oswestry Disability Index (ODI) The ODI [18] is a questionnaire to detect the extent of disability in patients with LBP; it is often used in studies in which orthopedic interventions are evaluated. The widely used measure has been shown to be valid, reliable, and responsive in patients with CLBP [15]. It measures the impact of LBP on daily functioning in ten domains. The total (sum) score ranges between 0 and 100, the higher the score the higher the disability. In literature the MCID varied from 4 to 22.1 points when fusion surgery was evaluated [17,19-22]. The MCID for clinical relevance in this study is considered to be 10 points [17,20]. III.Pain Self-Efficacy Questionnaire (PSEQ) The PSEQ is a 10-item inventory which measures the patient’s belief about his/her ability to accomplish a range of activities despite his/her pain [23]. Belief in self-efficacy influences the possibility of effectively using pain-coping strategies. It also measures physical and psychological function and rehabilitation outcome in patients with chronic pain. The measure is used to evaluate the participants’ ability to self-manage his/her pain complaints. Scores range from 0 to 60, with the higher scores indicating stronger self-efficacy beliefs. 31 02 32 02 Daily functioning after a pain management programme Validity and reliability has been shown, but no MCID for PSEQ was found in literature. The generally accepted MCID of 25% improvement above mean baseline score has been used for this study. IV. MOS Short Form–36 Health Survey Questionnaire (SF36) The SF36 [24] is a generic instrument to measure the health-related quality of life. The SF36 consists of two component scores: a physical and a mental score, each of which is made up of subscales. The 8 subscales of the SF36 represent generic health concepts, considered to be universal and to represent basic human functions and well-being. In this study we only used the SF36 Physical Component Score (SF36 PCS); it is has been used in several studies among patients with chronic pain and has been found valid, reliable, and responsive [24]. The total score ranges from 0 to 100. On the SF36 PCS, a higher score indicates better health. The MCID of the SF36 PCS is reported to be 5.4 [22]. Statistical Analysis Descriptive statistics were used to present frequencies. A General Linear Model (GLM) repeated measures multivariate analysis of variance was used to identify changes over time using variables measured on at least an interval scale. To show the strength of the within-subject factor Time in the multivariate models of primary outcome measures, R2 has been computed. This descriptive statistic indicates the proportion of the variability in the observed data that can be attributed to the treatment. In other words, it is a way to measure treatment effect. Because of the use of questionnaires we anticipated missing data on items. We assumed that these missing data are randomly divided over the different outcome measures. Imputation techniques were used to compensate for these item-missing data. With this, sensitivity analyses are performed to determine the robustness of the outcomes and to estimate the degree and direction of potential confounding. The level of statistical significance was set at 0.05. All statistical analyses were performed using SPSS, version 12.0.1 for Windows. Sensitivity analysis. Two methods were used for imputation: carrying the last observation forward (CLOF) and imputation of the baseline values for each missing data point (worstcase scenario). We considered the CLOF data the most realistic scenario since one assumes no change over time in the specific item that was substituted and the imputation of baseline values to be the worst-case scenarios, since every effect of the programme was considered to have vanished for that item. We completed the analysis by comparing the patterns of each method with the analysis for those cases with complete data (best-case scenario). When patterns are similar in direction and in the magnitude of estimation of effect size as well, CLOF is considered to be the approach of choice. Clinical relevance. Clinical outcome was defined as mean net change with time in outcome scores as well as the percentage of participants reaching a preset minimal clinical important difference (MCID) for each outcome. The MCID is the threshold for meaningful change; it is defined as the smallest difference that can be considered to be beneficial for the patient and that would result in a change in management by the patient, assuming an absence of excessive side effects or costs [25]. The MCID is often reported as minimal clinical important change (MCIC) [17,20]. Effect sizes were calculated for all outcome measures for the RealHealth programme as well as for the same measures used in published research to indicate the magnitude of treatment effect. It is measured as the standardized difference between the mean post– and pre-treatment score and it takes group variability into account. We used Daily functioning after a pain management programme Cohen’s d for effect size; it is defined as the difference between means (baseline and follow up) divided by the pooled standard deviation [26]. An effect size of 1 is equivalent to a change of 1 standard deviation (SD) in the sample. The effect sizes are translated into benchmarks for assessing the relative size of change. An effect size (d) of 0.2 is considered small, 0.5 moderate, while 0.8 is a large effect. To compare results of this cohort study with published research we used Standardized Morbidity Ratios (SMR) to estimate the relative rates of treatment improvement. In this study the SMR is the observed/expected ratio. The expected values have been computed using data from an external reference population [27]. Our reference population came from literature for spinal surgery and rehabilitation programmes for patients treated for CLBP. The percentages in this formula indicate the number of participants improved. The standard deviation of the difference in the reference data of Fairbank et al. [5] was not reported; therefore, we calculated the variance of the difference using the following formula: σ2X-Y = σ2X + σ2Y - 2ρσX σY, where ρ is the correlation between the variances of the differences of the variables X and Y in the reference; for this calculation it is assumed to be 0.50. With the normalized data, a z-value is calculated and the percentage patients improved by the intervention can be determined. Results Response One hundred fifty-five patients were referred for the RealHealth programme between October 2006 and July 2007; 107 (69%) were admitted according to the inclusion and exclusion criteria. The 48 excluded patients were referred back to the orthopedic surgeon. Nineteen (12%) of the 107 failed to meet the inclusion criteria, 17 (11%) were included but decided not to join the programme, and 12 (8%) were included but wished to postpone their participation until a later time. In August 2008, all participants had completed the 1-year follow up. Four of the 107 participants left during the 2-week residential programme: two due to lack of motivation, one because of inappropriate behavior during group sessions, and one due to acute illness of the partner. The scores for 77% (n= 82) of all possible items for each participant at each assessment were available for analysis: the best-case scenario. Some data were missing for 21 participants; these missing values were randomly divided among the four assessment moments and the items to be scored. In total 446 of the 5,592 data points were missing (7.4%) in the database. For RMDQ 45 data points were missing (0.7%); for ODI 43 (0.7%); for PSEQ 44 (0.7%), and for SF36 PCS 184 data points (3.1%). Imputation techniques were applied to assign a value for the missing data. Post hoc power analyses, to determine the minimal sample size needed to find a significant effect on the main outcome parameters, showed that a minimum of 26 complete data sets was needed to show clinical relevant (α= 0.05, β= 0.90) change on each outcome measure. Since we included the data of all 107 participants for analysis, and compared these results with the best-case scenario (complete cases only), sufficient power was achieved. 33 02 34 02 Daily functioning after a pain management programme Baseline characteristics The baseline characteristics for the 107 patients who participated in the RealHealth programme are shown in Table 2.1. The mean age was 44 (± 8.4) years; the mean duration of LBP was 12 years, with one 63-year-old patient reporting 52 years of complaints. Females were in the majority (57%), while 70% of the participants were currently at work, and one-third had previously undergone surgery for their LBP. Table 2.1 Baseline characteristics reported by participants in RealHealth NL programme with chronic low back pain (CLBP; n= 107) Baseline characteristics Total (n=107) Sociodemographic Age, mean (SD, range min-max) in years 44.1 (± 8.4, 23-60) Gender n (%), Male : Female 46 (43%) : 61 (57%) Employement status, n (%) Yes : no 75 (70.1%) : 32 (29.9%) Working – Full time 43 (40.2%) Working – Part time 32 (29.9%) Unemployed because of CLBP 15 (14%) Unemployed because of other causes 2 (1.9%) Disability pension 15 (14%) CLBP History Duration of LBP, mean (SD, range min-max) in years 12.3 (±10.9, 1-52) Pain medication n (%), yes : no 91 (85%) : 16 (15%) Previous surgery n (%), yes : no 34 (32%) : 73 (68%) Baseline values factors related to CLBP PCS Pain Catastrophizing Scale, mean (SD) 22.6 (± 9.2) ZSDS Zung Self-Rating Depression Scale, mean (SD) 24.8 (± 10.4) TSK Tampa Scale for Kinesiophobia, mean (SD) 41.0 (± 6.5) Clinical outcome Table 2.2 shows means and standard deviations (SD) for all outcome measures. Mean outcome scores improved greatly between baseline and directly post-treatment as seen in Table 2.2. During the following 12 months a continuous, slight improvement in mean scores is seen. Mean improvements between baseline and 12 months follow up are: 5 points on the RMDQ (± 6.3), 12 points on the ODI (± 15.2), 10 points on the PSEQ (± 12.8), and 21 points on the SF36 PCS (± 17.6). Daily functioning after a pain management programme The R2 for the factor time, a measure for treatment effect, for each outcome measure is also presented in Table 2.2 It is significant for each of the four outcome variables. For both physical functioning scores (ODI and RMDQ), the effects are of similar magnitude as it is for pain selfefficacy (PSEQ). The strongest effect was found for physical functioning-related quality of life (SF36 PCS); it was almost one and a half times greater: R2 = 0.59 than the 0.40 found for the other three outcomes. To verify that using the CLOF (carrying the last observation forward) method to compensate for missing data could be applied, the mean scores for each outcome variable were graphically compared as presented in Figures 2.1-4. Each figure presents three scenarios: the best-case scenario (n= 82) in which only the patients with no missing values have been included, the worst-case scenario (n= 107) in which the baseline value has been inserted for any missing value, and the CLOF method (n= 107). In each figure all three curves show the same tendency, which supports our assumption that the results are robust, independent of which imputation method was used. They all improve with time, and the curve for the best-case scenario stayed within the confidence intervals of the CLOF curve. Hence, the results obtained using CLOF can be considered as reasonably realistic and can be used to evaluate the RealHealth programme. Table 2.2 Mean (SD) for functional disability, self-efficacy and quality of life in CLBP Baseline Last day of 2-week programme 1 month follow up 12 months follow up R2 ODI 41.2 (14.5) 35.4 (16.6) 31.7 (16.0) 29.0 (17.9) 0.40 RMDQ 13.9 ( 4.0) 10.1 ( 5.2) 9.2 ( 5.7) 8.9 (6.5) 0.39 PSEQ 33.9 (11.5) 42.8 (11.0) 42.3 (11.2) 43.8 (11.3) 0.38 SF36 PCS 40.2 (12.6) 49.6 (15.4) 60.9 (20.5) 61.3 (18.8) 0.59 Outcome measures R variability attributed to treatment (within-subject factor time), a measure for treatment effect, was analyzed with Repeated Measures multivariate analyses of variance. CLOF scenario (n= 107) was used. CLOF carrying last observation forward, as described in the text Repeated Measures MANOVA are the following: ODI Oswestry Disability Index: df (1, 106), F= 69.20, p<0.001; RMDQ Roland and Morris Disability Questionnaire: df (1, 106), F= 67.71, p<0.001; PSEQ Pain Self-Efficacy Questionnaire: df (1, 106), F= 65.14, p<0.001; SF36 PCS MOS Short Form 36 Physical Component Scale: df (1, 106), F=152.08, p<0.001 2 The effect sizes for the functional scales, those to assess self-management and quality of life scales, are given in Table 3: these range between 0.8 and 1.3. For our study, all the effect sizes can be classified as large. The clinical relevancy is further supported by the percentage of participants who reached the preset minimal clinical important difference (MCID) for each outcome measure. At least 50% of the participants showed improvement greater than the reference MCID for all 4 measurements. For a reduction of 5 points on RMDQ [17], 49% showed a relevant improvement in daily functioning 12 months after the treatment. A clinical relevant improvement of 10 points or more on ODI [17,20] was seen in 56% of the participants. The MCID for self-management of pain (PSEQ) was set a 25% (8.5 points) of the mean baseline score; 57 participants (53%) showed a relevant improvement. For the quality of life related to physical functioning (SF36 PCS) 86 participants (80%) showed a meaningful improvement of 5.4 points [22] or more. 35 02 36 02 Daily functioning after a pain management programme In order to be able to compare the results of the RealHealth programme with those found in the literature, both the effect sizes and Standardized Morbidity Ratios (SMR), based on the data provided in the article, needed to be calculated (Table 3). No one study used all of our outcome variables. For the cognitive behavioral treatment group reported by Smeets et al. [9] the effect size is small (d= 0.2). To compare with the results of the UK MRC spine stabilization trial [5], we calculated the Cohen’s d for the fusion group and the rehabilitation group separately. As can be seen in Table 2.3, the effect sizes for both interventions for the ODI are moderate (surgery d= 0.7 and rehabilitation d= 0.5) as is that for the SF36 PCS (rehabilitation d= 0.6). Only the Cohen’s d for the SF36 PCS for the group, which had had a surgical intervention, is considered large (d= 0.8). For the SMR: first the literature studies the percentage of patients who improved was determined for the literature studies and for the RealHealth study. Subsequently, the SMR was calculated, as shown in Table 2.3. To compare with the cognitive behavioral treatment of Smeets et al. [9] a 2-point MCID was used, as reported in that study. The calculated SMR of 136% indicates that 36% more of the participants of the RealHealth programme showed improvement on the RMDQ. In comparison to the rehabilitation treatment of Fairbank et al. [5], the SMR of 107% indicates that a slightly larger percentage of the RealHealth participants showed improvement on the ODI. The SMR of 98% for the comparison between surgical treatment [5] and the RealHealth programme indicate that a slightly larger percentage of the surgical intervention group showed improvement on the ODI. More participants of the RealHealth programme (10% when compared with surgical intervention group; 18% for the rehabilitation group) showed improvement on the SF36 PCS. No comparative literature study could be found for the PSEQ. Table 2.3 Clinical relevancy after 12 months RMDQ (0 – 24) ODI (0 - 100) PSEQ (0 - 60) SF36 PCS (0 - 100) Effect Size RealHealth NL 0.9 0.8 0.8 1.3 Cohen’s d Smeets et al. [9] 0.2 - - - MCID (%) Surgery Rehab 0.5 Fairbank et al. [5] - 0.7 RealHealth NL 79% 56% SMR (%) Surgery Rehab 136% [9] 98% [5] 107% [5] (79/58) (77/79) (77/72) Surgery Rehab - 0.8 0.6 53% 80% - Surgery Rehab 110% [5] 118% [5] (85/77) (85/72) Calculated percentages of the RealHealth programme participants who reached the MCID. Effect sizes and SMR as relative rate for treatment improvement were calculated for the RealHealth programme as well as for the external reference populations found in literature [5,9] . MCID, minimal clinical important difference: MCID for RMDQ = 2 points [9]; MCID for ODI = 10 points [17,20]; MCID for PSEQ = 8.5 (based on 25% of the mean baseline score); MCID for SF36 PCS = 5.4 point [22] SMR, standardized morbibidity ratio Daily functioning after a pain management programme 37 Figures 2.1-4. Graphic trends of three scenarios: the worst case (imputation of baseline value, n=107), CLOF (carrying the last observation forward, n=107), and the best case scenario (completed cases only, n=82) 02 Figure 2.1 Oswestry Disability Index (ODI) Figure 2.2 Roland & Morris Disability Questionnaire (RMDQ) Figure 2.3 Pain Self Efficacy Questionnaire (PSEQ) Figure 2.4 SF36 Physical Component Score (SF36 PCS) 38 Daily functioning after a pain management programme Discussion 02 The aim of this article was to evaluate the 1-year follow-up results in patients with CLBP after a cognitive behavior based programme provided by RealHealth-Netherlands. This programme follows the international guidelines [10], and is indicated for patients for whom surgery or anaesthesiology is not an option. These results were compared to the outcomes for interventions for patients with CLBP found in published research. More than half of the prospective cohort of RealHealth participants showed improvement in daily functioning, learned to manage their chronic disabling low back pain, and for a tremendous amount of the cohort (80%) their quality of life improved significantly. All these clinically relevant results were sustained at 1-year follow up, and were meaningful to patients. These improvements were greater than the reference MCID for all outcome measures. The effect of the treatment is further established by the large effect sizes (Cohen’s d ranging from 0.8 to 1.3) found for the four outcome measures, which indicate a treatment effect and by the SMRs used to compare the RealHealth results to two other studies in which patients were treated for CLBP; these ranged from 98% to 136%. When compared to other multidisciplinary rehabilitation programmes, the results for daily functioning favor the RealHealth programme, and were comparable to those achieved after fusion surgery. Comparison with related research: multidisciplinary rehabilitation programmes We compared the results of the RealHealth programme to the interventions recently conducted in The Netherlands by Smeets et al.[9,13], in which the patients were randomized among three treatment groups (the active physical, the cognitive behavioral, and a combination of both) was also compared to a group of patients on the waiting list. One-year post-treatment results showed that all three active treatments were more effective than no treatment; however, no treatment group had greater clinical relevant improvement than the other two. The cognitive behavioral arm is most similar to the RealHealth programme; therefore, those results were compared to those obtained in the present study. The baseline level of functional disability, measured by the RMDQ, is comparable to that of our study population (both 14 ±4). Depending on the therapy given, the Smeets et al. study reported at 1-year follow up a mean improvement of 2-4 points on the RMDQ; this is lower than the 5 points improvement reported in the RealHealth study. When a 2-point reduction on RMDQ was used to show clinical relevancy, 58% of those patients reached this threshold 1-year post-treatment. When we applied the same criterion, 79% of our patients showed a clinically relevant improvement. An extremely small effect size was calculated for the cognitive behavioral group of Smeets et al. for functional disability (d= 0.2) versus the large effect size (d= 0.9) of the programme of RealHealth, which is favorably sustained by the SMR of 136%. The small effect size found is possibly due to the heterogeneous patients, as shown in the relatively large standard deviation of 4 points. The authors of that study also mentioned the limited exposure to cognitive treatment (78h). The results of the RealHealth programme seems to validate the guideline value of 100h exposure to cognitive behavioral techniques [8]. Comparison with related research: fusion surgery We also compared the results of the RealHealth programme to those reported in the MRC spine stabilization trial by Fairbank et al.[5]. They randomized patients to either a surgical stabilization (spinal fusion) or an intensive rehabilitation. Those authors concluded there is Daily functioning after a pain management programme no clear evidence that primary spinal fusion surgery was any more beneficial than intensive rehabilitation. We chose that study to determine whether an intensive cognitive behavioral pain management programme could be an alternative for patients with longstanding CLBP. In studies in which evaluations of surgical interventions, such as spinal fusion were reported, the primary disability outcome measure usually is the ODI. When comparing the baseline characteristics for disability, we found that the surgical group had a score of 47, whereas the mean score in the rehabilitation group was 45, which are reasonably comparable to the 41 points found in the current study. Although in the present study an orthopedic surgeon confirmed that there was no indication for an invasive treatment, this baseline characteristic implies that the RealHealth programme treated patients with equivalent severity of disability. At 2-year follow up a 13-point improvement in ODI score was found for the surgical group and 9 points for the rehabilitation group. Although we had only 1-year results, the current study shows 12 points improvement in ODI, which compares favorably with both the rehabilitation and surgical intervention groups of the UK MRC trial. Considering the generally accepted MCID, a 10-point improvement is needed to show meaningful clinical relevance [17,20]. In short, the RealHealth programme seems to be superior to the surgical or rehabilitation treatments: the effect size for quality of life related to physical functioning is extremely large (d= 1.3) and considered large for disability (d= 0.8). The comparison for reduction of disability is equivalent: 0.8 vs. 0.7 and 0.6, for surgical and rehabilitation treatment, respectively. This conclusion is substantiated since 80% of the RealHealth participants reported a meaningful improvement in quality of life after a year and 56% reported that they functioned better than previously. Therefore, the RealHealth programme seems to be an important alternative intervention when fusion surgery is considered. The improvement found with the RealHealth programme in comparison to the intensive outpatient rehabilitation programme reported by Fairbank et al. [5] may be explained by the fact that only 75h of outpatient rehabilitation had been offered during a 3-week period. Once more the RealHealth programme’s 100h seems to validate the guideline value of 100h exposure to cognitive behavioral techniques [8]. Limitations of the study This study also has its limitations. According to the inclusion criteria used for the cognitive behavioral RealHealth programme, patients are required to have no indication for surgical treatment and had to be motivated to change their behavior with regard to their pain complaints. This implies that a selection bias might have been introduced, especially since the Sint Maartenskliniek is a specialized hospital to which patients are often referred for second opinion or as a last resort. This limits the generalizability of the results. However, this type of treatment will not be efficacious if patients are not motivated to change their behavior. Therefore, it is possible that in general practice, the results could be less favorable since the inclusion criteria would not be as narrowly defined. In the study reported here, several precautions were taken to enhance internal validity and minimize the potential influence of bias and confounding on the outcome factors, these include multivariable adjustment, sensitivity analysis and standardization. Emphasis should to be placed on the use of sensitivity analyses, which was motivated by the fact that there were item-missing data on the outcome measurements. Although no systematic pattern was discerned, these missing data need to be taken into consideration when the results of the 39 02 40 02 Daily functioning after a pain management programme study are estimated and interpreted. The analyses indicate the ranges within which the effects may be expected and do not give an exact estimate of the magnitude of the effect. The use of SMR provides information about the magnitude of effect compared to other studies and/ or populations. An SMR, a rate ratio, is thus an estimate of relative treatment improvement. However, since an external reference has been used to calculate the SMRs, discrepancies in the population characteristics are likely and it is not possible to judge whether sufficient controls have been conducted to enhance the internal validity within the studies reported in the literature. However, the SMRs can be accepted as a technique to enable a measure of comparison. In summary, the comparisons with other treatments seem to indicate that the intensity, duration and frequency of a cognitive behavioral pain management programme may be the most important key to success. The results of the short and intensive RealHealth programme are promising, but further research is needed to determine which factors of CLBP are influenced by this treatment programme, and to answer the frequently reported question [2,9,12]: ‘Which type of patient benefits the most?’ It is interesting to note that even participants with longstanding CLBP complaints (mean complaint duration in the RealHealth programme was 12 years) seem to experience benefits from this short, intensive programme, suggesting that CLBP duration is not an important factor for the management of CLBP. Factors such as mood, fear of movement, catastrophizing, coping strategies, and self-management, which will be analyzed in a following study, do seem to be important factors. Although the comparison technique used in this study has some drawbacks, it is justified to conclude that the RealHealth programme seems to be allow CLBP patients to achieve clinically relevant improvement; the treatment effect seems to be comparable to spinal surgery and to achieve better results than less intensive rehabilitation programmes. Acknowledgement The authors thank the multidisciplinary treatment team at Realhealth NL who were responsible for the intake procedure and training the participants in pain management programme. Particular thanks are for Cissy Matthijssen (RealHealth NL), Lauren White (RealHealth UK) for their assistance in completing the database, and Patricia G. Anderson for her editorial assistance. Daily functioning after a pain management programme 41 References 1. van Tulder MW, Koes BW, Bouter LM. A cost-of-illness study of back pain in The Netherlands. Pain 1995;62:233240 2. van Tulder MW, Ostelo R, Vlaeyen JW, Linton SJ, Morley SJ, Assendelft WJ. Behavioral treatment for chronic low back pain: a systematic review within the framework of the Cochrane Back Review Group. Spine 2000;25:2688-2699 3. Smeets RJ, Wade D, Hidding A, Van Leeuwen PJ, Vlaeyen JW, Knottnerus JA. The association of physical deconditioning and chronic low back pain: a hypothesis-oriented systematic review. Disabil.Rehabil. 2006;28:673-693 4. Picavet HS, Schouten JS. Musculoskeletal pain in the Netherlands: prevalences, consequences and risk groups, the DMC(3)-study. Pain 2003;102:167-178 5. Fairbank J, Frost H, Wilson-MacDonald J, Yu LM, Barker K, Collins R. Randomised controlled trial to compare surgical stabilisation of the lumbar spine with an intensive rehabilitation programme for patients with chronic low back pain: the MRC spine stabilisation trial. BMJ 2005;330:1233 6. Koes BW. Surgery versus intensive rehabilitation programmes for chronic low back pain. BMJ 2005;330:12201221 7. Mirza SK, Deyo RA. Systematic review of randomized trials comparing lumbar fusion surgery to nonoperative care for treatment of chronic back pain. Spine (Phila Pa 1976.) 2007;32:816-823 8. Guzman J, Esmail R, Karjalainen K, Malmivaara A, Irvin E, Bombardier C. Multidisciplinary bio-psycho-social rehabilitation for chronic low back pain. Cochrane.Database.Syst.Rev. 2002:CD000963 9. Smeets RJ, Vlaeyen JW, Hidding A, Kester AD, van der Heijden GJ, Knottnerus JA. Chronic low back pain: physical training, graded activity with problem solving training, or both? The one-year post-treatment results of a randomized controlled trial. Pain 2008;134:263-276 10. National Institute for Health and Clinical Excellence. Low Back Pain: Early management of persistent non-specific low back pain [Report]. NICE Clinical Guideline 88. London, National Collaborating Centre for Primary Care; 2009 11. Chou R, Loeser JD, Owens DK, Rosenquist RW, Atlas SJ, Baisden J et al. Interventional therapies, surgery, and interdisciplinary rehabilitation for low back pain: an evidence-based clinical practice guideline from the American Pain Society. Spine (Phila Pa 1976.) 2009;34:1066-1077 12. McCracken LM, Turk DC. Behavioral and cognitive-behavioral treatment for chronic pain: outcome, predictors of outcome, and treatment process. Spine 2002;27:2564-2573 13. Smeets RJ, Vlaeyen JW, Hidding A, Kester AD, van der Heijden GJ, van Geel AC et al. Active rehabilitation for chronic low back pain: cognitive-behavioral, physical, or both? First direct post-treatment results from a randomized controlled trial. BMC.Musculoskelet.Disord. 2006;7:5 14. Roland M, Morris R. A study of the natural history of back pain. Part I: development of a reliable and sensitive measure of disability in low-back pain. Spine 1983;8:141-144 15. Roland M, Fairbank J. The Roland-Morris Disability Questionnaire and the Oswestry Disability Questionnaire. Spine 2000;25:3115-3124 16. Ostelo RW, de Vet HC, Knol DL, van den Brandt PA. 24-item Roland-Morris Disability Questionnaire was preferred out of six functional status questionnaires for post-lumbar disc surgery. J.Clin.Epidemiol. 2004;57:268-276 17. Ostelo RW, Deyo RA, Stratford P, Waddell G, Croft P, Von Korff M et al. Interpreting change scores for pain and functional status in low back pain: towards international consensus regarding minimal important change. Spine 2008;33:90-94 18. Fairbank JC, Couper J, Davies JB, O'Brien JP. The Oswestry low back pain disability questionnaire. Physiotherapy. 1980;66:271-273 02 42 Daily functioning after a pain management programme 19. Wilson-MacDonald J, Fairbank J, Frost H, Yu LM, Barker K, Collins R et al. The MRC spine stabilization trial: surgical methods, outcomes, costs, and complications of surgical stabilization. Spine 2008;33:2334-2340 20. Ostelo RW, de Vet HC. Clinically important outcomes in low back pain. Best.Pract.Res.Clin.Rheumatol. 02 2005;19:593-607 21. Tafazal SI, Sell PJ. Outcome scores in spinal surgery quantified: excellent, good, fair and poor in terms of patient-completed tools. Eur.Spine J. 2006;15:1653-1660 22. Glassman SD, Gornet M.F., Branch C., Polly D.Jr., Peloza J.D., Schwender J.D. et al. MOS Short Form 36 and Oswestry Disability Index outcomes in lumbar fusion: a multicenter experience. Spine J. 2006;6:21-26 23. Nicholas MK. The pain self-efficacy questionnaire: Taking pain into account. Eur.J.Pain 2007;11:153-163 24. Ware JE, Jr.Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med.Care 1992;30:473-483 25. Redelmeier DA, Guyatt GH, Goldstein RS. Assessing the minimal important difference in symptoms: a comparison of two techniques. J.Clin.Epidemiol. 1996;49:1215-1219 26. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Earlbaum Associates; 1988 27. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams and Wilkins; 2008; 664-681 43 Chapter 03 A short, intensive cognitive behavioral pain management program reduces healthcare use in patients with chronic low back pain Two-year follow-up results of a prospective cohort van Hooff ML ter Avest W Horsting PP O’Dowd J de Kleuver M van Lankveld W van Limbeek J Published in: Eur Spine J. 2012; 21(7):1257-64 44 Abstract Purpose: Cognitive behavioral interventions are recommended as non-invasive treatment options for patients with chronic low back pain (CLBP). However, most treatment effects are small and short-lived. Although a 2-week intensive pain management program for patients with CLBP seems to be effective, the long-term results are not known. The purpose of this study is to evaluate the stability of the 2-year follow up results and whether this is reflected in the use of healthcare services. Methods: A prospective cohort study was performed. Pre-treatment characteristics of patients and data of outcomes obtained at 1-year follow up were used. At 2-year follow up a structured interview was conducted following the principles of a post-marketing survey. Outcomes included daily functioning, quality of life, current intensity of pain, disturbance of pain during daily activities, and indicators of the use of pain medication and healthcare services. Results: Of the 90 eligible patients 85 (94%) participated in the post-marketing survey. The 1-year clinical relevant effects are maintained at 2-year follow up. Effect sizes for functioning and quality of life were large. More than 65% reached preset minimal clinically important differences. At pre-treatment all patients consulted their general practitioner (GP) and medical specialist (MS). At 2-year follow up 73% reported having consulted neither a GP nor an MS during the previous year. Most of the patients indicated not to use any pain medication (57%) and the percentage patients using opioids has decreased (14%). Moreover, 81% reported to be at work. Conclusions: The gained results from selected and motivated patients with longstanding CLBP at 1-year follow up are stable at 2-year follow up. Above all, most of the participants are at work and the use of both pain medication and healthcare has decreased substantially. Outcomes and healthcare use after a pain management programme 45 Introduction Low back pain is one of the most common disabling conditions and causes high health expenditure in developed countries [1-3]. This condition has a high prevalence: over 70% of the adult population experience at least one episode of low back pain [1,4-6]. In the Netherlands the annual prevalence in 2003 was approximately 44% [1]. A minority (20%) develop chronic low back pain (CLBP), meaning that the complaints persist at least 3 months [2] and are associated with persistent or recurrent disability. These complaints may result in the individuals experiencing a lower health-related quality of life; they cause a quarter of all sick leave in the employed population [1,3,4]. The Dutch National Institute for Public Health and the Environment (RIVM) estimated CLBP-related costs to be 0.9% of total healthcare costs in the Netherlands [5], resulting in its being the top three of highest healthcare costs [6]. Moreover, 14% of the adult population with a disability allowance in the Netherlands is diagnosed with CLBP. Therefore, CLBP is not only a burden for the patient but the related healthcare costs are also a problem for society. A wide range of interventions to manage CLBP is used including pharmaceutical, surgical and non-surgical interventions [3,7,8]. However, many commonly used interventions lack evidence of clinically relevant long-term effects [4]. International guidelines [9-11], a Cochrane review [12] as well as recently performed randomized controlled trials have demonstrated that a cognitive behavioral approach most effectively reduces disability in CLBP patients [8,13-15]. Nevertheless, most effects achieved by these non-invasive treatments are small and shortlived [14,16-18]. A systematic review with 1964 randomly allocated patients concluded that 100h or more of intensive, multidisciplinary rehabilitation with a functional restoration approach including cognitive behavioral interventions reduces pain and improves functionality [19]. Furthermore, most reported treatment programs have a mean duration of 4 weeks [18,20] or more [8,13-15]. A recently published study by Van Hooff et al. [21] evaluated the 1-year results of a cohort of patients who participated in 2-week program provided by RealHealth NL. The program is based on cognitive behavioral principles and aims at improving daily functioning by self-management of lower back pain complaints. Participants with longstanding CLBP complaints (12 years on average) learned to manage CLBP, improved fast in daily functioning and experienced a fast improvement in their quality of life. These results were meaningful and clinically relevant to the participants and comparable to results after spinal surgery and superior to results for rehabilitation programs of longer duration. However, the question remains whether these positive short-term effects are sustained in the long run and whether these benefits are reflected in the degree of healthcare use and the use of pain medication. Therefore, in this study the main purpose is to evaluate the stability of the 2-year (longterm) follow-up results of a short, intensive cognitive behavioral pain-management program provided by RealHealth NL. The emphasis is on evaluating daily functioning, the use of healthcare services, and pain medication 2 years after the intervention. We hope that improvements gained in the first year (short-term) will be maintained and the use of healthcare services and pain medication will be reduced in the second year of follow up. 03 46 Outcomes and healthcare use after a pain management programme Materials and Methods 03 Study design and Setting This study is an extension of a prospective cohort study in which the effectiveness of a intensive cognitive behavioral pain management program was evaluated after 1 year of follow up [21]. We used data obtained by questionnaire at pre-treatment, including patient characteristics, outcome measures and indicators of healthcare and pain medication use. Outcome assessments performed at 1 year after treatment yielded the primary outcome measure and health-related quality of life (Short-Form 36). The outcomes were compared with outcome assessments at 2-year follow up. To achieve a high response rate a structured interview following the principles of a post-marketing survey was added to obtain data at 2-year follow up. A short description of participants, treatment, and outcome measures follows. Patients and Treatment A detailed description of participants and treatment has been reported previously [21]. Patients entered the study consecutively. The main inclusion criteria for the intervention were low back pain for at least 6 months, no indication for surgical or other invasive pain treatment confirmed by spinal surgeons at the Sint Maartenskliniek, no intention of seeking medical treatment in the year following the 2-week program, age between 20 and 65 years, motivation to change behavior, willing to follow the program and to reside in a hotel for 2 weeks, able to speak and read Dutch. The main exclusion criterion was psychiatric disorders. The evidence-based, intensive cognitive behavioral pain management program was developed by the RealHealth Institute in the United Kingdom and follows published international guidelines [9-11]. In the Netherlands all sessions are delivered by the trainers of the RealHealth multidisciplinary team. The team consists of a psychologist, a physiotherapist and an occupational therapist. The full program comprises an assessment day for intake, the 10-day residential program with two follow-up days: 1-month and 1-year post-treatment. The main aim of the program is to improve daily functioning. This is achieved by increasing the capability of self-manage the CLBP complaints. The program consists of 100h of participant contact time, approximately 50h of cognitive behavioral training, 35h of graded physical activities, and 15h of education in which the cognitive behavioral principles are integrated. The program is delivered in a 2-week, group-orientated residential setting. Outcome assessment: Procedure Participants who had completed the 1-year follow up were contacted. All recruited respondents were telephoned by a secretary and were asked if they were willing to participate in a followup study, including a telephone survey at a later time. When the respondent consented, the secretary made the appointment for a telephone call; the questionnaire booklet as well as a background information sheet was sent. The participants completed the questionnaire without assistance. The questionnaires are in the ‘Outcome Measures’ section, which included daily functioning, health-related quality of life, different pain scales, and questions about use of healthcare services, pain medication and return-to-work. An independent researcher (WterA) conducted the 20-min standardized telephone interviews in the period March–June 2010. During the telephone interview, the answers were passed without any discussion. A small gift voucher for flowers as a present for participation was sent after the interview was completed. Outcomes and healthcare use after a pain management programme 47 Outcome Measures Primary outcome: - Roland and Morris Disability Questionnaire (RMDQ) The RMDQ [22] contains 24 questions and measures functional disability in patients with low back pain [8,22]. The total score ranges between 0 (no disability) and 24 (maximal disability). Secondary Outcomes: - Short-Form 36 Health Survey Questionnaire (SF36) The SF36 [23] is a generic instrument to measure the health-related quality of life. The validated Dutch language version has been used in a wide range of studies among patients with chronic health conditions including CLBP [24]. The instrument contains 36 items in 8 subscales. The subscales results were combined into two summary scores: the SF36 Physical Component Score (SF36 PCS) and the SF36 Mental health Component Score (SF36 MCS). - Healthcare use Indicators for healthcare use were consultation of a general practitioner (GP) or a medical specialist (MS) and referral to a physical therapist (PT) or a psychologist (PS) during the previous 12 months as well as current pain medication consumption (analgesics). Patients were asked to provide information before the program and at 2-year follow up. Consultation and referral questions were scored on a dichotomous scale (yes/no), which in addition to information about the frequency of these visits yielded an impression of the program’s impact on healthcare use. Pain medication was classified in accordance with the threestep World Health Organization (WHO) analgesic ladder. These steps are (1) non-opioid analgesics with adjuvant therapy when needed, (2) an addition of a weak opioid, and (3) a strong opioid addition to non-opioid and adjuvant therapy [25]. For this study the first step was split into two categories: (1A) paracetamol also known as acetaminophen in the USA, and (1B) non-steroidal anti-inflammatory drugs (NSAIDs). Pain medication was then classified as: ‘none-light’ (none and WHO-step 1A) and ‘moderate-severe’ (WHO-steps 1B-3). The ‘none/light’ classification indicates analgesics, which have no or only few side effects [3,16,26]. The analgesics in the ‘moderate-severe’ classification are known to have adverse side effects, especially when used for a long period [3]. Furthermore, we classified consumption of analgesics as being ‘structural’ (daily) and ‘incidental’ (only when needed or less than once a week). Tertiary outcome: - Visual Analogue Scales for pain to measure current intensity and disturbance during daily activities (VAS ‘current intensity’ and VAS ‘disturbance ADL’) Participants were asked about the current intensity of their back pain for the day of the questionnaire and about the disturbance of back pain during daily activities. Both severity and disturbance were marked on a line of 100 mm, with 0 mm indicating ‘no pain’ and 100 mm ‘unbearable pain’ [27,28]. 03 48 03 Outcomes and healthcare use after a pain management programme Statistical Analysis Frequencies of characteristics assessed at pre-treatment and healthcare use are described. To compare the characteristics of non-responders, an independent Student’s t test was performed for the pre-treatment characteristics and the outcome measures. Maintenance of gained results at 2-year follow up for all outcomes, except for healthcare use, was calculated with a paired samples Student’s t test. To explore clinical relevance we calculated effect sizes (Cohens’ d) for primary and secondary outcomes (RMDQ and SF36 PCS) to indicate the magnitude of treatment effect for the RealHealth program. This measure is defined as the difference between the means of the pretreatment assessment and of the 2-year follow up divided by the pooled standard deviation. An effect size (d) of 0.2 is considered to be small, 0.5 moderate, while 0.8 indicates a large effect [29]. Moreover, an effect size (d) of 1 is equivalent to a change of 1 standard deviation in the study sample. All statistical analyses were conducted using SPSS, version 17.0 for Windows. We set the level of significance at 0.05. Pie charts to present frequencies are created in STATA version 10.0. Results Response In March 2010 we had complete data sets available for 90 participants (84%), who were eligible to be contacted for the 2-year follow up. A total of five patients were seen as nonresponders, either because they could not be reached in time (three patients) or was in final stage of illness and had other priorities (one patient) or wished not to co-operate (one patient). These five non-responders were not significantly different to the included participants with regard to pre-treatment characteristics and outcome measures: RMDQ, SF36 and both VAS scales (‘current intensity’ and ‘disturbance ADL’). A total of 85 participants (94%) joined in the post-marketing survey at 2-year follow up. Patient characteristics Table 3.1 shows the pre-treatment characteristics of the 85 participants. They reported longstanding CLBP (11 years on average) and 29% had an earlier surgery for their back problem. Outcomes and healthcare use after a pain management programme 49 Table 3.1 Pre-treatment characteristics and 2-year follow-up results of return-to-work as reported by the participants (n= 85) Pre-treatment characteristics n= 85 Sociodemographic Age, mean (SD, range min-max) in years 42.9 (± 8.4, 23-60) Gender n (%), male : female 35 (41%) : 50 (59%) Pre-treatment assessment 2-year follow up 57 (68%) : 28 (32%) 69 (81%) : 16 (19%) At work – Full time 31 (37%) 32 (38%) At work – Part time 26 (31%) 37 (44%) Unemployed because of CLBP 13 (15%) 8 (9%) 4 (5%) 3 (4%) 11 (13%) 5 (6%) Work status n (%) Yes : no Unemployed because of other causes Disability pension CLBP History Duration of LBP, mean (range min-max) in years 11.3 (1-51) Previous surgery n (%) yes : no 25 (29%) : 60 (71%) Clinical outcome In Table 3.2 outcome measures are presented except those for healthcare use. Between at 1and 2-year follow-up assessments the mean scores remained stable. Only pain ‘disturbance of ADL’ significantly improved between 1- and 2-year follow up: df (1,84), t= 2.57, p= 0.01. In Figure 3.1 the trends, means with 95% confidence intervals for the primary outcome ‘functional disability’ as measured with the RMDQ, are graphically presented. Table 3.2 Mean (SD) for outcome measures at 1- and 2-year follow up with t-values for paired comparisons and significance levels (n= 85) 1-year FU 2-year FU t-value p-value 7.5 (5.0) 7.2 (5.0) 0.75 0.45 SF36 PCS 64.6 (17.8) 65.9 (20.6) -0.50 0.62 SF36 MCS 70.9 (15.2) 71.9 (17.1) -0.52 0.60 VAS ‘current intensity’ 35.9 (23.4) 35.0 (27.5) 0.96 0.34 VAS ‘disturbance in ADL’ 35.3 (26.9) 27.1 (27.1) 2.57 0.01* Primary outcome RMDQ Secondary outcomes Tertiary outcomes FU, Follow up; RMDQ, Roland and Morris Disability Questionnaire; SF36 PCS, Short Form 36 Physical Component Scale; SF36 MCS, Short Form 36 Mental Component Scale; VAS, Visual Analogue Scale, with ‘current intensity’ indicating pain severity and ‘disturbance in ADL’ indicating disturbance of pain during daily activities *p< 0.05 03 50 Outcomes and healthcare use after a pain management programme Figure 3.1 Roland and Morris Disability Index (RMDQ); means and 95% confidence intervals. Trend of maintenance of gained results between 1-year and 2-year follow up assessment 03 Healthcare use At the pre-treatment assessment all participants reported to have consulted their general practitioner (GP) for their back problem, at least once in the past year, and all of them were referred to a medical specialist (MS; i.e. orthopaedic surgeon, neurologist, pain consultant, rheumatologist, physiatrist or anaesthesiologist). Furthermore, at pre-treatment assessment 48% of the participants (n= 41) had consulted at least two different MS in the previous year. At 2-year follow up only a quarter of all participants, 27% (n= 23) reported having consulted their GP in the last year and 14 of these 23 consulted an MS just once. The remaining 73% consulted neither a GP nor an MS in that year. At the pre-treatment assessment most of the participants (94%; n= 80) indicated to have had physical therapy for their back problem in the previous year. In addition, 15% (n= 13) visited a psychologist. At 2-year follow up the allied healthcare visits have considerably decreased, 29% (n= 24) reported to have had physical therapy and only 1% (n= 1) consulted a psychologist for their back pain-related problems in the last year. Medication use decreased from 87% (n= 74) at baseline to 43% (n= 37) at 2-year follow up. At pre-treatment assessment 68% of the participants (n= 58) used analgesics for their back problem on a structural basis, while 13% (n= 11) did not use any pain medication. The pie charts in Figure 3.2 show the frequencies of analgesic consumption as classified in WHO analgesic ladder both at pre-treatment and at 2-year follow up. At 2-year follow up the ‘none-light’ consumption group has increased to almost three quarters of the participants (n= 60; 71%), while the ‘moderate-severe’ group has decreased to 29% (n= 25). Outcomes and healthcare use after a pain management programme 51 Figure 3.2 Pie charts illustrating percentages of participants (n= 85) who use pain medication, classified in accordance with the steps in WHO analgesic ladder[25] and differentiated in consumption groups: ‘none-light’ (green) and ‘moderate-severe’ (red) Consumption: Pain medication Pre-treatment assessment 2-year follow-up 03 3% 10% 13% 11% 15% 22% 15% 57% 40% None WHO_1B: NSAID 14% WHO_1A: Paracetamol WHO_2: Weak Opioid WHO_3: Strong Opioid Clinical Relevance The effect size (Cohens’ d) for functioning (RMDQ) is 1.6 and for functioning-related quality of life (SF36 PCS) is 1.4. The effect sizes of both measures were larger than 1 and, therefore, classified as ‘large’. These results were further substantiated by data related to work status as presented in Table 3.1. At 2-year follow up, 81% of all participants reported being at work. Eight of the 13 participants who had reported at pre-treatment assessment being unemployed because of their back problem were working 2 years after the treatment. Moreover, 5 out of 11 participants who received a disability allowance at baseline indicated having returned to work. Discussion The main purpose of this study was to evaluate the 2-year follow up results of the cognitive behavioral pain management program offered by RealHealth NL in patients with CLBP. We questioned whether improvements gained in the first year would be maintained and whether this would be reflected in the use of pain medication and healthcare services. Patients in our study population appeared to have a mean baseline level of functioning as measured with RMDQ (13 ±4). This level is indicative for a moderate to severe level of disability, which is comparable to patients being treated in other trials and daily practice in the Netherlands 52 03 Outcomes and healthcare use after a pain management programme [8,21]. In this study we confirmed that the previously reported 1-year clinically relevant effects on daily functioning and quality of life were maintained at the 2-year follow up. Participants even reported experiencing less pain while performing activities; this decrease was statistically significant. Moreover, healthcare use (i.e. GP, MS consultations and pain medication use at 2-year follow up) decreased between baseline and 2-year follow-up assessment. Positive outcomes of the intervention were further corroborated by work status data. Most of the participants returned to work, with 81% actually at work at 2-year follow up. These results suggest that patients who participated managed to incorporate the learned self-management techniques in daily life and that they changed their occupational and social behavior. Many commonly used interventions lack evidence for the maintenance of clinically relevant long-term effects [4]. This study shows large effect sizes (Cohens’ d= 1.4-1.6). Although we had five non-responders in the current study they did not differ in patient characteristics and outcomes at baseline to the included patients. Their dropout was not related to either the treatment program or the study itself. Therefore, it is noteworthy that patients with longstanding CLBP complaints, 11 years on average, benefit from this short and intensive pain management program which is based on international guidelines [9-11]. Post hoc analyses revealed that no significant correlations existed between duration of CLBP and change in the outcome measures at 1-year follow up (RMDQ r= 0.05; SF36PCS r= 0.07; current pain r= 0.15; pain disturbance ADL r= 0.09). The current study results suggest that the duration of CLBP is not an important factor for the management of CLBP, whereas duration and intensiveness of the program are important [7,12,21]. It is known that CLBP accounts for considerable healthcare and socioeconomic costs [5,6,14]. These healthcare costs are, among other things, related to sick leave and disability allowance, referrals to general practitioners and medical specialists, use of allied health care and pharmaceutical prescriptions for analgesics. Therefore, we evaluated healthcare use on all of these dimensions of healthcare costs. The results of this study show that healthcare use is decreased at long-term follow up. A marked reduction of analgesic use is seen and a shift of most patients is shown from the ‘moderate-severe’ (WHO-steps 1B-3; 65%) to the ‘nonelight’ (none and WHO-step 1A; 71%) category of the WHO-analgesic ladder. Moreover, with a reduction in analgesic use a decrease of pain intensity and pain experience during daily activities (VAS scores) is shown, as well as maintenance of these results at 2-year follow up. In patients with CLBP antidepressants are sometimes prescribed for pain reduction (selective serotonin reuptake inhibitors [SSRI] and tricyclyc antidepressants [TCA]). We found at baseline that only 11% used antidepressants (4% TCA and 7% SSRI) and at long-term follow up a reduction in consumption is seen: only two respondents (2%) reported to use this medication (2% TCA and none used SSRI). This implies that the program is successful not only on healthcare use with a reduction in healthcare costs, but also on safety possibly resulting in less adverse side effects. When the results are extrapolated to the Dutch adult population a quarter of the patients with CLBP could benefit from this program and therefore an estimated half of the healthcare costs could be saved. Outcomes and healthcare use after a pain management programme Limitations of the study This study has some limitations. The external validity of the study might be limited, depending to whom the results are generalized. Since we studied a prospective cohort with carefully selected patients over a period of time, we have to restrict the generalization to patients with similar characteristics. The patients included had no indication for a surgical intervention and they had to confirm that they were motivated to change their behavior with regard to the back pain complaints. Therefore, generalization to the general population is limited. We evaluated healthcare use by means of self-reported questionnaires and, therefore, bias could have been introduced. We took this aspect into account in the design of the study, a structured post-marketing survey, and by asking the participants to request additional information at general practitioner or pharmacy if necessary. A possible bias could have been introduced due to the fact that patients had to recall what happened in the last year. The intervention described in this study uses a wide range of techniques based on the principles of CBT. As yet, it is unclear which techniques or parts of the intervention are responsible for the observed effect. Therefore, we studied the program as an integral program. The main aim of the study was to evaluate the stability of positive outcomes of a short, intensive intervention and its impact on healthcare use. Therefore, we did not evaluate frequently reported cognitive and emotional factors as fear of movement, catastrophizing, and anxiety [4,30-32]. These cognitive and emotional factors contribute, among other factors, to a certain extent to the main outcome functioning and quality of life. We showed a long-term significant improvement on the mental component scale of the SF36, but a closer exploration of these cognitive behavioral factors and their impact on functionality is needed. Moreover, patients attending in this program have to be motivated to change behavior. Although a selection criterion for treatment, we neither assessed this factor at baseline nor assessed it systematically over time. Therefore, a clear description of ‘motivation to change behavior’ in the subgroup of CLBP patients benefitting from this program cannot be given. ‘Readiness or motivation to change pain-related behavior’ is a multidimensional construct recently described in the literature [33,34]. As individuals may vary in their readiness to learn and adopt new coping skills or selfmanagement strategies it may be a key element in understanding how participants benefit or fail to benefit from this program. Conclusion In a selected and motivated group of patients with longstanding CLBP the results of a short, intensive cognitive behavioral pain management program gained in the first year are maintained at 2-year follow up. Above all, at follow up most of the participants are at work, they perform a gainful employment, and the results suggested that the use of both pain medication and healthcare have decreased substantially. Acknowledgement The authors thank Mrs. P.G. Anderson for her editorial comments. 53 03 54 Outcomes and healthcare use after a pain management programme References 1. Picavet HS, Schouten JS. Musculoskeletal pain in the Netherlands: prevalences, consequences and risk groups, the DMC(3)-study. Pain 2003;102:167-178 03 2. Frymoyer JW. Predicting disability from low back pain. Clin.Orthop.Relat Res. 1992:101-109 3. Cohen SP, Argoff CE, Carragee EJ. Management of low back pain. BMJ 2008;337:a2718 4. Koes BW, van Tulder MW, Thomas S. Diagnosis and treatment of low back pain. BMJ 2006;332:1430-1434 5. Picavet HS. Aspecifieke lage rugklachten: omvang en gevolgen [Report in Dutch]. Chronische Ziekten RIVM Centrum voor Preventie- en Zorgonderzoek PZO; 2005:1-8 6. Meerding WJ, Bonneux L, Polder JJ, Koopmanschap MA, van der Maas PJ. Demographic and epidemiological determinants of healthcare costs in Netherlands: cost of illness study. BMJ 1998;317:111-115 7. Guzman J, Esmail R, Karjalainen K, Malmivaara A, Irvin E, Bombardier C. Multidisciplinary bio-psycho-social rehabilitation for chronic low back pain. Cochrane.Database.Syst.Rev. 2002:CD000963 8. Smeets RJ, Vlaeyen JW, Hidding A, Kester AD, van der Heijden GJ, Knottnerus JA. Chronic low back pain: physical training, graded activity with problem solving training, or both? The one-year post-treatment results of a randomized controlled trial. Pain 2008;134:263-276 9. Airaksinen O, Hildebrandt J, Mannion AF, Ursin H, Brox JI, Klaber-Moffett J et al. European Guidelines for the management of chronic non-specific low back pain. Working Group on Guidelines for Chronic Low Back Pain; 2004 10. Chou R, Loeser JD, Owens DK, Rosenquist RW, Atlas SJ, Baisden J et al. Interventional therapies, surgery, and interdisciplinary rehabilitation for low back pain: an evidence-based clinical practice guideline from the American Pain Society. Spine (Phila Pa 1976.) 2009;34:1066-1077 11. National Institute for Health and Clinical Excellence. Low back pain: early management of persistent non-specific low back pain [Report]. NICE Clinical Guideline 88. London, National Collaborating Centre for Primary Care; 2009 12. Ostelo RW, van Tulder MW, Vlaeyen JW, Linton SJ, Morley SJ, Assendelft WJ. Behavioural treatment for chronic low-back pain. Cochrane.Database.Syst.Rev. 2005:CD002014 13. Johnson RE, Jones GT, Wiles NJ, Chaddock C, Potter RG, Roberts C et al. Active exercise, education, and cognitive behavioral therapy for persistent disabling low back pain: a randomized controlled trial. Spine (Phila Pa 1976.) 2007;32:1578-1585 14. Lamb SE, Hansen Z, Lall R, Castelnuovo E, Withers EJ, Nichols V et al. Group cognitive behavioural treatment for low-back pain in primary care: a randomised controlled trial and cost-effectiveness analysis. Lancet 2010;375:916-923 15. Lambeek LC, van Mechelen W, Knol DL, Loisel P, Anema JR. Randomised controlled trial of integrated care to reduce disability from chronic low back pain in working and private life. BMJ 2010;340:c1035 16. Hall H, McIntosh G. Low back pain (chronic). BMJ Clin.Evid. 2008 17. Wand BM, O'Connell NE. Chronic non-specific low back pain - sub-groups or a single mechanism? BMC. Musculoskelet.Disord. 2008;9:11 18. Hampel P, Graef T, Krohn-Grimberghe B, Tlach L. Effects of gender and cognitive-behavioral management of depressive symptoms on rehabilitation outcome among inpatient orthopedic patients with chronic low back pain: a 1 year longitudinal study. Eur.Spine J. 2009;18:1867-1880 19. Henschke N, Ostelo RW, van Tulder MW, Vlaeyen JW, Morley S, Assendelft WJ et al. Behavioural treatment for chronic low-back pain. Cochrane.Database.Syst.Rev. 2010:CD002014 20. Poulain C, Kerneis S, Rozenberg S, Fautrel B, Bourgeois P, Foltz V. Long-term return to work after a functional restoration program for chronic low-back pain patients: a prospective study. Eur.Spine J. 2010;19:1153-1161 Outcomes and healthcare use after a pain management programme 55 21. van Hooff ML, van der Merwe JD, O'Dowd J, Pavlov PW, Spruit M, de Kleuver M et al. Daily functioning and self-management in patients with chronic low back pain after an intensive cognitive behavioral programme for pain management. Eur.Spine J. 2010;19:1517-1526 22. Roland M, Morris R. A study of the natural history of back pain. Part I: development of a reliable and sensitive measure of disability in low-back pain. Spine (Phila Pa 1976.) 1983;8:141-144 23. Ware JE, Jr., Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med.Care 1992;30:473-483 24. Aaronson NK, Muller M, Cohen PD, Essink-Bot ML, Fekkes M, Sanderman R et al. Translation, validation, and norming of the Dutch language version of the SF-36 Health Survey in community and chronic disease populations. J.Clin.Epidemiol. 1998;51:1055-1068 25. Ehrlich GE. Low back pain. Bull.World Health Organ 2003;81:671-676 26. Bertin P, Keddad K, Jolivet-Landreau I. Acetaminophen as symptomatic treatment of pain from osteoarthritis. Joint Bone Spine 2004;71:266-274 27. Huskisson EC. Measurement of pain. Lancet 1974;2:1127-1131 28. Von Korff M, Jensen MP, Karoly P. Assessing global pain severity by self-report in clinical and health services research. Spine (Phila Pa 1976.) 2000;25:3140-3151 29. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Earlbaum Associates; 1988 30. Vlaeyen JW, Kole-Snijders AM, Boeren RG, van Eek H. Fear of movement/(re)injury in chronic low back pain and its relation to behavioral performance. Pain 1995;62:363-372 31. Picavet HS, Vlaeyen JW, Schouten JS. Pain catastrophizing and kinesiophobia: predictors of chronic low back pain. Am.J.Epidemiol. 2002;156:1028-1034 32. Woby SR, Watson PJ, Roach NK, Urmston M. Are changes in fear-avoidance beliefs, catastrophizing, and appraisals of control, predictive of changes in chronic low back pain and disability? Eur.J.Pain 2004;8:201210 33. Kerns RD, Habib S. A critical review of the pain readiness to change model. J.Pain 2004;5:357-367 34. Nielson WR, Armstrong JM, Jensen MP, Kerns RD. Two brief versions of the multidimensional pain readiness to change questionnaire, version 2 (MPRCQ2). Clin.J.Pain 2009;25:48-57 03 56 03 57 Chapter 04 Predictive factors for successful outcome one year after an intensive combined physical and psychological programme for chronic low back pain Van Hooff ML Spruit M O’Dowd JK van Lankveld W Fairbank JC van Limbeek J Published in: Eur Spine J. 2014;23(1):102-12 58 Abstract Purpose: The aim of this longitudinal study is to determine the factors, which predict a successful 1-year outcome from an intensive combined physical and psychological (CPP) programme in chronic low back pain (CLBP) patients. Methods: A prospective cohort of 524 selected consecutive CLBP patients was followed. Potential predictive factors included demographic characteristics, disability, pain and cognitive behavioural factors as measured at pre-treatment assessment. The primary outcome measure was the Oswestry Disability Index (ODI). A successful 1-year follow-up outcome was defined as a functional status equivalent to ‘normal’ and healthy populations (ODI ≤22). The 2-week residential programme fulfills the recommendations in international guidelines. For statistical analysis we divided the database into two equal samples. A random sample was used to develop a prediction model with multivariate logistic regression. The remaining cases were used to validate this model. Results: The final predictive model suggested being ‘in employment’ pre-treatment (OR 3.61 [95%CI 1.80-7.26]) and an initial ‘disability score’ (OR 0.94 [95%CI 0.92-0.97]) as significant predictive factors for a successful 1-year outcome (R2=22%; 67% correctly classified). There was no predictive value from measures of psychological distress. Conclusion: CLBP patients who are in work and mild to moderately disabled at the start of a CPP programme are most likely to benefit from it and to have a successful treatment outcome. In these patients the disability score falls to values seen in healthy populations. This small set of factors is easily identified, allowing selection for programme entry and triage to alternative treatment regimes. Prediction of successful outcome after a pain management programme 59 Introduction Chronic low back pain (CLBP) is a major cause of distress and disability and in the Netherlands CLBP accounts for considerable healthcare and socioeconomic costs [1,2]. CLBP is defined as back symptoms persisting for at least 3 months [3] and these symptoms are associated with persistent or recurrent disability. Multiple studies have emphasized the psychosocial influence on the development of chronicity and the persistence of pain complaints [4-6]. Increased distress accompanies more severe pain, enhances pain-related disability and contributes to the development of chronicity of LBP [7-9]. Some evidence suggests that fear of movement [8] and catastrophizing [8,10,11] play a role when pain has become persistent. In line with these findings, international guidelines [12-14] and a Cochrane review [15] have recommended multidimensional interventions using a cognitive behavioural approach to improve psychological and physical functioning. However, most of the interventions studied show only small, short-lived effects [7,15-17]. One explanation for these small effects could be the heterogeneity of the CLBP population studied. Although the aetiology of chronic low back pain remains unknown, it has been suggested that several subgroups could be identified amongst CLBP patients who are likely to benefit from specific recommended interventions [14]. It is possible that the efficacy of the interventions employing physical and cognitive behavioural approaches would be improved by matching interventions to patient characteristics. Multiple studies, including several systematic reviews, have studied patient characteristics to identify potentially predictive factors for treatment outcome in CLBP [6,18,19]. In the most recent review, Van der Hulst et al. [6] analyzed the prognostic value of numerous biomedical, demographic and psychosocial factors in 17 internally valid studies (n=3,356) to determine the multidisciplinary rehabilitation treatment outcome in patients with non-specific CLBP. Due to methodological flaws in the included studies, they were not able to define a generic set of predictive factors. These methodological problems include heterogeneity in the study populations, the high number of prognostic factors, and the wide variety of treatment and outcome measures. Against this background, more research related to the subject is warranted. A recent study reviewed the results of a short, intensive, 2-week residential combined physical and psychological (CPP) programme for patients with longstanding CLBP who were not eligible for spinal surgery. The main goal of that programme is to improve daily functioning. On average, there was a large, clinically relevant improvement in terms of both disability and quality of life. Both remained stable during the following 12 months [20]. Two years after participation in that programme, not only did these post-treatment improvements remain consistent, there was a substantial reduction in healthcare use and an increased return to work [21]. Although large effect sizes for disability had been found, the study showed a wide range in the improvements of disability among participants. Identifying the predictive factors associated with improvement in disability would enable the selection of those CLBP patients who are most likely to benefit from such a programme, and ultimately, to develop treatment regimes for those who will not. 04 60 Prediction of successful outcome after a pain management programme The purpose of this longitudinal study is to determine those factors pre-treatment that predict a successful CPP programme outcome. We defined successful outcome as a clinically relevant and consistent improvement at 1-year follow up towards the values seen in healthy populations. The data from524 consecutive CLBP patients were used to answer this. We expected that the pre-treatment degree of experienced pain intensity, belief in the ability to manage and to cope with CLBP complaints, the degree of disability, and employment status were the most likely predictive factors for treatment outcome. We expected high psychological distress to be an indicator of poor outcome [8,22,23]. 04 Methods Study design The predictive value of patient characteristics, including disability, pain severity, cognitive and behavioural factors were analysed prospectively. These analyses were related to the patient’s disability 12 months following the 2-week residential CPP programme. Patients The study group consisted of patients with CLBP referred to a tertiary orthopaedic hospital, specialized in spine care. Patients who had not improved following conservative treatment delivered in primary care [21] and who were not eligible for spinal surgery or invasive pain management were referred by the spine surgeons for the CPP programme. The main inclusion criteria were (1) low back pain for at least 6 months, (2) age between 18 and 65 years, (3) willingness to change behaviour, (4) willingness to follow the 2-week programme in a hotel facility, and (5) able to speak and read Dutch. The main exclusion criteria were involvement in litigation and/or compensation claims and psychiatric disorders as formally and primarily diagnosed by psychiatrists, in accordance with the DSMIV classification. Final inclusion was based on an extensive intake procedure and all patients were assessed by a multidisciplinary team consisting of a psychologist, a physiotherapist, an occupational therapist, and a movement teacher. Intervention The CPP programme is NICE guidelines compliant [14]; it is a CPP residential 2-week programme including a cognitive behavioural approach. The programme runs in collaboration with the spine surgeons. The group-orientated training sessions in the 2-week programme are delivered by a multidisciplinary team, extensively trained in cognitive behavioural techniques for chronic pain. The programme involves 100 h of patient contact time, delivered in a grouporientated residential setting, including 40 h of cognitive behavioral training, 30 h of physical activities, and a 10 h of education. It includes a pre-treatment assessment day, the 10-day residential programme, and 1 day follow-up assessments at 1 and 12 months post-treatment. The main goal of the intervention is to improve daily function, and this is made clear to the patient. This goal is achieved by increasing the participant’s ability to self-manage, addressing the psychological impact of pain, and increasing physical condition; all are directed towards decreasing disability and thus enhancing future return to work. A more detailed description of the intervention is reported in a previously published article [20]. Prediction of successful outcome after a pain management programme 61 Outcome measures We used a self-report questionnaire at pre-treatment, at the end of the 2-week residential programme (post-treatment), and at 12-months follow up. These assessments are an integral part of the programme. At pre-treatment assessment, participants provided information on medical history, pain history, pain scores, consumption of pain medication, and employment status. In this study, age was categorized in tertiles (years; age ≤42, 43-50, >50). We dichotomized values for the consumption of pain medication and employment status (‘employed’) into (1= yes; 0= no). At each assessment participants completed questionnaires on functional status, pain severity, psychological distress, self-efficacy, pain catastrophizing, and fear of movement. All these self-report measures have previously been validated in CLBP samples. Self-report measures The primary outcome variable for this study is functional status as measured by the Oswestry Disability Index (ODI, version 1.0 in Dutch) [24]. The ODI measures the impact of LBP on daily functioning in ten domains of daily life. In ‘normal’ healthy populations the weighted mean ODI score is 10 (SD range 2-12) and in chronic back pain 43.3 (SD range 10-21) [25]. We used several secondary outcome variables to quantify aspects of physical and psychosocial functioning. (1) Current pain severity was assessed with the Numeric Rating Scale (NRSseverity) [26], which is used to measure the experienced intensity of pain. The ordinal scores range from 0 to 100, with higher scores indicating higher levels of pain intensity. (2) The Modified Zung Self-Rating Depression Scale (ZSDS) [27] is an indicator of psychological distress and depression. Patients are asked to rate 23 items on a four-point ordinal scale. Total scores range between 0 and 69, with higher scores indicating higher levels of depressed mood. For patients with CLBP, the following classification has been given: <17 ‘normal’, 17-33 ‘at risk’ and >33 ‘depressed mood’ [28]. (3) We used the Pain Self-Efficacy Questionnaire (PSEQ, Dutch translation) [29] to measure the strength of the patient’s belief about the ability to accomplish a range of activities despite the pain. The PSEQ is a ten-item inventory with responses rated on a seven-point ordinal scale. The total score ranges from 0 to 60, with higher scores indicating higher perceived self-efficacy beliefs. (4) Dysfunctional cognitive behavioural factors (i.e. pain-related catastrophizing and fear of movement) were assessed with the Pain Catastrophizing Scale (PCS) and the Tampa Scale for Kinesiophobia (TSK). We used a Dutch translation of the PCS based on the original version [30]. The items are scored on a five-point ordinal scale. The total score is between 0 and 52 points, with higher scores reflecting higher levels of pain catastrophizing. Fear of movement/(re)injury in individuals with pain was measured by the TSK [27,31,32]. The unweighted sum score ranges between 17 and 68 points, with higher scores indicating higher levels of fear of movement. 04 62 Prediction of successful outcome after a pain management programme Data analysis Descriptive analysis Pre-treatment patient characteristics were descriptively summarized, with categorical data in count and percentages and continuous variables as means and standard deviations (SD). The percentage non-responders were calculated and the pre-treatment data of nonresponders and responders compared. To evaluate differences between both groups at pretreatment, we used Chi square tests for categorical variables and independent Student’s t tests for continuous variables. 04 Definition of ‘ successful treatment outcome’ The ODI as the primary outcome measure was used to define successful treatment. ‘Normal’ healthy populations have an ODI mean score of 10 (SD 2-12) [25]. Therefore, being successful was defined as having reached a maximum of 22 points on the ODI at the 1-year follow up, including the maximum reported standard deviation of 12 points (mean plus 2 SD). The scores of the patients were then dichotomized into ‘success’ (value= 1) and ‘failure’ (value= 0) for the programme, and both groups were compared to pre-treatment characteristics. Prediction analysis We considered all pre-treatment variables as factors that could influence the outcome. These influencing factors were identified by a prediction model. To develop the prediction model, associated factors were first identified by using Pearson’s correlation coefficients. To predict those factors with contribution to the probability of a successful outcome, a univariate logistic regression analysis was performed. Subsequently, we randomly divided the complete dataset into two equal samples. One sample was used to develop the final prediction model, and the second was used to validate that model. The final prediction model with odds ratios (OR) and 95% confidence intervals (95%CI) for predictive factors, was based on a multivariate logistic regression analysis. The dichotomized primary outcome variable disability (success/failure) was used as the dependent variable in this model. In one block the identified and significant pre-treatment patient characteristics and pre-treatment values on secondary outcomes were entered into the model as independent variables. A forward, stepwise selection method was used for analysis. The procedure starts with the independent variable that correlates most strongly with the dependent variable. Subsequently, the next independent variable is selected and added to the final model. The remaining cases were used to validate the final prediction model that had been developed. For this procedure, the identified predictive factors from the model developed were entered into the model as independent variables. This final prediction model was then used to calculate a pre-treatment probability as to whether an individual patient belongs to the group that will have a successful treatment outcome. This is estimated with the formula: p (success/failure)= ef(x) / 1+ef(x). For this purpose, all identified significant predictor variables were included in the logistic function: f(x) = a+b1x1+b2x2 … bkxk. The calculated probabilities are between 0 and 1, and interpreted as follows: <0.5 probability in favour of failure, >0.5 probability in favour of success, and 0.5 equal likelihood for either outcome. In the literature it has been hypothesized that patients with high psychological distress have a poor outcome [22], and more specifically, that the level of depressed mood contributes to pain-related disability [7,8]. Where in the above multivariate logistic regression analysis, Prediction of successful outcome after a pain management programme 63 psychological distress appeared to be a predictive value for treatment outcome, additional separate analyses are performed. All data analyses were performed using SPSS version 18.0. An α of 0.05 was considered statistically significant. A scatter plot to give an illustration of disability in the study sample was created in STATA version 10.0. Missing data As self-report questionnaires were used as outcome measures, we expected missing data during the follow-ups. To handle such missing data, the multiple imputation (MI) method was used under the assumption that the data were ‘missing at random’ (MAR). This implies that the missing data are related to other observed or documented patient data but not to unobserved outcomes. The MI-technique replaces each missing value of the incomplete data set with a set of plausible values (n= 10, current study), derived from the available data. These values represent the uncertainty in the correct value to impute. To generate these values to impute, the data augmentation Markov chain Monte Carlo replacement method was used. In this study ten datasets were generated; each generated dataset was analysed according to the previously mentioned statistical tests. Overall means, beta weights and standard errors were calculated. Results Study population The spine surgeons recruited 727 CLBP-patients for pre-treatment assessment. Between October 2006 and January 2011, 524 patients (72.1%) were included and participated in the programme. Of this sample (n=524), 67 patients (12.8%) had data missing from at least one assessment after the pre-treatment assessment. The flow diagram (Figure4.1) shows the available patient data at each stage of the study. At post-treatment assessment, data for 25 patients were missing. These included the first group of ten patients where this assessment had not been conducted. In addition, 15 patients left during the 2-week residential programme. The missing data of the remaining 42 patients were randomly divided between the 1- and 12-month assessments. These 67 (25+42) patients with missing data are not significantly different from the patients with complete data sets with regard to pre-treatment characteristics and the pre-treatment scores on the various outcome measures: ODI, PCS, TSK, NRSseverity, ZSDS, and PSEQ. General pre-treatment characteristics for the complete study population (n=524) are given in Table 4.1. The reported mean age was 45 (±9.6) years; a small majority was female (58%). The mean LBP duration was 13 (±10.8) years, indicating that our study population had longstanding CLBP. At pre-treatment assessment, two-thirds of the patients were at work (68%); one third had undergone surgery for LBP. 04 64 Prediction of successful outcome after a pain management programme Figure 4.1 Flow diagram of CLBP patients recruited by the spine surgeons at the outpatient orthopaedic department for the CPP programme and handling of the data of these patients Recruited (n= 727) Attending intake procedure (multidisciplinary team) 04 Included (n= 524) Participating in CPP programme Excluded after intake procedure (n= 727) I. Failed to meet inclusion criteria • Prefer biomedical treatment (n= 36) • Age (n= 2) • Language (n= 3) • Individual approach required (n= 42) • Mental / Physical ability (n= 22) II. Included, but decided not to join III.Included, but wished to postpone their participation until a later time IV.Reason unknown Prediction model: Development Random sample (n= 262) Imputation Prediction model: Validation Random sample (n= 262) Imputation (n= 47) (n= 44) (n= 7) Patients with missing data (n= 67) No data post-treatment (n= 25) • No post-treatment assessment (n= 10) • Left during 2-week programme (n= 15) - Lack of motivation (n= 8) - Illness (n= 4) - Family circumstances (n= 3) No data at follow-up * (n= 42) • 1-month follow-up assessment (n= 20) • 1-year follow-up assessment (n= 22) * Missing data random divided between 1 and 12 months follow-up Handling of data: all cases (n= 524) Study sample analysed with Multiple Imputation techniques (n= 105) Prediction of successful outcome after a pain management programme 65 Table 4.1 Pre-treatment characteristics reported by patients with CLBP participating in the CPP programme (n= 524) Pre-treatment characteristics Total (n=524) Disability categorical variables n (%) Successa (n=217) n (%) Failurea (n=307) n (%) Gender, female 303 (57.8) 118 (54.4)b 185 (60.3)b Employment status, yes 356 (67.9) 194 (89.4)c 162 (52.8)c Pain medication, yes 454 (86.6) 176 (81.1)d 278 (90.6)d Previous surgery, yes 169 (32.3) 54 (24.9)e 115 (37.5)e Sociodemographic CLBP History Pre-treatment characteristics Total (n=524) Disability continuous variables mean (SD) Success (n=217) mean (SD)f Failurea (n=307) mean (SD)f 45.4 (± 9.6) 43.7 (± 9.2) 46.6 (± 9.8) 12.5 (± 10.8) 11.7 (± 9.9) 13.0 (± 11.3) 41.4 (± 14.1) 33.7 (± 13.1) 46.8 (± 12.0) ZSDS Zung Self-rated Depression Scale 26.2 (± 9.3) 24.4 (± 9.9) 27.5 (± 8.6) NRS Numeric Rating Scale 60.7 (± 21.1) 56.4 (± 22.2) 63.7 (± 19.8) PCS Pain Catastrophizing Scale 22.9 (± 8.9) 22.3 (± 8.7) 23.4 (± 8.9) TSK Tampa Scale for Kinesiophobia 39.6 (± 6.4) 39.0 (± 6.5) 40.0 (± 6.4) PSEQ Pain Self-Efficacy Questionnaire 32.4 (± 10.8) 36.3 (± 10.1) 29.6 (± 10.4) a Sociodemographic Age, in years CLBP History Duration of LBP, in years Primary outcome ODI Oswestry Disability Index Secondary outcomes Success number of patients reaching at 1-year follow up a ‘normal’ value of ten points on ODI (SD 12); Failure number of patients reaching at 1-year follow up an ODI value of >22 points b 2 χ = 1.80, p= 0.21; c χ2 = 78.32, p<0.001; d χ2 = 9.81, p<0.05; e χ2 = 9.20, p<0.05 f All variables p<0.001 a Table 4.2 Means and standard deviations (SD) for the primary outcome of disability as measured with the Oswestry Disability Index (ODI) in this study and in reference populations Mean SD Pre-treatment assessment 41.4 14.1 One-year follow-up assessment 27.6 16.4 ‘Normal’ population [25] (n=461) 10.2 Range 2.2-12.0 Chronic back pain population [25] (n=1,530) 43.3 Range 10.0-21.0 RealHealthNL programme (n=524) a b b a Current study; b Values based on different study populations 04 66 04 Prediction of successful outcome after a pain management programme Disability in the study sample As shown in Table 4.2, the mean pre-treatment disability (ODI) score for the study sample is comparable with the reported weighted mean score in chronic back pain populations [25] (41.4 [SD 14.1] and 43.3 [SD range 10.0-21.0], respectively). Figure 4.2 shows that most of the patients are improved at one-year follow up (green values). Moreover, at one-year follow up 217 patients (41.4%) reached the value for disability as measured in ‘normal’ populations; the green values below the black dashed horizontal line. Of these patients, 60 (27.7%) already had an ODI pre-treatment value of 22 or less. At one-year follow up, the mean improvement in disability was 31.0% in relation to the pre-treatment value (25th percentile 59.0%; 50th percentile 32.3%; 75th percentile 8.5%). Figure 4.2 Functional status as measured with the ODI (0-100) in the study sample (n= 524). Prediction model for success at one-year follow up Overall, small Pearson correlations were found between pre-treatment patient characteristics and pre-treatment values for primary and secondary outcome measures as well as the outcome of being successful on the ODI; Pearsons’ r ranging from <0.01 (pre-treatment pain duration) to 0.62 (pre-treatment disability). This means that no strong co-linearity exists between different variables and successful outcome. In Table 4.1 the pre-treatment characteristics are described for the success and failure groups after the programme. As shown in Table 4.1, all but one categorical variable (‘gender’; χ2= 1.80, p= 0.21) as well as all the continuous variables were significantly associated with the outcome of 1-year successful diminution of perceived disability. A univariate logistic regression model was built with all these variables entered in one block. Pre-treatment age categories, previous surgery, being employed as well as pre-treatment pain self-efficacy and pre-treatment disability appeared to be potential predictor variables. With a forward selection method and in one block, these variables were included in the model. The final prediction model revealed being employed (OR 3.61 [95%CI 1.80-7.26]) and pre-treatment disability (OR 0.94 [95%CI 0.92-0.97]) as significantly contributing factors for clinically relevant improvement in disability, defined as having values Prediction of successful outcome after a pain management programme 67 measured in ‘normal’ populations (Table 4.3). No interaction effects between different pretreatment characteristics were found. Moreover, the results obtained by the ten databases that were generated from the MI-database produced the same result. These results imply that a patient has a 1.3-fold risk of failure in the programme when not employed at the pre-treatment assessment. Moreover, the predictive value of disability is protective, meaning that for each point that the pre-treatment ODI-score is closer to the normal value, the probability that that patient will meet the success criterion increases by 6.0%. Overall, using this final model, 66.8% of the participants had been correctly classified as being successful. The validity of the model was checked with the remaining cases (n= 262; Table 4.4). A multivariate prediction model with the same variables as found in the prediction model that had been developed was built. As shown in Table 4.4, the results are comparable to those found using the other half of the dataset. However, most of the 95%CI limits around the calculated OR’s (Exp [b]) are broader. This means that the model is less precise for the second half of the dataset, even though both the explained variance and the percentage participants correctly classified are higher than that found for the model developed with the first half of the dataset (R2= 40.0% [Hosmer & Lemeshow]) and 75.0%, respectively). To predict a patient’s probability of being successful after having participated in the programme, the identified contributing factors were included in the logistic function. For example, a patient who is employed at pre-treatment assessment, and who has a pretreatment ODI value of 35, is classified as probably being successful in the programme (p= 0.70). On the other hand, a patient who is not employed at pre-treatment assessment, and who has a pre-treatment ODI value of 60, will probably be a failure in the programme (p= 0.13). Contribution of psychological distress As psychological distress appeared not to be a predictive factor for treatment outcome, a separate analysis was not performed. Therefore, there is no evidence to support the hypothesis that an association exists between ‘depressed mood’ and failure 1 year after the programme. 04 68 Prediction of successful outcome after a pain management programme Table 4.3 Development of a multivariate logistic regression model for being successful at 1-year follow up (n= 262; 50% random selection of cases). Forward selection method Β (SE) Wald p (final model) 95% Confidence Interval for Exp (b) Lower Exp (b) Upper 0.91 0.93 0.95 Included: Step 1. 04 Functional status (ODI) -0.07 (0.01) 37.35 0.00 Constant 2.39 (0.48) 24.97 0.00 Employed 1.28 (0.36) 12.97 0.00 1.80 3.61 7.26 Functional status (ODI) -0.06 (0.01) 24.32 0.00 0.92 0.94 0.97 Constant 1.62 (0.52) 9.69 0.002 10.88 Step 2. 5.05 R2= 0.22 (Hosmer & Lemeshow), 0.17 (Cox & Snell), 0.23 (Nagelkerke) Model χ2(2)= 62,136 p<0.001; 66.8% correct classification ODI, Oswestry Disability Index Table 4.4 Validation of developed multivariate logistic regression model for being successful at 1-year follow up (n= 262; 50% remaining cases). Forward selection method Β (SE) Wald p (final model) 95% Confidence Interval for Exp (b) Lower Exp (b) Upper 0.88 0.91 0.93 Included: Step 1. Functional status (ODI) -0.09 (0.01) 50.74 0.00 Constant 3.70 (0.56) 43.20 0.00 Employed 1.84 (0.40) 21.39 0.00 1.96 6.29 13.70 Functional status (ODI) -0.09 (0.01) 36.47 0.00 0.89 0.92 0.94 Constant 2.80 (0.61) 21.23 0.00 40.35 Step 2. R = 0.40 (Hosmer & Lemeshow), 0.25 (Cox & Snell), 0.34 (Nagelkerke) 2 Model χ2(2)= 101,651 p<0.001; 75.0% correct classification ODI, Oswestry Disability Index 16.38 Prediction of successful outcome after a pain management programme 69 Discussion The most important finding of this longitudinal study is that being employed, and the level of disability before treatment are predictive factors for relevant improvement in CLBP patients’ functional status at 1-year follow up. In contrast to our expectation, the pre-treatment degree of experienced pain intensity and belief in one’s ability to manage and to cope with chronic low back pain (CLBP) complaints appeared not to be predictive of outcome. Moreover, the results revealed that 1 year after the programme, highly distressed patients who were referred to the programme, were not at risk of being a failure. Previously, this combined physical and psychological (CPP) programme has been evaluated for patients who met the inclusion criteria [20,21]. The present analysis was conducted to determine whether it would be possible to enhance the efficacy of the programme by further patient selection by identifying a subgroup of patients who could benefit of the programme. As the main goal of the intervention is to improve disability, success at 1-year follow up was defined as having reached 22 points or lower on the ODI. We reasoned that less change is not clinically relevant. A minimal clinical important difference (MCID) of ten points on the ODI has been recommended as a measure for clinical relevancy in CLBP [33]. Although consensus has been reached for this MCID value, the value is still arbitrary because some of the studies upon which the consensus is based contain heterogeneous CLBP-population samples and were derived from primary care [33]. It is difficult to measure what is clinically relevant to patients [34]. Patients who are highly disabled at pre-treatment assessment and who did reach the MCID value after treatment could be classified as improved success whilst in fact they are still disabled. Therefore, we decided to use ODI values seen in ‘normal’ healthy populations as a measure of success. The current study results show that at 1-year follow up 217 patients (41.4%) reached this ODI value. With the exception of one study, which was performed in primary care [35] and included CLBP patients who were who were still at work and who were less disabled (ODI 20 [range 2-52]), we are not aware of any studies performed in secondary or tertiary care investigating factors predicting a functional outcome related to ‘normal’ and healthy populations. Prediction model: pre-treatment ‘employed’ and pre-treatment ‘disability’ Being employed appeared to be the most important predictive factor (OR 3.61 [95%CI 1.807.26]; dichotomized). To a lesser extent, the level of pre-treatment disability predicts the outcome (OR 0.94 [95%CI 0.92-0.97]; decrease per point on ODI). These findings are consistent with the results of the systematic review by Van der Hulst et al. [6]. We recommend screening CLBP patients for these factors. It is known that CLBP patients who are significantly disabled and who are absent from work pre-treatment have a poor outcome [36,37]. The ODI might have screening potential as it has been shown to be of predictive value for chronicity [37]. Patients who are moderately disabled and who are at least partially employed before treatment could be given a higher priority for entry into a CPP programme. From an organisational and economic perspective, patients who are at work and who are mildly disabled might benefit from a shortened programme. To substantiate these ideas more research is needed. 04 70 04 Prediction of successful outcome after a pain management programme In the current study the prediction model (Table 4.3) has wider confidence intervals for the validation model (Table 4.4), and a lower explained variance (R2 22% versus 40% [Hosmer & Lemeshow]), resulting in a greater number of cases correctly classified (67% versus 75%) for the validation model. Because of these discrepancies and to estimate the stability of the prediction model we performed a post-hoc multivariate logistic regression analysis on the random sample (n=252) using a bootstrap procedure that is 500 repeated samples with replacement. All potential prediction variables were then entered in one block. This result is comparable to the final prediction model (Model χ2[5]= 68,157 p<0.001; R2 24% [Hosmer & Lemeshow]; 23% [Cox & Snell]; 31% [Nagelkerke]; 70% correct classified). Based on these results we conclude that the final prediction model, as initially developed, is robust. This model explains 22% (Hosmer & Lemeshow) of the total variance. Moreover, 67% of the patients were correctly classified. Although inconsistent evidence does exist for predictive factors that were identified for outcome of interventions with a physical and cognitive behavioural approach, a comparable and typical low amount of explained variance has been found [38-41]; as well as the percentage correctly classified patients [42]. Because physical and psychosocial factors only marginally contribute to treatment success, other non-specific or moderating factors such as clear treatment rationale, a highly structured programme, providing a pressurecooker model programme, the dose of treatment, skilful staff, and the patient’s readiness to change pain-related behaviour have been proposed as being predictive for a successful outcome [11,43,44]. There are two increasingly suggested specific contributing factors to functional treatment outcome in chronic musculoskeletal pain: expectancy of treatment outcome [45] and central sensitisation [46-48]. Central sensitization includes features of referred pain, hypersensitivity to peripheral stimuli and neuropathic pain, which are felt to represent peripheral manifestations of augmented central pain sensations. However, further research is required to determine which specific factors contribute to a successful outcome for CLBP patients in a CPP programme. Some inconsistent qualitative evidence has been reported which is related to other potential and a priori predictive factors that might be expected for this study: experienced pain intensity [6,49], gender [7,23], or self-efficacy [35,50,51]. However, no support for these predictive factors could be found in the present study. It has also been suggested that improvement of dysfunctional cognitive behavioural factors such as catastrophizing cognitions and fear of movement behaviour might contribute to a successful outcome [11,52]. This suggestion is endorsed by the fear avoidance model (FAM) which postulates a causal relationship between pain catastrophizing, fear of movement, disability and experienced pain severity [4]. Some studies have concluded that the impact of these dysfunctional cognitive behavioural factors on outcome measures as pain as well as functional status are diminished [15,53] or are even absent [6], which is consistent with the results of the present study. Studies investigating the predictive value of psychological distress have only yielded inconclusive and tentative evidence [6,15]. Self-rated depressive mood has been reported to be of prognostic value [8,18,22,39,54]; furthermore, it has been suggested that patients with reported symptoms would benefit less from a multidisciplinary programme compared to patients with no or only mildly depressive symptoms [7,18,23]. In the current study, despite a small association between the level of distress and being successful at 1-year follow up (Pearson’s r –0.23, p<0.001), no predictive value of psychological distress could be found in the final prediction model. This means that CLBP patients who are distressed at pre-treatment assessment might benefit from a CPP programme. Prediction of successful outcome after a pain management programme Strengths and Limitations The strengths of this study are the large sample size (n= 524) and the wide range of available pre-treatment data. This means there was enough statistical power to study the contribution of the different potential predictive factors towards successful treatment outcome over time. Although data were missing on at least one assessment for 67 (13%) patients, no pretreatment differences between non-responders and responders were seen. Our main results are based on the multiple imputation (MI) technique. MI is a technique that depends on model-based imputation of multiple values for each missing observation instead of only one estimate as in single imputation techniques. The major advantage of this method, over single imputation techniques or ‘complete cases only’, is that it does not underestimate variability. Single imputation methods could result in the estimated standard errors being too small, whereas multiple imputation results in the correct magnitude for estimated standard errors and confidence intervals [55,56], i.e. these imputed values reflect the uncertainty in estimation caused by the missing values [56]. Thus, the information contained within the missing data seems similar in nature to the information actually documented. This implies that the conclusions based on the results obtained with MI are robust. Moreover, the large study sample gave us the opportunity to develop a prediction model in a 50% random sample of the original set and to validate and check this final model with the remaining data. Limitations in this study include possible selection bias. Therefore, generalization to common clinical practice is limited as our findings are theoretically relevant only to specialised back care. There are no data for those patients not selected (28%), it is possible that other factors could be predictive for a successful treatment outcome. It is possible that these patients were not ready or motivated to change pain-related behaviour. Although a selection criterion for treatment, we neither assessed this factor in a valid and reproducible way at pre-treatment nor assessed it systematically over time. Further research is needed to assess this factor and to evaluate its contribution to the outcome. Conclusion The study results imply that CLBP patients who are in work and mild to moderately disabled at the start of a CPP programme benefit from it and have a successful treatment outcome. In these patients the disability falls to values seen in healthy populations. Even psychologically highly distressed patients may respond positively to this programme. The limited number of predictive indicators is extremely useful. The small set of easily identified indicators might speed up assigning priority for programme entry and triage to alternative treatment regimes. Acknowledgement The authors thank the multidisciplinary team at RealHealthNL who were responsible for the assessments and treatment of the participants in the CPP programme. Particular thanks to Patricia G. Anderson for her editorial assistance. 71 04 72 Prediction of successful outcome after a pain management programme References 1. van Tulder MW. Health technology assessment (HTA) increasingly important in spine research. Eur.Spine J. 2011;20:999-1000 2. Lambeek LC, van Tulder MW, Swinkels IC, Koppes LL, Anema JR, van Mechelen W. The trend in total cost of back pain in The Netherlands in the period 2002 to 2007. Spine (Phila Pa 1976.) 2011;36:1050-1058 04 3. Frymoyer JW. Predicting disability from low back pain. Clin.Orthop.Relat Res. 1992:101-109 4. Vlaeyen JW, Linton SJ. Fear-avoidance and its consequences in chronic musculoskeletal pain: a state of the art. Pain 2000;85:317-332 5. Truchon M. Determinants of chronic disability related to low back pain: towards an integrative biopsychosocial model. Disabil.Rehabil. 2001;23:758-767 6. van der Hulst M, Vollenbroek-Hutten MM, Ijzerman MJ. A systematic review of sociodemographic, physical, and psychological predictors of multidisciplinary rehabilitation-or, back school treatment outcome in patients with chronic low back pain. Spine (Phila Pa 1976.) 2005;30:813-825 7. Hampel P, Graef T, Krohn-Grimberghe B, Tlach L. Effects of gender and cognitive-behavioral management of depressive symptoms on rehabilitation outcome among inpatient orthopedic patients with chronic low back pain: a 1 year longitudinal study. Eur.Spine J. 2009;18:1867-1880 8. Pincus T, Smeets RJ, Simmonds MJ, Sullivan MJ. The fear avoidance model disentangled: improving the clinical utility of the fear avoidance model. Clin.J.Pain 2010;26:739-746 9. Tlach L, Hampel P. Long-term effects of a cognitive-behavioral training program for the management of depressive symptoms among patients in orthopedic inpatient rehabilitation of chronic low back pain: a 2-year follow-up. Eur.Spine J. 2011;20:2143-2151 10. Severeijns R, Vlaeyen JW, van den Hout MA, Weber WE. Pain catastrophizing predicts pain intensity, disability, and psychological distress independent of the level of physical impairment. Clin.J.Pain 2001;17:165-172 11. Smeets RJ, Vlaeyen JW, Kester AD, Knottnerus JA. Reduction of pain catastrophizing mediates the outcome of both physical and cognitive-behavioral treatment in chronic low back pain. J.Pain 2006;7:261-271 12. Airaksinen O, Brox JI, Cedraschi C, Hildebrandt J, Klaber-Moffett J, Kovacs F et al. Chapter 4. European guidelines for the management of chronic nonspecific low back pain. Eur.Spine J. 2006;15 Suppl 2:S192-S300 13. Chou R, Loeser JD, Owens DK, Rosenquist RW, Atlas SJ, Baisden J et al. Interventional therapies, surgery, and interdisciplinary rehabilitation for low back pain: an evidence-based clinical practice guideline from the American Pain Society. Spine (Phila Pa 1976.) 2009;34:1066-1077 14. National Institute for Health and Clinical Excellence. Low back pain: early management of persistent non-specific low back pain [Report]. NICE Clinical Guideline 88. London, National Collaborating Centre for Primary Care; 2009 15. Henschke N, Ostelo RW, van Tulder MW, Vlaeyen JW, Morley S, Assendelft WJ et al. Behavioural treatment for chronic low-back pain. Cochrane.Database.Syst.Rev. 2010:CD002014 16. Hall H, McIntosh G. Low back pain (chronic). Clin.Evid.(Online.) 2008 17. Wand BM, O'Connell NE. Chronic non-specific low back pain - sub-groups or a single mechanism? BMC. Musculoskelet.Disord. 2008;9:11 18. McCracken LM, Turk DC. Behavioral and cognitive-behavioral treatment for chronic pain: outcome, predictors of outcome, and treatment process. Spine (Phila Pa 1976.) 2002;27:2564-2573 19. Pincus T, Burton AK, Vogel S, Field AP. A systematic review of psychological factors as predictors of chronicity/ disability in prospective cohorts of low back pain. Spine (Phila Pa 1976.) 2002;27:E109-E120 20. van Hooff ML, van der Merwe JD, O'Dowd J, Pavlov PW, Spruit M, de Kleuver M et al. Daily functioning and self-management in patients with chronic low back pain after an intensive cognitive behavioral programme for pain management. Eur.Spine J. 2010;19:1517-1526 Prediction of successful outcome after a pain management programme 73 21. van Hooff ML, Ter Avest W, Horsting PP, O'Dowd J, de Kleuver M, van Lankveld W et al. A short, intensive cognitive behavioral pain management program reduces health-care use in patients with chronic low back pain : Two-year follow-up results of a prospective cohort. Eur.Spine J. 2011 22. Brage S, Sandanger I, Nygard JF. Emotional distress as a predictor for low back disability: a prospective 12year population-based study. Spine (Phila Pa 1976.) 2007;32:269-274 23. Mohr B, Graf T, Forster M, Krohn-Grimberghe B, Kurzeja R, Mantel F et al. [Influence of depressive symptoms and gender in chronic low back pain rehabilitation outcome: a pilot study]. Rehabilitation (Stuttg) 2008;47:284-298 24. Fairbank JC, Couper J, Davies JB, O'Brien JP. The Oswestry low back pain disability questionnaire. Physiotherapy. 1980;66:271-273 25. Fairbank JC, Pynsent PB. The Oswestry Disability Index. Spine (Phila Pa 1976.) 2000;25:2940-2952 26. Jensen MP, Karoly P. Pain-specific beliefs, perceived symptom severity, and adjustment to chronic pain. Clin.J.Pain 1992;8:123-130 27. Nicholas MK, Asghari A, Blyth FM. What do the numbers mean? Normative data in chronic pain measures. Pain 2008;134:158-173 28. Main CJ, Wood PL, Hollis S, Spanswick CC, Waddell G. The Distress and Risk Assessment Method. A simple patient classification to identify distress and evaluate the risk of poor outcome. Spine 1992;17:42-52 29. Nicholas MK. Self-efficacy and chronic pain. Paper presented at: the annual conference of th British Psychological Society. St.Andrews; 1989 30. Sullivan MJ, Bishop SR, Pivik J. The Pain Catastrophizing Scale: Development and validation. Psychological Assessment 1995;7:524-532 31. Kori SH, Miller RP, Todd DD. Kinesiophobia: a new view of chronic pain behavior. Pain Management 1990;3:3543 32. Vlaeyen JW, Kole-Snijders AM, Boeren RG, van Eek H. Fear of movement/(re)injury in chronic low back pain and its relation to behavioral performance. Pain 1995;62:363-372 33. Ostelo RW, Deyo RA, Stratford P, Waddell G, Croft P, Von Korff M et al. Interpreting change scores for pain and functional status in low back pain: towards international consensus regarding minimal important change. Spine 2008;33:90-94 34. Kirwan JR. Minimum clinically important difference: the crock of gold at the end of the rainbow? J.Rheumatol. 2001;28:439-444 35. Rasmussen-Barr E, Campello M, Arvidsson I, Nilsson-Wikmar L, Ang BO. Factors predicting clinical outcome 12 and 36 months after an exercise intervention for recurrent low-back pain. Disabil.Rehabil. 2012;34:136144 36. Hildebrandt J, Pfingsten M, Saur P, Jansen J. Prediction of success from a multidisciplinary treatment program for chronic low back pain. Spine (Phila Pa 1976.) 1997;22:990-1001 37. Nordin M, Skovron ML, Hiebert R, Weiser S, Brisson PM, Campello M et al. Early predictors of delayed return to work in patients with Low Back Pain. Journal of Musculoskeletal Pain 1997;5:5-27 38. Underwood MR, Morton V, Farrin A. Do baseline characteristics predict response to treatment for low back pain? Secondary analysis of the UK BEAM dataset [ISRCTN32683578]. Rheumatology.(Oxford) 2007;46:12971302 39. van der Hulst M, Vollenbroek-Hutten MM, Groothuis-Oudshoorn KG, Hermens HJ. Multidisciplinary rehabilitation treatment of patients with chronic low back pain: a prognostic model for its outcome. Clin.J.Pain 2008;24:421-430 40. Johansson AC, Linton SJ, Rosenblad A, Bergkvist L, Nilsson O. A prospective study of cognitive behavioural factors as predictors of pain, disability and quality of life one year after lumbar disc surgery. Disabil.Rehabil. 2010;32:521-529 04 74 Prediction of successful outcome after a pain management programme 41. Lindell O, Johansson SE, Strender LE. Predictors of stable return-to-work in non-acute, non-specific spinal pain: low total prior sick-listing, high self prediction and young age. A two-year prospective cohort study. BMC.Fam.Pract. 2010;11:53 42. Luk KD, Wan TW, Wong YW, Cheung KM, Chan KY, Cheng AC et al. A multidisciplinary rehabilitation programme for patients with chronic low back pain: a prospective study. J.Orthop.Surg.(Hong.Kong.) 2010;18:131-138 43. Vlaeyen JW, Morley S. Cognitive-behavioral treatments for chronic pain: what works for whom? Clin.J.Pain 2005;21:1-8 04 44. Morley S. Efficacy and effectiveness of cognitive behaviour therapy for chronic pain: Progress and some challenges. Pain 2011;152:S99-106 45. Bingel U, Wanigasekera V, Wiech K, Ni MR, Lee MC, Ploner M et al. The effect of treatment expectation on drug efficacy: imaging the analgesic benefit of the opioid remifentanil. Sci.Transl.Med. 2011;3:70ra14 46. Gwilym SE, Keltner JR, Warnaby CE, Carr AJ, Chizh B, Chessell I et al. Psychophysical and functional imaging evidence supporting the presence of central sensitization in a cohort of osteoarthritis patients. Arthritis Rheum. 2009;61:1226-1234 47. Gwilym SE, Filippini N, Douaud G, Carr AJ, Tracey I. Thalamic atrophy associated with painful osteoarthritis of the hip is reversible after arthroplasty: a longitudinal voxel-based morphometric study. Arthritis Rheum. 2010;62:2930-2940 48. Gwilym SE, Oag HC, Tracey I, Carr AJ. Evidence that central sensitisation is present in patients with shoulder impingement syndrome and influences the outcome after surgery. J.Bone Joint Surg.Br. 2011;93:498-502 49. Bendix AF, Bendix T, Haestrup C. Can it be predicted which patients with chronic low back pain should be offered tertiary rehabilitation in a functional restoration program? A search for demographic, socioeconomic, and physical predictors. Spine (Phila Pa 1976.) 1998;23:1775-1783 50. Borsbo B, Gerdle B, Peolsson M. Impact of the interaction between self-efficacy, symptoms and catastrophising on disability, quality of life and health in with chronic pain patients. Disabil.Rehabil. 2010;32:1387-1396 51. Costa LC, Maher CG, McAuley JH, Hancock MJ, Smeets RJ. Self-efficacy is more important than fear of movement in mediating the relationship between pain and disability in chronic low back pain. Eur.J.Pain 2011;15:213-219 52. Wessels T, van Tulder M, Sigl T, Ewert T, Limm H, Stucki G. What predicts outcome in non-operative treatments of chronic low back pain? A systematic review. Eur.Spine J. 2006;15:1633-1644 53. Schiphorst Preuper HR, Reneman MF, Boonstra AM, Dijkstra PU, Versteegen GJ, Geertzen JH et al. Relationship between psychological factors and performance-based and self-reported disability in chronic low back pain. Eur.Spine J. 2008;17:1448-1456 54. Pfingsten M, Hildebrandt J, Saur P, Franz C, Seeger D. [Multidisciplinary treatment program on chronic low back pain, part 4. Prognosis of treatment outcome and final conclusions]. Schmerz. 1997;11:30-41 55. Donders AR, van der Heijden GJ, Stijnen T, Moons KG. Review: a gentle introduction to imputation of missing values. J.Clin.Epidemiol. 2006;59:1087-1091 56. Twisk J, de Vente W. Attrition in longitudinal studies. How to deal with missing data. J.Clin.Epidemiol. 2002;55:329-337 75 THEME B: Outcomes assessment 76 77 Chapter 05 Evidence and practice of spine registries A systematic review and recommendations for future design of registries van Hooff ML Jacobs WCH Willems PC Wouters WJM de Kleuver M Peul WC Ostelo RWJG Fritzell P Published in: Acta Orthop. 2015;86(5):534-44 Appendix to Chapter 05 - editorial comment (after Chapter 06 - page 130-133) Guest editorial: Spinal disorders, quality-based healthcare and spinal registers Fairbank JCT Published in: Acta Orthop. 2015; 86 (5): 521–522 78 Abstract Purpose: We performed a systematic review and a survey in order to 1) evaluate the evidence for the impact of spine registries on quality of spine care, and with that, on patient-related outcomes, and 2) evaluate the methodology used to organize, analyze, and report the ‘quality of spine care’ from spine registries. Methods: To study the impact, the literature on all spinal disorders was searched. To study methodology, the search was restricted to degenerative spinal disorders. The risk of bias in the included studies was assessed with the Newcastle-Ottawa Scale. Additionally, a survey among registry representatives was performed to acquire information about the methodology and practice of existing registries. Results: 4,273 unique references up to May 2014 were identified and 1,210 were eligible for screening and assessment. No studies on Impact were identified, but 34 studies were identified to study the methodology. Half of these studies (17 of the 34) were judged to have a high risk of bias. The survey identified 25 spine registries, representing 14 countries. The organization of these registries, methods, analytical approaches and dissemination of results are presented. Interpretation: We found a lack of evidence that registries have had an impact on the quality of spine care, regardless of whether the intervention was non-surgical and/or surgical. To improve the quality of evidence published with registry data we present several recommendations. Application of these recommendations could lead to registries showing trends, monitoring the quality of spine care given, and ultimately improving the value of the care delivered to patients with degenerative spinal disorders. Evidence and practice in spine registries 79 Introduction Lumbar spine disorders are a heterogeneous group of conditions with a lack of diagnostic clarity. Thus, in both surgical and non-surgical spinal interventions there are large variations in practice. The increasing frequency of spine-related interventions with increasing costs has led to a shift towards the delivery of value-based spine care [1]. Here, value is expressed as patient-centred outcomes (safety and effectiveness; quality) divided by the related costs of care or per unit cost [2]. While Randomized Clinical Trials (RCTs) are considered the gold standard for assessing the efficacy of interventions, the difficulties with RCTs, specifically for assessing surgical procedures in spinal disorders, are acknowledged [3]. Some barriers are surgeon preferences, patient selection, patients’ reluctance regarding randomization, difficulties in blinding, high cost, the need for long-term follow up, the often high proportions of loss to follow up, and the problem with crossover. Well-designed observational cohort studies, reflecting daily clinical practice, have been reported to produce as trustworthy and externally valid results as RCTs [4-6], [7-10]. An outcome registry is an organized system that uses observational study methods [11], based on STROBE recommendations [12]. Registries could therefore, be used to describe care patterns, including appropriateness of care and disparities in the delivery of care. Registry data could also be used to understand variations in treatment and outcomes and to identify and select subgroups in the heterogeneous chronic low back pain population, with a probability of successful or poor outcome. The ultimate goal of health services registries is to increase value of delivered care (i.e. outcome per unit cost) [13]. Moreover, it has been suggested that measuring with continuous feedback (audit cycles) of outcomes captured in registries raises the awareness and improves quality of care [14]). However, as yet, little is known about the effect of registries on the quality of spine care and the methods for registering and feedback. This study had 2 aims: 1) to evaluate the available evidence for the effect and possible impact of introducing and using spine registries on quality of spine care after any intervention and on patient-related outcomes; 2) to evaluate the methodology used to organize, analyze, and report the ‘quality of spine care’ from spine registries. Methods We performed a systematic review according to the PRISMA statement for reporting in systematic reviews and meta-analyses [15]. In addition, for the second aim (regarding methodology) a survey among spine registry representatives was performed to acquire information about the current status of existing spine registries. The complete protocol of this study was presented at the preconference meeting of the International Society for the Study of the Lumbar Spine 2014 (ISSLS) [16]. Selection of studies and appraisal of their quality was performed independently by MH and WJ. Discrepancies were discussed during consensus meetings, with mediation by a third author (PW) were disagreements persisted. Search A comprehensive search was conducted because terminology in the field of chronic low back pain is not yet standardized, and because we aimed to include both randomized and non- 05 80 Evidence and practice in spine registries randomized studies. The search was performed by one of the authors (WJ) on May 12, 2014, using the most common databases: CBRG trials register (up to search date), MEDLINE (from 1966 to search date), EMBASE (from 1980 to search date), and ISI Web of science (up to search date). The search string for MEDLINE is given in Appendix 1 (See Supplementary data) and was adapted for the additional databases. No language or date restrictions were made. References and citation of selected articles were tracked and included in the search. Impact of registries on quality of spine care Articles were included if they met the following criteria: 05 Types of studies Studies on spine registries based on results from prospectively acquired data were included. To comply with the definition of patient or outcomes registry, we used the following registry characteristics: inclusion principle, mergeable data, standardized dataset for all consecutively included patients, rules for data collection (i.e. systematically and prospectively collected, including pre-intervention data), knowledge about patient-related outcomes, and observations collected over time (i.e. follow-up assessments) [17]. Studies were included if published between 2000 and May 2014 and written in English. Types of spine disorders We included patients of all ages with spine disorders who underwent any elective or acute, non-surgical or surgical spinal intervention. The following specific disorders were defined: degenerative disc disease (DDD, ‘non-specific’ and/or chronic low back pain, segmental pain), spinal stenosis, disc herniation, spondylolisthesis/-lysis (isthmic/degenerative), failed back surgery syndrome, spinal deformities (degenerative deformity: de novo, osteoporotic, idiopathic, neuromuscular, congenital), spinal oncology, spine trauma, and infections of the spine. Types of interventions Interventions were included that provide a system to register quality of spine care, i.e. outcomes, including a system for feedback: quality improvement strategies [18-20]. These strategies include those targeted: health systems (e.g. team changes), healthcare professionals (e.g. reminders), and patients (e.g. reminders). Specific improvement strategies included: (clinical) audit and feedback, (electronic) patient registries, case management, clinician education, (promotion of) self-management, patient reminder systems, and continuous quality improvement. Types of outcome measures Quality of care is a multidimensional concept and is defined in many ways, e.g. ‘doing the right thing, at the right time, in the right way, for the right person, and having the best possible results’ [21]. Following this definition, the outcomes of spine interventions are a proxy for quality of care. As measures for quality of spine care we included patient-related outcome indicators: patient-reported outcome measures (PROMs) and clinical outcome measures. PROMs [22] are functional status (e.g. Roland and Morris Disability Questionnaire [RMDQ], Oswestry Disability Index [ODI], Scoliosis Research Society-22 [SRS22]), pain intensity back and leg (e.g. Visual Analogue Scale [VAS], Numeric Pain Rating Scale [NPRS]), and health-related quality of life (e.g. Short Form-36 [SF36], EuroQol 5 Dimensions [EQ5D]). Clinical outcomes Evidence and practice in spine registries 81 were regarded as re-intervention (i.e. reoperation), complications, and failed back surgery syndrome (FBSS). The search did not reveal any studies related to the first aim of this study (impact) concerning the effect and possible impact of introducing and using spine registries on the quality of spine care and on patient-related outcomes (Figure 5.1). Methodology used in existing spine registries Selection criteria The same criteria were used as for the first aim, but they were restricted to include studies with patients with degenerative lumbar spine disorders: degenerative disc disease (DDD, ‘non-specific’ and/or chronic low back pain, segmental pain), spinal stenosis, disc herniation, spondylolisthesis/-lysis (isthmic/degenerative), and spinal deformities (degenerative deformity: de novo, osteoporotic). Risk of bias assessment The included studies were assessed for methodological quality, to get an impression of the quality of published scientific studies based on registries. Quality was assessed with the Newcastle-Ottawa Scale (NOS; Appendix 2, see Supplementary data) for cohort studies[23]. Studies were considered to be of high quality if the total score was 6 or more (75% of the maximum score). The clinical relevance of study results was assessed with 3 questions: 1) ‘Are the patients described in detail so that you can decide whether they are comparable to those that you see in your practice?’; 2) ‘Are the interventions and treatment settings described well enough so that you can provide the same for your patients?’; 3) ‘Were all clinically relevant outcomes measured and reported?’. Data extraction and management Using forms already developed, the following data were extracted: authors (affiliation, sponsoring), name and type of registry (i.e. based on exposure: health service, disease/ condition and medical devices [11]), setting (nationwide, multicentre), diagnosis, methods (purpose, study design, outcomes, covariates, statistical analysis, patient numbers recruited and included), follow-up response, non-responder analysis, conclusion. MH extracted the data and WJ checked the data; inconsistencies were discussed and PW was consulted if necessary. Survey A web-based survey, built in the Harvard Business online Qualitrics Survey Software and provided by International Consortium for Health Outcomes Measurement (ICHOM), was performed among spine registry representatives. The survey consisted of 21 questions regarding: 1) organizational structure and financing, 2) methodology used and outcome assessment, 3) procedures concerning response rates and missing data, and 4) approaches for analysis and reporting. The sample included participants of the ICHOM Low Back Pain Working Group, representatives of spine registries identified through spine registry websites, and corresponding authors of publications on spine registries as identified in this systematic review. All recipients were contacted by e-mail and asked to participate in an online survey. Subjects who did not respond were sent a reminder after 2 weeks. 05 82 Evidence and practice in spine registries Analyses Results from the included studies in the review were not pooled; instead, we compared and reported on the methods used in these studies. The data from the survey are described and support the results found in the review. The PROMs used were checked with the ICHOM-LBP PROMs criteria [24]: 1) functional status (ODI [0-100] version 2.1a); 2) pain intensity (NPRS [010] back and leg; average pain during last 7 days); 3) health-related quality of life (EQ5D-3L and EQ-VAS); 4) timeline assessments included were baseline, 3 and 6 months, and 1, 2, and 5 years after the ‘index event’ (3 months and 5 years assessments were optional). The ‘index event’ was defined as the reported first intervention episode. 05 Figure 5.1 Flowchart of studies through the different phases of the systematic review. Literature search (n = 5,417): - PubMed, 897 - Embase, 1,247 - Web of Science, 1,637 - Cochrane Central, 1,636 Duplicates n = 1,144 Unique references n = 4,273 Excluded (n = 3,063): - no outcome, 2,429 - language, 129 - conference, 398 - before 2000, 107 Screened and assessed for eligibility by 2 reviewers n = 1,210 Excluded (n = 1,176): - not relevant, 1,138 - indication, 17 - single center, 8 - other methods, 8 - duplicate, 4 - closed register, 1 - no description, 1 Included from survey n=1 Included by both reviewers n = 30 Included by both reviewers n=3 Total included (n = 34) Aim 1: n = 0 Aim 2: n = 34 Evidence and practice in spine registries 83 Results Impact of registries on quality of spine care Included studies 4,273 unique references were identified 1,210 of which were eligible for screening and assessment (Figure 5.1). No studies on the effect of spine registries on quality of care were identified. Methodology used in existing spine registries Included studies 34 studies were identified for study of the methodology used to organize, analyze, and report the quality of spine care in degenerative lumbar spine disorders (Appendix 3, see Supplementary data; Table S1). The 34 studies were based on 11 separate registries, representing 7 countries. Indications included were disc herniation (3), spinal stenosis (9), chronic low back pain (5), adult deformity (5), and spinal disorders (7). In 3 studies a mixture of indications (non-specific subacute and chronic neck, back, and low back pain) was included, but these allowed to extract data of methodology used for separate indications. Risk of bias Half of the studies were classified as having a high methodological quality (17 of 34; Table 5.1). Although ‘selection of the non-exposed cohort’ (item 2) scored high quality (i.e. low risk of bias), 21 studies were rated ‘n.a.’ as these studies did not include a control group. In 20 of the 34 studies, a low risk of bias in ‘comparability’ (item 5a) was seen, meaning studies controlled for the most relevant case-mix variables (i.e. diagnosis and baseline outcome score). In all studies the ‘assessment of outcome’ (item 6) was rated ‘self-report’ (c), and with that scoring low quality. The follow up was long enough for outcomes to occur (item 7; 29 of 34). However, low quality was seen in the adequacy of follow up of cohorts (item 8; 18 of 32). Although in almost all studies the clinical relevant outcomes are measured and reported (31 of 32; ‘clinical relevance’ [item 3]), in less than one-third of the studies (10 of 32) the description of patients and intervention (‘clinical relevance’, items 1 and 2) was sufficiently detailed. Survey We identified 25 spine registries, representing 14 countries, within the ICHOM Low Back Pain Working Group (ICHOM-LBP WG; 10) through the literature (10; 4 overlap with ICHOM-LBP WG) and through internet searches (9). We were unable to make contact with representatives of 7 of the multicentre registries; the remaining 18 were invited to participate in the survey. 16 of them responded, representing 12 countries and 2 including ‘diverse’ countries (Spine Tango and European Spine Study Group database [ESSG]). The non-responders were representatives of Russian and Indian registries. An overview of existing spine registries is presented in Table 5.2: survey responders (16) and searches though internet and literature (6). 3 multi-centre registries in the USA were found through internet searches, but we were unable to obtain relevant information (NASS Spine Registry, SMISS Prospective Data Registry, and Scolisoft Scoliosis Database [see references for websites]); these registries were not included in Table 5.2. 05 84 Evidence and practice in spine registries Organization and Methods used in spine registries Organization (Table 5.2) 9 of 22 registries are organized on a nationwide basis. Most spine registries started within the last decade. All registries are health services registries, except for Kaiser Permanente which is a device or implant registry and SWISSspine, a device or implant registry with health technology assessment purposes. 05 Methods (Table 5.2) All registries incorporated the main patient-reported outcome domains. ODI, NPRS back and leg, and EQ5D are mainly used as PROMs. In the majority of the registries clinical outcomes (e.g. complications and reoperations) are also registered. All registries have baseline and 12- and 24-months follow-up assessments, except for NORspine and N²QOD (with only a 12-months follow up). Although 15 registries report on lumbar spine disorders, only 3 fulfil all the ICHOM-LBP criteria for PROMs. The Adult Deformity Outcomes Database registry has the longest follow up (25 years). To improve the response rate, all registries use postal, e-mail or telephone reminders. Analyses and reporting In the 34 scientific publications, various analytic approaches were used (Appendix 3, see Supplementary data, Table 5.4), varying from descriptive statistics, all studies, to multivariate techniques as mixed linear modelling [25,26] and propensity modelling [27,28]. To evaluate the study purpose all studies used 1 or more PROMs as an outcome measure. In 8 studies secondary clinical outcomes were defined: complications [25,27,29], reoperation [30], BMI [31], adverse events [32], and Bridwell classification for fusion rates [33]. In 5 studies, no adjustment for covariates was performed to explain variation in outcomes [33-37]. In the remaining 29 studies patient characteristics were used as covariates, varying from 17 predefined covariates [38] to adjustment for baseline PROMs only [39]. Adjustment for baseline PROMs was not performed in 8 studies [27,30,32,40-44]. In 8 studies, a dropout analysis was performed to compare baseline characteristics (missing data on assessments) with the remaining cases. To handle missing data, multiple imputation techniques were applied in 2 studies [38,41]. In the remaining studies complete case analysis was performed. All 16 registries representatives reported in the survey, to describe the population and outcomes using descriptive statistics and to provide feedback reports on a regular basis to all participating institutions and spine societies. Benchmarking is performed against the average value of participating institutions in 10 registries (Table 5.2). The 12-months followup responses on PROMs vary from 20% (British Spine Registry) to 88% (Neuroreflexotherapy registry within Spanish National Health Service [NRT en el SNS]). Evidence and practice in spine registries 85 Discussion We found a lack of evidence to support or refute the effect that spine registries may have on the quality of spine care and on patient-related outcomes. Nonetheless, the publications that have resulted from the spine registries have yielded relevant evidence on interventions or predictive factors for spinal disorders. We have therefore described the methodology used to organize, analyze, and report the ‘quality of spine care’ from spine registries. To improve the quality of results published with registry data and to study the effects of spine registries in future; we have formulated and included several recommendations, which are summarized in Table 5.3. First of all, the registries should be methodologically well constructed and we need to learn from existing registries so that a more standardized approach to registering and analysis are achieved to allow international collaboration, national and international benchmarking, and to make sure that in future spine care is value-based (Table 5.3; recommendation [rec.] 1). Quality improvement in spine care Although we did not find any scientific evidence for an effect of introducing and using spine registries on the quality of spine care, in 16 registries feedback reports are compiled and disseminated on a regular basis to the participating institutions and the spine societies in order to improve the quality of spine care delivered. In general, improvement strategies include (clinical) audit and feedback, (electronic) patient registries, case management, clinician education, (promotion of) self-management, patient reminder systems, and continuous quality improvement [18-20] (Table 5.3; rec. 2). That registries can have an important effect on quality of health care has, however, been reported in other fields. For example, the Swedish Hip Register has shown that prospectively and systematically collected data decreased revision rates, by describing trends in outcomes adjusted for case-mix factors and early problems [58]. Antibiotic treatment for patients with hard-to-heal ulcers was reduced from 71% before registration to 29% after registration and feedback [59]. A collaborative cohort study of 5 ICUs in the USA showed that an evidence-based intervention resulted in a large sustained reduction (up to 66%) in the rate of catheter-related bloodstream infection, which was maintained throughout the 18-months study period [60]. A study of 13 patient registries in 5 countries demonstrated that these systems have great potential to both improve health outcomes and lower health care costs[13]. Recently, reports from Sweden indicate that spine registries have a positive effect on health care, i.e. on patient-related outcomes, by the SweSpine registry (complying with the recommendations in Table 3). For example, the national mean length of stay (LoS) for surgery in lumbar disc herniation today is 2 days, with a range of 0-4 days. After introduction of new routines the LoS in 1 university hospital was reduced from 4 to 2.5 days, giving the same patientrelated outcomes at lower costs [61]. Another example is a change of surgical procedure in elderly with lumbar spinal stenosis (LSS). 2-year registry data on 8,785 elderly patients showed that surgery can be limited to an invasive procedure of decompression alone, in order to avoid unnecessary complications associated with fusion procedures [62]. These registry findings have recently been confirmed with a multicentre RCT among 229 elderly patients with 1- or 2-level LSS. After 2 years, no benefit from adding fusion to decompression surgery was found, which means that in this population a less invasive procedure of decompression can reduce the number of complications and costs for society [63]. 05 86 05 Evidence and practice in spine registries Quality of studies based on registry data In the present study, only half of the publications could be regarded as having a low risk of bias, as assessed on the Newcastle-Ottawa Score (NOS). The main weaknesses of the included studies were the inaccurate descriptions of the patients and interventions studied, and the lack of long-term assessment of the outcome. We therefore recommend to use the STROBE guidelines for reporting observational studies [12] (Table 5.3; rec. 3). Moreover, as it is known that during the first year after spine surgery changes in patient-related outcomes are seen [6,27,64,65], a minimum follow-up period of 1 year is recommended (Table 5.3; rec. 4). As stability of patient-reported outcome measures (PROMs) results is seen between 2 and 4 years [6,27] longer follow-up periods are desirable. Another weakness causing high risk of bias according to NOS is that the assessment of outcomes in all studies was performed by PROMs. As PROMs are self-report measures, these measures are assessed as low quality by NOS (Appendix 2, see Supplementary data). Although of lower quality methodologically, PROMs are the recommended outcome measures in spine surgery [22], as there is no valid biomedical measure currently available to evaluate recovery after a spinal intervention. Although they are usually defined as clinical patient-related outcomes, reoperation and complications are in fact process measures for a complicated course to an endpoint defined by PROMs. Methodology of studies based on registries Although all 34 studies met the 6 inclusion criteria for registry characteristics [17] (Table 5.3; rec. 5), we found various analytical approaches. Thus, we cannot give the best approach to use when comparing institutions in the search to identify best practices. In measurement of patientbased outcomes, the PROMs used should ideally fulfill the criteria of good measurement properties [66]. Recently, consensus was reached within the ICHOM collaboration on which PROMs should be recommended for the evaluation of outcomes of interventions for degenerative lumbar spinal disorders (Oswestry Disability Index [ODI], Numeric Pain Rating Scale [NPRS] back and leg, and EuroQol 5 Dimensions [EQ5D]; [24]; Table 5.3; rec. 6). Although as yet only 3 lumbar spine patient registries (SweSpine, Dutch Spine Surgery Registry and Spine Tango) use the specific ICHOM PROMs, every other studied registry already evaluated functional status, pain intensity, and quality of life with PROMs, but the assessment used other tools. To prevent selection bias [67] and to explain real differences in outcomes between institutions multivariate approaches with adjustment for covariates (corrections for differences in characteristics of patients treated in hospitals; ‘case-mix adjustments’) and correction for chance variation (reliability adjustments) are needed [64,68-70] (Table 5.3; rec. 7). A shortcoming of these techniques is that they only account for known covariates. In this systematic review we found that a large variety and number of covariates are included in different registries. A recently published study showed that a large number of patient characteristics (biomedical, psychosocial and health-related indicators) could influence the outcome of interventions of lumbar spinal disorders or maintain the complaints [71]. Within the ICHOM collaboration consensus was reached to use a minimum set of factors: age, sex, education level, work status, duration of sick leave, smoking status, comorbidities, BMI, duration of back/leg pain, morbidity state, diagnosis and indication surgery, need for continuous analgesic use, prior intervention, and baseline patient-reported disability, back and leg pain baseline, and healthrelated quality of life [24] (Table 5.3; rec. 7). Currently, none of the registries collect data of all of these recommended factors. Within the countries influenced by the SweSpine registry format Evidence and practice in spine registries 87 (Sweden, Norway, Denmark, and the Netherlands), consensus has been reached to implement all these factors in the registry, to allow them to be used as covariates in future benchmark analyses. When benchmarking across centers, it has been suggested that together with these patient-related covariates, center-specific characteristics might also influence the outcome of surgery in degenerative lumbar spine disorders [64]. In the registries, the 12-months follow up on PROMs varied from 20% (British Spine Registry) to 88% (NRT en el SNS). A suggested rule of thumb is that a loss to follow up larger than 20% would probably lead to bias in results, whereas a rate of less than 5% would not [72,73]. A recently performed study on Norwegian registry 2-year follow-up data on 633 patients showed that a loss to follow up of 22% would not alter conclusions about the outcome of interventions [74]. Efforts should be made to increase 12- and 24-months follow-up responses to 60-80% (Table 5.3; rec. 8). Although patients are 3 times more likely to respond when invited for follow-up visits [74], it is too demanding for all parties involved to arrange long-term follow-up visits in large patient registries [75]. Solberg et al. [74] found that forgetfulness is the most important reason for not responding, which could possibly be prevented by modern communication techniques as text messages and email (Table 5.3; rec. 9). To handle missing data in most registries only analyses are performed on complete cases. To understand potential sources of bias, a nonresponder analysis on baseline characteristics should be provided (Table 5.3; rec. 10). Statistical sensitivity techniques are available to test whether there is bias present. When missing at random, indicating that the missing data are related to other observed or documented patient data but not to unobserved outcomes, we recommend multiple imputation techniques (Table 3; rec. 11). The major advantage of this method over single imputation techniques or ‘complete cases only’ is that it does not underestimate variability [76,77]. Strengths and Limitations To evaluate the risk of bias in the studies included, as a quality assessment, we defined a cutoff value (total score ≥6 [75%]) for the Newcastle-Ottawa Scale (NOS) for cohort studies. However, research is needed to identify whether this is the correct tool for assessing risk of bias in patient registries. Another limitation is that selection bias might be present in the spine registries identified. We identified 2 types of registries: national and institutional. The national registries are in most cases part of an obligatory, government or insurer driven need for quality control and/or audit. The multicentre institutional registries carry the risk of selection bias (e.g. many institutional registries include premier spine institutes with selected patients) and even more when they are sponsored by industry (ESSG, SWISSspine, Canadian Spine Outcomes, and Research network; Indian Spine Registry) or by membership (SSE Spine Tango, British Spine Registry, National Spine Network Spine Outcomes Registry). Although we performed a profound search and gained an overview of 25 large registries for degenerative spinal disorders we cannot rule out that more spine registries exist. The main strength of this study was that we adopted a systematic approach, including a systematic search and an appraisal of quality. In addition, we conducted a survey among representatives of all the known registries to add information to that found in the literature. To increase the response, we contacted (successfully) all the representatives of the spine registries to complete our data. 05 88 05 Evidence and practice in spine registries Conclusions Currently, despite there being some evidence in other fields of healthcare, there is a lack of evidence to either support or refute the impact that spine registries may have on the quality of spine care and, with that, on patient-related outcomes. To improve the quality of results published from registry data, we have formulated several recommendations. With the first indications of the effects of the SweSpine registry already known (e.g. improved outcomes after feedback on length of stay and no patient-related benefit from adding fusion to decompression surgery), we believe that application of these recommendations could lead to spine registries demonstrating trends and outcomes, monitoring the quality of spine care delivered, resolving controversies in the management of degenerative spinal disorders, and ultimately improving the value of the care given to our patients. Acknowledgements The authors thank members of the ICHOM Low Back Pain Working Group and all participants who completed the spine registry survey. Particular thanks are to Caleb Stowell (ICHOM) for his support in building and managing the survey in the Harvard Business’ Qualitrics online web-application and to Serge Stommels for his support in translating Russian texts in relation to the Russian Spine Registry. Supplementary data Appendices 1-3 are available at Acta’s website (www.actaorthop.org), identification number 8170. NOS Newcastle-Ottawa Scale; n.a. not applicable * score NOS; a high quality; b n items, considering n.a.; c National Outcomes registry; d Community outcomes management study; e Adult Deformities Outcomes Database; f Multicenter registry for lumbar spine surgery; g Singapore General Hospital Spine Outcomes Registry Explanation NOS, including description of items a-d and method of scoring, is given in Appendix 2: Selection: 1 representativeness exposed cohort; 2 selection non-exposed cohort; 3 ascertainment exposure; 4 outcome not present at start study Comparability: 5a and 5b Comparability cohorts on the basis of design/analysis Outcome: 6 outcome assessment; 7 follow up long enough; 8 adequacy follow up Explanation Clinical relevance (yes/no): 1 Are the patients described in detail so that you can decide whether they are comparable to those that you see in your practice? 2 Are the interventions and treatment settings described well enough so that you can provide the same for your patients? 3 Were all clinically relevant outcomes measured and reported? NORspine NORspine NRT en el SNS NRT en el SNS NRT en el SNS NRT en el SNS SweSpine SweSpine SweSpine SweSpine SweSpine SweSpine SweSpine SweSpine SweSpine SweSpine SweSpine SSE Spine Tango SSE Spine Tango SSE Spine Tango SWISSspine SWISSspine SWISSspine SWISSspine N2QOD Nat Outc Reg c Com outc m study d A D O Database e A D O Database e A D O Database e A D O Database e A D O Database e Multicenter reg f Singapore GH Reg g Nerland et al. (2014) [25] Solberg et al. (2013) [39] Corcoll et al. (2006) [34] Kovacs et al. (2012) [41] Kovacs et al. (2007) Royuela et al. (2014) [38] Fritzell et al. 2014 Forsth et al. (2013) [49] Jansson et al. (2005) Jansson et al. (2009) Knutsson et al. (2013) [31] Robinson et al. (2013) [26] Sanden et al. (2011) Sigmundsson et al. (2012) [42] Sigmundsson et al. (2013) [37] Sigmundsson et al. (2014) Stromqvist et al. (2012) [48] Berg et al. (2010) Grob and Mannion (2009) [35] Porchet et al. (2009) Aghayev et al. (2012) Aghayev et al. (2010) Schluessmann et al. (2009) Zweig et al. (2011) [43] McGirt et al. (2013) [40] Deer et al. (2004) [32] Taylor et al. (2000) Bridwell et al. (2007) [30] Glassman et al. (2009) [44] Glassman et al. (2007) Kasliwal et al. (2012) [28] Schwab et al. (2008) [29] Adogwa et al. (2014) [27] Seng et al. (2013) [33] Percentage with *, aor yes Spine registry Study 94 b* a* a* b* b* b* b* b* b* a* a* a* b* a* c c b* a* a* a* a* a* a* a* a* a* a* a* a* b* b* a* a* a* 1 92 a* a* n.a. a* n.a. n.a. n.a. n.a. n.a. a* n.a. n.a. a* b a* n.a. a* n.a. a* n.a. n.a. a* a* n.a. n.a. n.a. a* n.a. n.a. n.a. n.a. n.a. n.a. a* 17 c d d d d d d d c d d d d d d d a* d d d a* d d d d d a* c d d b* b* d a* 3 Selection 2 86 a* a* b a* a* a* a* b a* a* a* a* a* a* a* b a* a* a* a* a* a* a* a* a* a* a* a* b a* a* a* b a* 4 59 no no yes * yes * yes * no no yes * no n.a. yes * yes * yes * yes * no no no no yes * no yes * yes * yes * yes * yes * yes * yes * no yes * yes * yes * no no yes * 5a 71 no yes * yes * yes * yes * yes * yes * yes * no n.a. yes * yes * yes * yes * no no no no yes * no yes * yes * yes * yes * yes * yes * yes * yes * yes * yes * yes * no no yes * 5b Comparability 0 c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c 6 85 a* a* a* a* a* a* a* a* a* a* a* a* a* a* a* a* a* a* a* b a* a* a* a* a* a* a* a* b b b b a* a* 7 Outcome 7 (8) a 5 (7) 5 (7) 5 (7) 6 (8) 6 (8) 6 (7) 2 (7) 6 (8) a 3 (7) 6 (8) a 2 (7) 3 (8) 5 (8) 6 (8) a 5 (7) a 5 (7) a 4 (5) a c c c c c c d d c c b* b* d d c c d n.a. 4 (7) 5 (7) a 6 (8) a 4 (8) 5 (8) 5 (8) 50 d d c d a* 13 a a a a a a a d 4 (7) 4 (7) c d 3 (7) c 3 (7) 4 (7) c 4 (7) 6 (7) b* c 3 (7) c c 2 (7) c a 7 (7) n.a. Total *scoreb 8 no 31 no no yes yes yes no yes no no n.a. yes no no no no no yes no no no yes no no no no no no no yes yes no yes 31 yes no no no no no no no yes n.a. yes yes no no no no yes no no no yes no no no no no no no yes yes yes yes no n.a. 2 3 97 yes yes yes yes yes yes yes no yes n.a. yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes n.a. Clinical relevance n.a. 1 Evidence and practice in spine registries 89 Table 5.1 Risk of bias assessment according to Newcastle-Ottawa scale (NOS) 05 Sweden Norway Denmark The Netherlands UK Spain Switzerland Diverse Canada Singapore Australia USA USA USA Germany Diverse SweSpine NORspine DaneSpine Dutch Spine Surgery Registry British Spine Registry NRT en el SNS SWISSspine SSE Spine Tango Canadian a Singapore b Newro Foundation Texas Back Institute Kaiser c N²QOD d Schön-Clinics Spine Registry European Spine Study Group Survey respondents Location M M/I M M I I N/I N/M M N N N N N N N Setting 2 2010 2010 2012 2009 New 2010 2001 2012 2002 2005 2002 2012 2014 2009 2006 1998 Since (year) D L; C; D; T; I; M L; C; D O, instrumented procedures L; C; D; T; I; M L; C L; C; D; T; I; M L; C; T L; C; D; T; I; M L; C; D; T; I; M L; C L; C; D; T; I; M L & D (mainly); C; T; I; M L; C; D; T; I; M L; D L; C; D; T; I; M Location spine 3 ODI; SRS22r; COMI ODI ODI n.a. ODI ODI; RMDQ ODI; NASS ODI ODI; COMI NASS; COMI RMDQ ODI ODI ODI ODI ODI Functional status 4 NRS B&L VAS B&L NPRS B&L n.a. VAS B&L NRS B&L NPRS B&L VAS B&L COMI: NPRS B&L NPRS B&L NPRS B&L VAS B&L NPRS B&L VAS B&L NPRS B&L VAS B&L Pain 5 SF36 EQ5D EQ5D n.a. SF12, future EQ5D SF12, future EQ5D SF36 SF12; EQ5D EQ5D occasion. EQ5D not meas. EQ5D SF36; EQ5D EQ5D; SF36 occasion. EQ5D SF36; EQ5D Quality of Life 6 Outcomes Lumbar spine B; P; 6w; 6; 12; 24 B; P; 3; 12; 24 B; 3; 12 P B; 3 % B; 6w; 3; 6; 12; 24 B; 1; 3; 6; 24 B; P; 3; 12; 24 B; P; 6w; 3; 12; 24 $ B; P; 3; 6; 12; 24; O, 5y;10y B; P; 3; O, each 3mo # B; 3; 12; 24; O, 5y B; P; 2/6; 12; 24* B; P; 3; 6; 12; 24; O, 5y;10y B; P; 3; 12 B; P; 12; 24; O, 5y PROMs assessments 7 n.a. No No No No No No No Yes No No No Yes No No Yes ICHOM LBP PROMS criteria Benchmark 6 n.a. average & individual centers average only own data only own data only own data only own data average average average only own data average & individual centers (planned) average & individual centers (planned) average average average & individual centers 05 Registry name 1 Yes Yes Yes; 1 Yes No No Yes; 1 No Yes; 3 Yes; 4 Yes; 4 No No No Yes; 2 Yes; 12 Scientific publication 8 4 10 5 3.3 (2-5) 5.3 (5-6) 10 4.0 (2-6) 4.5 (2-7) 5.3 (2-7) 10 NOS Risk of Bias score 9 90 Evidence and practice in spine registries Table 5.2 Registry characteristics India USA USA & Canada USA Spine Deformity Study Group USA International Spine Study Group Indian Spine (Surgery) Registry 12 National Spine Network e; 12 Multicenter registry f; 13 ADO Database g;14 ATSD Database h;15 M M M M M; future M 2010 2002 2003 1995 Plan. 2012 D D L L; C L L SRS-22; ODI SRS-22; ODI ODI ODI Not found ODI SRS-22 (pain) SRS-22 (pain) VAS B&L Not found Not found VAS SF36 SF12 Not found SF36 or SF12 or SF8 Not found SF36 B; 12; 24 B; 6; 12; 24; 5y; 10y; 15y; 20y; 25y B; P; 12; 24 B; 12 optional: 3; 6; 24 Not found B; P; not reported n.a. n.a. No Not found Not found No Yes Not found Yes; 1 Yes; 1 Yes; 5 Yes Not found Not found Not found Not found Not found Not found 4.6 (4-6) 5 3 n.a. not applicable 1 Registry names: a Canadian Spine Outcomes and Research Network; b Singapore General Hospital Spine Outcomes Registry; c Kaiser Permanente Spine Implant Registry; d National Neurosurgery Quality and Outcomes Database (N²QOD); e National Spine Network Spine Outcomes Registry (SpineChart); f Multicenter registry for lumbar spine surgery; g Adult Deformity Outcomes Database; h Adult Thoracolumbar Spinal Deformity Database 2 Setting: N National, M Multicenter, I Institutional 3 Location: L Lumbar spine; C Cervical spine; D Spinal Deformity; T Spine Trauma; I Spinal Infections; M Spinal metastases; O Other, … 4 Functional status: ODI Oswestry Disability Index; RMDQ Roland and Morris Disability Questionnaire; NASS North American Spine Society lumbar spine outcome scale; COMI Core Outcome Measures Index; SRS22 Scoliosis Research Society 22 questions 5 Pain: VAS Visual Analogue Scale; NPRS Numeric Pain Rating Scale; 6 Quality of Life: SF8, SF12, SF36 Short Form 8 or 12 or 36 questions; EQ5D EuroQol 5 Dimensions (including EuroQol VAS) 7 PROMs at: B Baseline; P Peri-operative; 6w 6 weeks; 1 1 month; 3 3 months; 6 6 months; 12 12 months; 24 24 months; O Other, …; * 2 months in hernia/stenosis; # until discharge; $ at least 1 follow up; % variabel: when patient returns to clinic 8 n; according to Appendix 2; Table 1 9 NOS Risk of Bias score Newcastle-Ottawa scale – total score; median (range) according to Table 1 10 high quality 11 Shevelev et al. [79]; 12 See references for websites 13 Adogwa et al. [27]; 14 e.g. Kasliwal et al. [28] (see Table 1); 15 Scheer et al. [78] Russia Russian Spine Registry 11; 12 Other sources Evidence and practice in spine registries 91 05 92 Evidence and practice in spine registries Table 5.3 Recommendations to improve the quality of study results published from registry data Organization and Method 05 1. Use a standardized approach to registering in design, methodology, and analysis to allow international collaboration, to achieve benchmark purposes, and to make sure that in future spine care is value based. 2. Study and incorporate strategies to improve quality of care, e.g. continuous feedback and audit cycles of results collected in spine registries of delivered spine care. 3. To increase the quality of registry studies the population needs to be well defined, in terms of diagnosis and indication for surgery. Both in the developmental stage of a registry and when reporting on registry data follow the STROBE guidelines. 4. Include a minimum follow-up period of 1 year for surgically treated patients. 5. To meet the definition of a patient registry all registry characteristics should be present ([17]. This means an inclusion principle, mergeable data, standardized dataset for all consecutively included patients, rules for data collection [i.e. systematically and prospectively collected, including pre-intervention data], knowledge about patient-related outcomes, and observations collected over time [i.e. follow-up assessments]) Patient-related outcomes 6. Patient-reported outcome measures for degenerative lumbar spine disorders are PROMs with good measurement properties and as recommend by ICHOM. Although often defined as clinical patient-related outcomes (i.e. re-operation, complications, and Failed Back Surgery Syndrome), these indicators are in fact process measures for complicated course. Analyses and Report 7. To explain differences in outcomes with advanced multivariate analytical techniques, include a reliability adjustment and an adjustment for covariates. For degenerative lumbar spine disorders the recommended factors in ICHOM could be used as covariates. 8. To reduce bias in results a 60-80% 12-months follow-up response is recommended. 9. To increase PROMs response at follow up reminders by text messages or email could be sent. 10. To understand potential sources of bias a non-responder analysis on baseline characteristics should be provided, including a quantitative sensitivity analysis in order to evaluate to which extent the results are affected by bias. 11. Multiple imputation techniques are recommended for sensitivity analysis when missing data are randomly divided. Practical issues a a 12. Linkage between electronic medical records and registry data to avoid double data entry and to enhance routine in daily practice. 13. Participating departments should have direct access to their own data and should have real-time comparisons with other departments and if available, with the national mean. 14. After approval, analyzed results corrected for case mix should be presented for public on open web pages in order to increase credibility and to make adequate and relevant comparisons. not discussed in this study Evidence and practice in spine registries 93 Appendix 1: Search strategies MEDLINE - Pubmed (“Lumbar vertebrae”[MH] OR “Spine”[mesh] OR “intervertebral disk”[mesh] OR “intervertebral disc”[tiab] OR spine[tiab] OR spinal[tiab] OR vertebra*[tiab] OR disc[tiab] OR discs[tiab] OR disk[tiab] OR disks[tiab]) AND (“low back pain”[tiab] OR “back pain”[mesh] OR “back pain”[tiab] OR “intermittent neurogenic claudication”[tiab] OR “intermittent claudication”[mesh] OR “intermittent claudication”[tiab] OR ”neurogenic claudication”[tiab] OR dorsalgia[tiab] OR backache[tiab] OR lumbago[tiab] OR (lumbar[tiab] AND (“pain”[mesh] OR “pain”[all fields])) OR “sciatica”[mesh] OR “sciatica”[all fields] OR sciatica[tiab] OR “Spondylolisthesis”[mesh] OR “spondylolisthesis”[tiab] OR “isthmic”[tiab] OR “lytic”[tiab] OR “low-grade”[tiab] OR “lumbar stenosis”[ tiab] OR “spinal stenosis”[mesh] OR “spinal stenosis”[TIAB] OR “stenosis” [TIAB] OR “scoliosis”[TIAB] OR “deformity”[ti] OR "Scoliosis"[Mesh] OR “degenerative disc disease”[tiab] OR “spinal diseases”[mesh] OR “intervertebral disk displacement”[mesh] OR “intervertebral disk displacement”[ tiab] OR “discitis”[mesh] OR “discitis”[all fields] OR “spondylosis”[tiab] OR ((disc[tiab] OR discs[tiab] OR disk[tiab] OR disks[tiab]) AND degeneration[tiab]) OR herniated[tiab] OR hernia[tiab] OR “Failed back surgery syndrome” [TIAB] OR “FBSS” [TIAB] OR “myelomeningocele”[TIAB] OR “Ankylosing spondylitis” [TIAB] OR “tumours” [TIAB] OR “metastases” [TIAB] OR “Trauma” [TIAB] OR “Fracture” [TIAB]) AND ("Clinical Audit"[Mesh] OR "audit"[TIAB] OR "Quality Assurance, Health Care"[Mesh] "Outcome and Process Assessment (Health Care)"[Mesh] "Quality Improvement"[Mesh] "Benchmarking"[Mesh] OR "Benchmarking"[TIAB] OR "Register"[TIAB] OR "Registry"[TIAB] OR "Registries"[Mesh]) Within Reference manager on all fields (indexed and non-indexed) ("Outcome and Process Assessment (Health Care)"[Mesh] OR "Quality of Life"[Mesh] OR “Functional status”[TIAB] OR “disability”[tiab] OR “Patient reported outcome” [tiab] OR "PROMS"[tiab]) 05 94 Evidence and practice in spine registries Appendix 2: NEWCASTLE - OTTAWA QUALITY ASSESSMENT SCALE for COHORT STUDIES Note: A study can be awarded a maximum of one star (*) for each numbered item within the Selection and Outcome categories. A maximum of two stars can be given for Comparability. 05 Selection 1) Representativeness of the exposed cohort (dependent on the diagnostic group) a) truly representative of the average case in the community * b) somewhat representative of the average case in the community * c) selected group of users eg nurses, volunteers d) no description of the derivation of the cohort 2) Selection of the non-exposed cohort a) drawn from the same community as the exposed cohort * b) drawn from a different source c) no description of the derivation of the non-exposed cohort 3) Ascertainment of exposure a) secure record (eg surgical records) * b) structured interview * c) written self-report d) no description 4) Demonstration that outcome of interest was not present at start of study a) yes * b) no Comparability 5) Comparability of cohorts on the basis of the design or analysis a) study controls for the most relevant case mix variables (1. Diagnosis and 2. Baseline outcome score) * b) study controls for any additional factor * Outcome 6) Assessment of outcome a) independent blind assessment * b) record linkage * c) self-report d) no description 7) Was follow-up long enough for outcomes to occur a) yes (select an adequate follow up period for outcome of interest) * b) no 8) Adequacy of follow up of cohorts Evidence and practice in spine registries 95 a) complete follow up - all subjects accounted for * b) subjects lost to follow up unlikely to introduce bias - small number lost → 20%; 80% response * c) follow up rate < 80% (select an adequate %) and no description of those lost d) no statement Total score (n stars) 05 Kovacs et al., 2012 [41] Corcoll et al., 2006 [34] Solberg et al., 2013 [39] NORspine Norwegian Registry for Spine Surgery Nerland et al., 2014 [25] Spain NRT en el SNS Registry within Spanish National Health Service Spain - Balearic Islands NRT en el SNS Registry within Spanish National Health Service Norway NORspine Norwegian Registry for Spine Surgery Norway Name Registry To explore the feasibility of implementing a registry in routine practice. To develop predictive models to quantify the likelihood that a given patient experiences a clinical relevant improvement To describe the implementation of Neuroreflexotherapy and the audit results To estimate cut off values for success To study the equivalence of changes in functional outcomes Study purpose Acute and chronic Low Back Pain with or without leg pain Seeking care Non-specific subacute and chronic neck, back, and low back pain. Lumbar disc herniation Lumbar spinal stenosis Indication PROMs: RMDQ VAS (10cm) Back pain intensity VAS (10cm) Leg pain intensity Secondary PROMs: VAS pain intensity of local and referred pain RMDQ Rates of referral and appropriate referral Patient satisfaction Physician satisfaction Secondary PROM: Global perceived effect (7-point Likert scale) PROMs: ODI v.1 NPRS (0-10) Back pain intensity NPRS (0-10) Leg pain intensity EQ5D Clinical: perioperative complications duration of surgical procedures length of hospital stay Secondary PROMs: EQ5D PROM: ODI v.2.0 Outcomes 1 Independent variables in model: Gender Age Duration of current pain episode (acute, subacute, chronic) Employment status Education Recruitment setting Previous back surgery Current episode because of Failed Back Surgery Syndrome Diagnostic procedures/tests current episode Concomitant treatments Not reported Adjustment for baseline scores: ODI v.1 NPRS Back pain intensity NPRS Leg pain intensity EQ5D Adjustment for: Numbers of levels operated (one or two) Age BMI Baseline ODI value Covariate adjustment 05 Author, year Descriptive statistics Improvement based on Minimal Clinical Important Change Multivariate logistic regression models using backward strategy Methods Missing data: Multiple imputation analysis (n= 5 imputed datasets) Descriptive statistics Descriptive statistics Treatment effect: Paired Students t-tests Subgroup comparison: oneway ANOVA Relationship Global Perceived change and change scores: Spearman rank correlation coefficient Cut off values for success: ROC analyses and AUC Methods Missing data: Complete cases analysis Descriptive statistics Mixed linear models Statistics 96 Evidence and practice in spine registries Appendix 3: Table S1. Study characteristics Fritzell et al., 2014 [57] Royuela et al., 2013 [38] Kovacs et al., 2007 [48] Sweden SweSpine Swedish National Spine Register Spain NRT en el SNS Registry within Spanish National Health Service Spain - Balearic Islands NRT en el SNS Registry within Spanish National Health Service To compare PROMs between primary LDH and recurrent LDH. To determine risk factors for worse outcomes. To assess the feasibility of using a registry in routine practice. To develop models predicting the probability of improvement To identify prognostic factors for clinical outcome. Lumbar disc herniation Non-specific subacute and chronic neck, back, and low back pain. Non-specific subacute and chronic neck, back, and low back pain. PROMs: VAS (10cm) leg pain intensity VAS (10cm) back pain intensity ODI EQ5D Satisfaction and Global assessment of change in leg pain PROMs: RMDQ for LBP / NDI for neck pain VAS (10cm) local pain intensity VAS (10cm) referred pain intensity PROMs: RMDQ VAS (10cm) local pain intensity VAS (10cm) referred pain intensity Descriptive statistics Improvement based on Minimal Clinical Important Change Multivariate logistic regression models Nomograms to illustrate results of models Methods Missing data: Multiple imputation analysis (n= 10 imputed datasets) Descriptive statistics Comparison baseline characteristics between groups: independent Students’ t-test continuous variables, Chi-square test ordinal data. In outcome calculation ANCOVA was used. Multivariate logistic regression analysis Independent vars in model: Reason for referral (neck, back LBP) Gender Age Baseline PROMs Number of days with implanted surgical staples Duration of current pain episode (classified) Duration since first diagnosis (classified) Employment status Type of pain Diagnosis of fybromyalgia Other comorbidities Involvement in employment claims Involvement in litigation Diagnostic tests before Neuroreflexotherapy (NRT) Imaging findings History of spine surgery Treatments before NRT Adjustments for: Age Gender Smoking Baseline value of analysed PROM Descriptive statistics Multivariate logistic regression models using backward strategy Independent variables in model: Reason for referral (neck, back) Gender Age Baseline PROMs Number of days with implanted surgical staples Duration of current pain episode (classified) Duration since first diagnosis (classified) Failed previous surgery for current episode Evidence and practice in spine registries 97 05 Knutsson et al., 2013 [31] Jansson et al., 2009 [50] Jansson et al., 2005 [49] Sweden SweSpine Swedish National Spine Register Sweden SweSpine Swedish National Spine Register Sweden SweSpine Swedish National Spine Register Sweden SweSpine Swedish National Spine Register To determine the association between BMI and outcome of lumbar spine surgery. To report HRQoL outcome in an Lumbar Spinal Stenosis cohort To compare the findings with the Swedish population. To report the health-related quality of life outcome in Lumbar Disc herniation. To compare the results with the Swedish population. To compare satisfaction after decompression alone and following decompression and fusion. Lumbar spinal stenosis Lumbar spinal stenosis Lumbar disc herniation Age: ≥ 50 years Lumbar spinal stenosis in one or two levels, with and without pre-operative spondylolisthesis PROMs: VAS (10cm) leg pain intensity VAS (10cm) back pain intensity ODI EQ5D Satisfaction 3-point Likert scale Clinical outcome: Height & weight (BMI) PROM EQ5D score and EQ-VAS PROM: EQ5D PROMs: VAS (10cm) leg pain intensity VAS (10cm) back pain intensity ODI EQ5D Satisfaction and Global assessment of change in leg pain Adjustment for: Age Gender Smoking Use of analgesics Previous back surgery Duration od symptoms PROMs: Baseline values Covariates: Age Gender Smoking status Type of surgery Duration of back and leg pain Pre-operative walking distance PROM: baseline EQ5D Covariates: Age Gender Smoking status Type of surgery Duration of back and leg pain PROM: baseline VAS leg pain Pre-operative walking distance PROM: baseline EQ5D Baseline value of analysed PROM A frailty component was included to handle withinhospitals dependencies in patient selection and surgical technique Adjustments for: Age (continuous) Gender Smoking Duration of symptoms Previous spinal surgery Baseline analgesic use 05 Forsth et al., 2013 [62] Descriptive statistics General linear models (GLM). Restricted cubic-spline logistic regression analysis Descriptive statistics MANOVA, adjusted for covariates. Descriptive statistics MANOVA, adjusted for covariates. Descriptive statistics Adjusted means were estimated using Students’ t-tests. Multivariate logistic regression analyses 98 Evidence and practice in spine registries Sigmundsson et al., 2013 [37] Sigmundsson et al., 2012 [42] Sanden et al., 2011 [55] Robinson et al., 2013 [26] Sweden SweSpine Swedish National Spine Register Sweden SweSpine Swedish National Spine Register Sweden SweSpine Swedish National Spine Register Sweden SweSpine Swedish National Spine Register To determine how different constellations of back and leg pain influence preoperative health related quality of life. To determine predictive factors of surgical outcome. To determine the relation between smoking status and disability after surgical treatment. To compare the 2-year results of 3 methods of lumbar fusion (UIF, IPF, and TLIF/PLIF). Lumbar spinal stenosis Lumbar spinal stenosis Lumbar spinal stenosis Degenerative disc disease PROMs: VAS (10cm) leg pain intensity VAS (10cm) back pain intensity ODI Walking distance (categorized) SF36 EQ5D PROMs: VAS (10cm) leg pain intensity VAS (10cm) back pain intensity ODI Walking distance (categorized) EQ5D PROMs: VAS (10cm) leg pain intensity VAS (10cm) back pain intensity ODI Walking distance (categorized) SF36 EQ5D PROMs: VAS (10cm) leg pain intensity VAS (10cm) back pain intensity ODI EQ5D Descriptive statistics General Linear Models (GLM), adjusted means Multivariate logistic regression Descriptive statistics Paired Students’ t-test; Mann Whitney U, Kruskall Wallis) Multivariate regression analysis MANOVA for variation in PROMs. Descriptive statistics Parametric tests: Satterthwait t-test Non-parametric tests: Mann-Whitney test, test for trend (Chi-square) Controlled for: Age Duration of back and leg pain MRI: Multilevel stenosis and spondylolisthesis MRI: Central dural sac area ? PROMs: baseline values Walking distance, Back and Leg pain Not reported Descriptive statistics PROC MIXED and KenwardRoger method, adjusted means Modified Poisson regression approach Adjustment for: Age Gender Smoking Use of analgesics PROMs: Baseline value under study Year of surgery: as 2 of the methods were unevenly distributed over study period. In many hospitals 1 surgical method predominated. Adjustment for: Age Gender Smoking Use of analgesics Previous back surgery Duration od symptoms PROMs: Baseline value understudy Evidence and practice in spine registries 99 05 Berg et al., 2010 [47] Stromqvist et al., 2012 [61] Sweden SweSpine Swedish National Spine Register Sweden SweSpine Swedish National Spine Register Sweden SweSpine Swedish National Spine Register To determine whether a registry can provide the same information as an RCT. To elucidate the incidence of dural lesions in decompressive surgery, to identify risk factors and effect on postoperative outcome. To evaluate outcome of surgery and to explore the role of spinal fusion in predominant back pain and predominant leg pain. CLBP - Degenerative disc disease Lumbar spinal stenosis Lumbar spinal stenosis Complications Reoperations Work status Medication Satisfaction with operation (categorized) Secondary PROMs: VAS (10cm) leg pain intensity VAS (10cm) back pain intensity ODI Walking distance (categorized) SF36 EQ5D Global rating scale for improvement of back and leg pain PROMs: VAS (10cm) leg pain intensity VAS (10cm) back pain intensity ODI SF36 EQ5D Satisfaction with operation (categorized) Not reported Risk factors: Age Gender Smoking Work Consumption of analgesics Walking distance Clinical risk factors: Dural lesion Number of levels decompressed Baseline PROM score Satisfaction with operation (categorized) Clinical: Dural lesion Adjustments for: Age Gender Duration of Leg and Back Pain Comorbidity Smoking PROMs: VAS (10cm) leg pain intensity VAS (10cm) back pain intensity ODI Walking distance (categorized) SF36 EQ5D 05 Sigmundsson et al., 2014 [56] Descriptive statistics Two-tailed Mann-Whitney U; Wilcoxon rank sum tests Students’ t test; Spearman r, Fisher exact; Chi-square tests Descriptive statistics Logistic regression analysis Descriptive statistics Linear regression analysis. Cox proportional hazard model (Robust) Outcomes compared with unadjusted nonparametric tests, test for trend (Chi square), Mann Whitney U test. 100 Evidence and practice in spine registries Aghayev et al. 2010 [45] Aghayev et al. 2012 [46] Porchet et al. 2009 [51] Grob and Mannion, 2009 [35] Switzerland SWISSspine SWISSspine Registry Switzerland SSE Spine Tango Surgery Registry SWISSspine SWISSspine Registry Switzerland SSE Spine Tango Surgery Registry Switzerland SSE Spine Tango Surgery Registry To evaluate the outcomes of all single-level Dynardi TDAs compared with all other prostheses in the SWISSspine data pool. To compare back and leg pain alleviation after total disc arthroplasty and ALIF stratified by implant and surgeon from the SWISS-spine and Spine Tango registries. To compare outcome after lumbar disc excision with and without the use of the microscope. To investigate the occurrence of post-surgical complications form the patient’s perspective. CLBP - Degenerative disc disease CLBP - Degenerative disc disease Lumbar / lumbosacral degenerative disorders Spine surgery for different pathologies of the cervical and lumbar spine PROM: NASS, used - VAS (10 cm) Back pain intensity - VAS (10 cm) Leg pain intensity EQ5D PROM: NASS, used - VAS (10 cm) Back pain intensity - VAS (10 cm) Leg pain intensity EQ5D Secondary PROMs: Satisfaction intervention (5-point Likert scale) Global perceived effect (5-point Likert scale) Patient-rated complications (dichotomous) PROM: COMI, incl. - NPRS (0-10) Back pain intensity - NPRS (0-10) Leg pain intensity Secondary PROMs: Satisfaction intervention (5-point Likert scale) Global perceived effect (5-point Likert scale) Patient-rated complications (dichotomous) PROM: COMI Occurrence / nature of postop complications Covariates: Device used Gender Age (categorized) Surgical volume of center of intervention Pharmacologically treated depression Preoperative PROM scores (pain and EQ5D; categorized) Baseline PROM score Covariates: Implant Surgeon Depression Age Gender Follow-up interval Length of stay (LoS) Descriptive statistics Wilcoxon rank-sum test Chi-square tests Multiple logistic regression models; backward elimination Descriptive statistics First step: univariate logistic regression Second step: generalized linear model (GLM), adjusted Descriptive statistics Unpaired Students t tests Contingency analyses (Chisquare tests) Gender Age categories (<60; >60) Health insurance (Private; Basic obligatory) Comorbidity (ASA score) Baseline PROM score (COMI, NPRS back and leg) Descriptive statistics To evaluate group differences: Chi-square tests for proportion differences Not reported Evidence and practice in spine registries 101 05 Deer et al. 2004 [32] McGirt et al. 2013 [40] SWISSspine SWISSspine Registry Zweig et al. 2011 [43] USA National Outcomes Registry for LBP USA N2QOD National Neurosurgery Quality and Outcomes Database Switzerland SWISSspine SWISSspine Registry To obtain data on patient demographics, clinical practices, and long-term outcomes for patients with CLBP treated with implantable drug-delivery systems. To provide an overview of the aims, registry design and methods of the N2QOD pilot year lumbar module. To prove that preoperative nucleus pulposis status and presence or absence of radiculopathy has an influence on clinical outcomes in patients with mono-segmental lumbar total disc replacement. To report the methodology and implementation of the SWISSspine registry and early results of the cases with TDA. CLBP - intrathecal Drug Delivery (IDD) Lumbar spinal disorders: - symptomatic lumbar disc herniation - symptomatic recurrent lumbar disc herniation - lumbar stenosis - lumbar adjacent segment disease Not reported mono-segmental TDR surgery for Degenerative disc disease Hernia nucleus pulposis no radiculopathy, Stenosis, Hernia nucleus pulposis radiculopathy Not reported Lumbar Total Disc Arthroplasty (TDA) PROMs: NPRS (0-10) back and leg pain ODI v1.0 Secondary Return to work Satisfaction (with IDD, recommend IDD to others, and quality of life) Adverse events PROMs: Patient satisfaction NPRS (1-10) back and leg pain ODI EQ5D Occupational outcome (return to work, capacity) Perioperative measures: Blood loss Length of Stay Need for inpatient rehabilitation or skilled nursing 90-day morbidity readmission reoperation PROM: NASS, used - VAS (10 cm) Back pain intensity - VAS (10 cm) Leg pain intensity EQ5D PROM: NASS, used - VAS (10 cm) Back pain intensity - VAS (10 cm) Leg pain intensity EQ5D Patient characteristics: age, gender, underlying cause of pain, type of pain, previous pain treatments, use of systemic opioids, work status, trialling site, trial duration, type of medical insurer, previous psychological evaluations, implant location, type of system. Patient characteristics and demographic factors for risk-adjustment Adjustement for covariates: Gender Age Preoperative pain medication Intervertebral level of intervention Pharmacologically treated depression Type of work Working activity level Covariates: Prosthesis used Gender Age Surgical volume of center of intervention (categorized) Pharmacologically treated depression Preoperative PROM scores (pain and EQ5D) 05 Schluessmann et al. 2009 [54] Descriptive statistics. Chi-square tests and Paired t-tests treatment effect. Repeated measures ANOVA outcomes over time . Descriptive statistics Risk-adjusted models Complete cases analyses Descriptive statistics Univariate logistic regression or ANOVA General linear modelling (GLM). Bonferroni-Holm adjustments for multiple testing . Descriptive statistics Wilcoxon rank-sum test Chi-square test Multiple logistic regression models, backward elimination 102 Evidence and practice in spine registries Kasliwal et al. 2012 [28] Glassman et al. 2007 [52] Glassman et al. 2009 [44] Bridwell et al. 2007 [30] Taylor et al. 2000 [53] USA Adult Deformity Outcomes Database USA Adult Deformity Outcomes Database USA Adult Deformity Outcomes Database USA Adult Deformity Outcomes Database USA; community-based Community outcomes management study To assess differences in surgical parameters (e.g. Surgical time, blood loss), complication rates, and outcomes in adults undergoing spinal deformity correction who either did or did not have a history of a shortsegment spinal procedure. To determine whether perioperative complications alter clinical outcomes. To examine outcomes after adult deformity surgery. Do 1-year outcomes predict 2-year outcomes? To prospectively analyse the responsiveness of the SRS-22 to change 1 and 2-years following primary surgery. To examine factors associated with favourable self-reported outcomes 1 year after elective surgery. Adult deformity Adult deformity Adult deformity Adult deformity Lumbar spinal disorders: - degenerative changes - herniated disc - instability (incl. Spondylolisthesis) - spinal stenosis Clinical: Complications PROMs: SRS-22 ODI v1 NPRS (0-10) for back and leg pain SF12 (physical and mental component scores) PROMs: SRS-22 ODI NPRS (0-10) for back and leg pain SF12 (physical and mental component scores) Clinical: Complications PROMs: SRS-22 ODI NPRS (0-10) for back and leg pain SF12 (physical and mental component scores) PROMs: SRS-22 ODI SF12 Patient-reported questions: - back surgery changed quality of life - functioning better/worse than before surgery - rate of overall treatment of back problem - bothersomeness back pain - bothersomeness leg pain - interference of physical health in activities - most strenuous level of physical activity Descriptive statistics Descriptive statistics Matched-pairs sample t-test statistics and posthoc ANOVA for treatment effect and differences in subgroups Descriptive statistics Propensity modelling. Analysis: 1-way and repeated measures ANOVA. Descriptive statistics Propensity modelling Wilcoxon signed-rank or Fisher exact tests used to compare outcomes. Subgroup analyses based on: Diagnosis Curve type Three defined complication cohorts were matched: Age (categorized) Diagnosis Baseline PROM SRS-22 total score Distal fusion level Sagittal balance at 1-year post-operation Patients with/without prior surgery were matched: Age (categorized) Baseline PROM ODI score Cobb angle Sagittal Vertical Axis (SVA) Descriptive statistics Chi-square test, Fisher exact test differences characteristics Unconditional logistic regression techniques used Multivariate logistic regression models Age (categorized) Curve type (major curve location) Baseline scores on patientreported questions Age Gender Smoking habits Duration of symptoms Clinical signs Diagnosis Surgical procedure Previous surgery Work status Workers’ compensation Seeing attorney Evidence and practice in spine registries 103 05 Singapore Singapore General Hospital Spine Outcomes Registry USA & Canada Multicenter registry for lumbar spine surgery To compare midterm clinical and radiological outcomes of minimal invasive surgery (MIS) versus open transforaminal lumbar interbody fusion (TLIF). To assess the effect of incidental durotomies on the immediate postoperative complications. To investigate the patientreported outcomes at longer-term follow up following lumbar fusion. To determine if models for predicting outcome and complications can be constructed. Lumbar spinal disorders Lumbar spinal disorders: - Degenerative disc disease - Grade spondylolisthesis with central of foraminal stenosis Adult deformity Clinical: Bridwell classification PROMs: ODI Neurogenic Symptom Score (NSS) SF36 VAS Back pain VAS Leg pain Clinical: Postoperative complications PROMs: VAS Back pain VAS Leg pain ODI Clinical: Complications PROMs: SRS-22 ODI v1 NPRS (0-10) for back and leg pain SF12 (physical and mental component scores) Not reported Patients with durotomy and controls were matched using propensity modelling (1:2) `based on: Age Gender Comorbidities Other relevant surgical factors Baseline PROM scores Diagnosis Classification type (type and modifiers) Surgical procedure Age Gender BMI Previous surgery (revision state) Pearson Chi-square, Students’ t-test to compare differences in characteristics. ANOVA to evaluate differences in PROMs. Independent Students’ t-test to compare differences between groups. Descriptive statistics Student t-test, MannWhitney U test and Chisquare tests. Propensity modelling to stratify risk. Descriptive statistics Two approaches were used to determine factors predicting successful surgical outcome: 1. Binary logistic regression models were built to examine how factors combine and interact. 2. Multiple linear regression analyses using backward and stepwise techniques were used to eliminate redundant predictive factors. Subsequently, binary logistic regression to predict a reported complication. 1 PRO, M Patient-reported outcome measure; ODI, Oswestry Disability Index; RMDQ, Roland and Morris Disability Questionnaire; NPRS, Numeric Pain Rating Scale; VAS, Visual Analogue Scale; NASS, North American Spine Society lumbar spine outcome scale; COMI, Core Outcome Measures Index; SF36, SF 12, Short Form 36 or 12 questions; EQ5D, EuroQol 5 Dimensions (including EQ VAS); SRS-22, Scoliosis Research Society 22 questions Seng et al. 2013 [33] Adogwa et al. 2013 [27] USA Adult Deformity Outcomes Database 05 Schwab et al. 2008 [29] 104 Evidence and practice in spine registries Evidence and practice in spine registries 105 References 1. Hoy D, March L, Brooks P, Blyth F, Woolf A, Bain C et al. The global burden of low back pain: estimates from the Global Burden of Disease 2010 study. Ann.Rheum.Dis. 2014;73:968-974 2. 3. Porter ME. What is value in health care? N.Engl.J.Med. 2010;363:2477-2481 Jacobs WC, Kruyt MC, Verbout AJ, Oner FC. Effect of methodological quality measures in spinal surgery research: a metaepidemiological study. Spine J. 2012;12:339-348 4. Benson K, Hartz AJ. A comparison of observational studies and randomized, controlled trials. N.Engl.J.Med. 2000;342:1878-1886 5. Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N.Engl.J.Med. 2000;342:1887-1892 6. Weinstein JN, Lurie JD, Tosteson TD, Zhao W, Blood EA, Tosteson AN et al. Surgical compared with nonoperative treatment for lumbar degenerative spondylolisthesis. four-year results in the Spine Patient Outcomes Research Trial (SPORT) randomized and observational cohorts. J.Bone Joint Surg.Am. 2009;91:1295-1304 7. Concato J, Lawler EV, Lew RA, Gaziano JM, Aslan M, Huang GD. Observational methods in comparative effectiveness research. Am.J.Med. 2010;123:e16-e23 8. Colditz GA. Overview of the epidemiology methods and applications: strengths and limitations of observational study designs. Crit Rev.Food Sci.Nutr. 2010;50 Suppl 1:10-12 9. Jacobs WC, Kruyt MC, Verbout AJ, Oner FC. Spine surgery research: on and beyond current strategies. Spine J. 2012;12:706-713 10. Phillips FM, Slosar PJ, Youssef JA, Andersson G, Papatheofanis F. Lumbar spine fusion for chronic low back pain due to degenerative disc disease: a systematic review. Spine (Phila Pa 1976.) 2013;38:E409-E422 11. Gliklich RE, Dreyer NA. Registries for Evaluating Patient Outcomes: A User's Guide. 2nd ed. Rockville: Agency for Healthcare Research and Quality; 201012. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 2007;370:1453-1457 13. Larsson S, Lawyer P, Garellick G, Lindahl B, Lundstrom M. Use of 13 disease registries in 5 countries demonstrates the potential to use outcome data to improve health care's value. Health Aff.(Millwood.) 2012;31:220-227 14. van Leersum NJ, Kolfschoten NE, Klinkenbijl JH, Tollenaar RA, Wouters MW. ['Clinical auditing', a novel tool for quality assessment in surgical oncology]. Ned.Tijdschr.Geneeskd. 2011;155:A4136 15. PRISMA. PRISMA statement and checklist. 2015 [cited 2016 July 20]. Available from: http://www.prismastatement.org 16. Jacobs W, Hooff van M.L., Stoefs J, Stowell C, Fritzell P. ISSLS Focus Group Global Collaboration of Spine Registries. In: 2014 International Society for the Study of the Lumbar Spine (ISSLS) 2014 June 3-7; Seoul, South Korea 17. Drolet BC, Johnson KB. Categorizing the world of registries. J.Biomed.Inform. 2008;41:1009-1020 18. Shojania KG, Ranji SR, McDonald KM, Grimshaw JM, Sundaram V, Rushakoff RJ et al. Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA 2006;296:427-440 19. Tricco AC, Ivers NM, Grimshaw JM, Moher D, Turner L, Galipeau J et al. Effectiveness of quality improvement strategies on the management of diabetes: a systematic review and meta-analysis. Lancet 2012;379:22522261 20. Munce SE, Perrier L, Tricco AC, Straus SE, Fehlings MG, Kastner M et al. Impact of quality improvement strategies on the quality of life and well-being of individuals with spinal cord injury: a systematic review protocol. Syst.Rev. 2013;2:14 05 106 Evidence and practice in spine registries 21. AHRQ. Agency for Healthcare Research and Quality. [cited2015 February 09]; Available from: http://archive. ahrq.gov/consumer/qnt/qntqlook.htm 22. McCormick JD, Werner BC, Shimer AL. Patient-reported outcome measures in spine surgery. J.Am.Acad. Orthop.Surg. 2013;21:99-107 23. Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 2014 [cited 2015 February 09]. Available from: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp 24. ICHOM (International Consortium for Health Outcomes Measurements). Standard Set for Low Back Pain. 2014 [cited 2015 February 09]. Available from: http://www.ichom.org/project/low-back-pain/ 25. Nerland US, Jakola AS, Solheim O, Weber C, Rao V, Lonne G et al. Comparative effectiveness of 05 microdecompression and laminectomy for central lumbar spinal stenosis: study protocol for an observational study. BMJ Open. 2014;4:e004651 26. Robinson Y, Michaelsson K, Sanden B. Instrumentation in lumbar fusion improves back pain but not quality of life 2 years after surgery. A study of 1,310 patients with degenerative disc disease from the Swedish Spine Register SWESPINE. Acta Orthop. 2013;84:7-11 27. Adogwa O, Huang MI, Thompson PM, Darlington T, Cheng JS, Gokaslan ZL et al. No difference in postoperative complications, pain, and functional outcomes up to 2 years after incidental durotomy in lumbar spinal fusion: a prospective, multi-institutional, propensity-matched analysis of 1,741 patients. Spine J. 2014;14:1828-1834 28. Kasliwal MK, Smith JS, Shaffrey CI, Carreon LY, Glassman SD, Schwab F et al. Does prior short-segment surgery for adult scoliosis impact perioperative complication rates and clinical outcome among patients undergoing scoliosis correction? J.Neurosurg.Spine 2012;17:128-133 29. Schwab FJ, Lafage V, Farcy JP, Bridwell KH, Glassman S, Shainline MR. Predicting outcome and complications in the surgical treatment of adult scoliosis. Spine (Phila Pa 1976.) 2008;33:2243-2247 30. Bridwell KH, Berven S, Glassman S, Hamill C, Horton WC, III, Lenke LG et al. Is the SRS-22 instrument responsive to change in adult scoliosis patients having primary spinal deformity surgery? Spine (Phila Pa 1976.) 2007;32:2220-2225 31. Knutsson B, Michaelsson K, Sanden B. Obesity is associated with inferior results after surgery for lumbar spinal stenosis: a study of 2633 patients from the Swedish spine register. Spine (Phila Pa 1976.) 2013;38:435441 32. Deer T, Chapple I, Classen A, Javery K, Stoker V, Tonder L et al. Intrathecal drug delivery for treatment of chronic low back pain: report from the National Outcomes Registry for Low Back Pain. Pain Med. 2004;5:613 33. Seng C, Siddiqui MA, Wong KP, Zhang K, Yeo W, Tan SB et al. Five-year outcomes of minimally invasive versus open transforaminal lumbar interbody fusion: a matched-pair comparison study. Spine (Phila Pa 1976.) 2013;38:2049-2055 34. Corcoll J, Orfila J, Tobajas P, Alegre L. Implementation of neuroreflexotherapy for subacute and chronic neck and back pain within the Spanish public health system: audit results after one year. Health Policy 2006;79:345-357 35. Grob D, Mannion AF. The patient's perspective on complications after spine surgery. Eur.Spine J. 2009;18 Suppl 3:380-385 36. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2197-2223 37. Sigmundsson FG, Jonsson B, Stromqvist B. Impact of pain on function and health related quality of life in lumbar spinal stenosis. A register study of 14,821 patients. Spine (Phila Pa 1976.) 2013;38:E937-E945 Evidence and practice in spine registries 107 38. Royuela A, Kovacs FM, Campillo C, Casamitjana M, Muriel A, Abraira V. Predicting outcomes of neuroreflexotherapy in patients with subacute or chronic neck or low back pain. Spine J. 2014;14:1588-1600 39. Solberg T, Johnsen LG, Nygaard OP, Grotle M. Can we define success criteria for lumbar disc surgery? : estimates for a substantial amount of improvement in core outcome measures. Acta Orthop. 2013;84:196201 40. McGirt MJ, Speroff T, Dittus RS, Harrell FE, Jr., Asher AL. The National Neurosurgery Quality and Outcomes Database (N2QOD): general overview and pilot-year project description. Neurosurg.Focus. 2013;34:E6 41. Kovacs FM, Seco J, Royuela A, Corcoll RJ, Abraira V. Predicting the evolution of low back pain patients in routine clinical practice: results from a registry within the Spanish National Health Service. Spine J. 2012;12:1008-1020 42. Sigmundsson FG, Kang XP, Jonsson B, Stromqvist B. Prognostic factors in lumbar spinal stenosis surgery. Acta Orthop. 2012;83:536-542 43. Zweig T, Hemmeler C, Aghayev E, Melloh M, Etter C, Roder C. Influence of preoperative nucleus pulposus status and radiculopathy on outcomes in mono-segmental lumbar total disc replacement: results from a nationwide registry. BMC.Musculoskelet.Disord. 2011;12:275 44. Glassman SD, Schwab F, Bridwell KH, Shaffrey C, Horton W, Hu S. Do 1-year outcomes predict 2-year outcomes for adult deformity surgery? Spine J. 2009;9:317-322 45. Aghayev E, Roder C, Zweig T, Etter C, Schwarzenbach O. Benchmarking in the SWISSspine registry: results of 52 Dynardi lumbar total disc replacements compared with the data pool of 431 other lumbar disc prostheses. Eur.Spine J. 2010;19:2190-2199 46. Aghayev E, Henning J, Munting E, Diel P, Moulin P, Roder C. Comparative effectiveness research across two spine registries. Eur.Spine J. 2012;21:1640-1647 47. Berg S, Tropp H. Results from a randomized controlled study between total disc replacement and fusion compared with results from a spine register. SAS J. 2010;4:68-74 48. Kovacs F, Abraira V, Muriel A, Corcoll J, Alegre L, Tomas M et al. Prognostic factors for neuroreflexotherapy in the treatment of subacute and chronic neck and back pain: a study of predictors of clinical outcome in routine practice of the Spanish National Health Service. Spine (Phila Pa 1976.) 2007;32:1621-1628] 49. Jansson KA, Nemeth G, Granath F, Jonsson B, Blomqvist P. Health-related quality of life in patients before and after surgery for a herniated lumbar disc. J.Bone Joint Surg.Br. 2005;87:959-964 50. Jansson KA, Nemeth G, Granath F, Jonsson B, Blomqvist P. Health-related quality of life (EQ-5D) before and one year after surgery for lumbar spinal stenosis. J.Bone Joint Surg.Br. 2009;91:210-216 51. Porchet F, Bartanusz V, Kleinstueck FS, Lattig F, Jeszenszky D, Grob D et al. Microdiscectomy compared with standard discectomy: an old problem revisited with new outcome measures within the framework of a spine surgical registry. Eur.Spine J. 2009;18 Suppl 3:360-366 52. Glassman SD, Hamill CL, Bridwell KH, Schwab FJ, Dimar JR, Lowe TG. The impact of perioperative complications on clinical outcome in adult deformity surgery. Spine (Phila Pa 1976.) 2007;32:2764-2770 53. Taylor VM, Deyo RA, Ciol M, Farrar EL, Lawrence MS, Shonnard NH et al. Patient-oriented outcomes from low back surgery: a community-based study. Spine (Phila Pa 1976.) 2000;25:2445-2452 54. Schluessmann E, Diel P, Aghayev E, Zweig T, Moulin P, Roder C. SWISSspine: a nationwide registry for health technology assessment of lumbar disc prostheses. Eur.Spine J. 2009;18:851-861 55. Sanden B, Forsth P, Michaelsson K. Smokers show less improvement than nonsmokers two years after surgery for lumbar spinal stenosis: a study of 4555 patients from the Swedish spine register. Spine (Phila Pa 1976.) 2011;36:1059-1064 56. Sigmundsson FG, Jonsson B, Stromqvist B. Preoperative pain pattern predicts surgical outcome more than type of surgery in patients with central spinal stenosis without concomitant spondylolisthesis: a register study of 9051 patients. Spine (Phila Pa 1976.) 2014;39:E199-E210 05 108 Evidence and practice in spine registries 57. Fritzell P, Knutsson B, Sanden B, Stromqvist B, Hagg O. Recurrent Versus Primary Lumbar Disc Herniation Surgery: Patient-reported Outcomes in the Swedish Spine Register Swespine. Clin.Orthop.Relat Res. 2014 58. Karrholm J. The Swedish Hip Arthroplasty Register (www.shpr.se). Acta Orthop. 2010;81:3-4 59. Oien RF, Forssell HW. Ulcer healing time and antibiotic treatment before and after the introduction of the Registry of Ulcer Treatment: an improvement project in a national quality registry in Sweden. BMJ Open. 2013;3:e003091 60. Pronovost P, Needham D, Berenholtz S, Sinopoli D, Chu H, Cosgrove S et al. An intervention to decrease catheter-related bloodstream infections in the ICU. N.Engl.J.Med. 2006;355:2725-2732 61. Stromqvist F, Jonsson B, Stromqvist B. Dural lesions in decompression for lumbar spinal stenosis: incidence, risk factors and effect on outcome. Eur.Spine J. 2012;21:825-828 05 62. Forsth P, Michaelsson K, Sanden B. Does fusion improve the outcome after decompressive surgery for lumbar spinal stenosis?: A two-year follow-up study involving 5390 patients. Bone Joint J. 2013;95-B:960-965 63. Forsth P. No benefit from fusion in decompressive surgery for lumbar spinal stenosis. Two-year results from the Swedish spinal stenosis study. A multicenter RCT of 229 patients. In: 2014 EuroSpine conference; Lyon, France 64. Desai A, Bekelis K, Ball PA, Lurie J, Mirza SK, Tosteson TD et al. Variation in outcomes across centers after surgery for lumbar stenosis and degenerative spondylolisthesis in the spine patient outcomes research trial. Spine (Phila Pa 1976.) 2013;38:678-691 65. Mannion AF, Brox JI, Fairbank JC. Comparison of spinal fusion and nonoperative treatment in patients with chronic low back pain: long-term follow-up of three randomized controlled trials. Spine J. 2013;13:1438-1448 66. Terwee CB, Bot SD, de Boer MR, van der Windt DA, Knol DL, Dekker J et al. Quality criteria were proposed for measurement properties of health status questionnaires. J.Clin.Epidemiol. 2007;60:34-42 67. Wood AM, White IR, Thompson SG. Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals. Clin.Trials 2004;1:368-376 68. Iezzoni LI. Risk adjustment for medical effectiveness research: an overview of conceptual and methodological considerations. J.Investig.Med. 1995;43:136-150 69. Wouters MW, Wijnhoven BP, Karim-Kos HE, Blaauwgeers HG, Stassen LP, Steup WH et al. High-volume versus low-volume for esophageal resections for cancer: the essential role of case-mix adjustments based on clinical data. Ann.Surg.Oncol. 2008;15:80-87 70. Dimick JB, Staiger DO, Birkmeyer JD. Ranking hospitals on surgical mortality: the importance of reliability adjustment. Health Serv.Res. 2010;45:1614-1629 71. van Hooff ML, van Loon J, van Limbeek J, de Kleuver M. The Nijmegen decision tool for chronic low back pain. Development of a clinical decision tool for secondary or tertiary spine care specialists. PLoS.One. 2014;9:e104226 72. Sackett D L, Richardson W S, Rosenberg W, Haynes R B. Evidence-based medicine: how to practice and teach EBM. Edinburgh: Churchill Livingstone; 2000 73. Schulz KFGrimes DA. Sample size slippages in randomised trials: exclusions and the lost and wayward. Lancet 2002;359:781-785 74. Solberg TK, Sorlie A, Sjaavik K, Nygaard OP, Ingebrigtsen T. Would loss to follow-up bias the outcome evaluation of patients operated for degenerative disorders of the lumbar spine?. Acta Orthop. 2011;82:56-63 75. Fritzell P, Stromqvist B, Hagg O. A practical approach to spine registers in Europe: the Swedish experience. Eur.Spine J. 2006;15 Suppl 1:S57-S63 76. Twisk J, de Vente W. Attrition in longitudinal studies. How to deal with missing data. J.Clin.Epidemiol. 2002;55:329-337 77. Donders AR, van der Heijden GJ, Stijnen T, Moons KG. Review: a gentle introduction to imputation of missing values. J.Clin.Epidemiol. 2006;59:1087-1091 Evidence and practice in spine registries 109 78. Scheer JK, Tang JA, Smith JS, Klineberg E, Hart RA, Mundis GM, Jr. et al. Reoperation rates and impact on outcome in a large, prospective, multicenter, adult spinal deformity database: clinical article. J.Neurosurg. Spine 2013;19:464-470 79. Shevelev IN, Kornienko VN, Konovalov NA, Cherkashov AM, Molodchenkov AI, Votkins RG et al. [Working results of the electronic "on-line" version of the Spine Registry for Degenerative Lumbar Spine Diseases and study of its synchronization capacity with the electronic case history.]. Zh.Vopr.Neirokhir.Im N.N.Burdenko 2013;77:57-64 Guest editorial: Spinal disorders, quality-based healthcare and spinal registers Acta Orthop. 2015; 86 (5): 521–522 See after Chapter 06 - page 130-133 05 110 05 111 Chapter 06 A proposed set of metrics for standardized outcome reporting in the management of low back pain Clement RC, Welander A, Stowell C, Cha TD, Chen JL, Davies M, Fairbank JC, Foley KT, Gehrchen M, Hägg O, Jacobs WC, Kahler R, Khan SN, Lieberman IH, Morisson B, Ohnmeiss D, Peul WC, Shonnard NH, van Hooff ML, Wassan AD, Willems PC, Yeo W, Fritzell P Published in: Acta Orthop. 2015;86(5)523-33 Appendix to Chapter 06 - editorial comment (after Chapter 06 - page 130-133) Guest editorial: Spinal disorders, quality-based healthcare and spinal registers Fairbank JCT Published in: Acta Orthop. 2015; 86 (5): 521–522 112 Abstract Background & Purpose: Outcome measurement has been shown to improve performance in several fields of healthcare. This understanding has driven a growing interest in value-based healthcare, where value is defined as outcomes achieved per money spent. While low back pain (LBP) constitutes an enormous burden of disease, no universal set of metrics has yet been accepted to measure and compare outcomes. Here, we aim to define such a set. Materials & Methods: An international group of 22 specialists in multiple disciplines of spine care was assembled to review literature and select LBP outcome metrics through a 6-round modified Delphi process. The scope of the outcome set was degenerative lumbar conditions. Results: Patient-reported metrics include a numeric pain scales, lumbar-related function using the Oswestry Disability Index, health-related quality of life using the EQ-5D-3L questionnaire, and questions assessing work status and analgesic use. Specific common and serious complications were included. Recommended follow-up intervals include 6, 12 and 24 months after initiating treatment, with optional follow-up at 3 months and 5 years. Metrics for risk stratification were selected based on pre-existing tools. Interpretation: The outcome measures recommended here are structured around specific etiologies of LBP, span a patient’s entire cycle of care, and allow for risk adjustment. Thus, when implemented, this set can be expected to facilitate meaningful comparisons and ultimately to provide a continuous feedback loop, enabling ongoing improvements in quality of care. Much work lies ahead in implementation, revision, and validation of this set, but it represents an essential first step toward establishing a community of LBP providers focused on maximizing the value of care we deliver. Proposed set of metrics for LBP 113 Introduction Measuring outcomes in healthcare has well documented benefits as well as challenges [1,2]. Simply asking providers to report their outcomes has been shown to improve performance [3]. Additionally, understanding one’s results empowers a provider to continuously learn from and refine the care he or she delivers [4]. On a broad scale, outcome reporting also facilitates dissemination of best practices between physicians and makes it possible to compare the quality delivered by different providers, allowing patients to make intelligent choices about where to seek care [4]. This type of continuous improvement and informed decision making could be an important driving force in improving healthcare delivery by refocusing the system on value (defined as the outcomes of care divided by the cost). This concept of ‘value-based healthcare’ has been gaining attention both throughout the medical field [1,5] and specifically within the spine community [6,7]. With evolving reimbursement systems in many countries, it is also conceivable that there will be growing interest in ‘value-based reimbursement’ in the future, with payment levels adjusted based on outcomes. This type of scheme will only be fair with a broadly-accepted and risk-adjusted set of outcome metrics. Low back pain (LBP) is a growing problem in the population and constitutes a major component of the global burden of disease [8]. Measuring outcomes in the field of low back pain is challenging. Numerous disease states affect the low back, resulting in low back pain, leg pain or both; to compare outcomes, patients must be accurately stratified by both diagnosis and severity. Moreover, existing treatment algorithms are complex and often controversial, including both operative and non-operative options and frequently requiring multidisciplinary provider teams. Additionally, low back pain rarely causes death or other objective end points, so outcomes are best measured by patient-reported metrics, which are inherently subjective and require thorough psychometric testing. A substantial amount of work has already been done in the field of low back pain and wellvalidated tools exist for measuring disease-specific outcomes [9]. Similarly, several large registries are already in existence collecting outcomes, along with many other data points [10-13]. Previous consensus-based efforts have been made to define sets of outcome measures or domains for research purposes [14-16]. Still, the field of low back pain care has not yet developed a universal international set of outcomes to be measured and compared as a part of standard clinical practice. This type of outcome set requires availability and validity in many languages, requires capacity for case-mix adjustment to ensure that comparisons are made fairly, and should focus on the outcomes that matter most to patients. The purpose of this study was to define such a set based on international and interdisciplinary expert and patient opinion. Methods The set of outcomes we present, referred to as the standard set, was developed by consensus among a 22-member ‘working group’ mostly comprised of surgical, rehabilitation and medical experts in the field of low back pain, many of whom are active in spine registries (all members are listed as authors). The group also included a former spine patient involved in patient support groups (MD). The working group was convened and organized by the International 06 114 Proposed set of metrics for LBP Consortium for Health Outcomes Measurement (ICHOM), a non-profit organization focused on the development of standard sets of outcomes and risk factors for multiple medical conditions [17]. The working group’s efforts were coordinated by a core ‘project Team’ consisting of a working group lead (PF), a project leader (AW), a research fellow (RC), and the ICHOM vice president of research and development (CS). 06 The project was structured as a modified Delphi process [18] involving 6 teleconferences held between June and November of 2013. The goals of these calls were choosing inclusion and exclusion criteria for the relevant patient population, selecting and defining outcome metrics, and identifying initial disease conditions and risk factors that would allow patient stratification and case-mix adjusted comparisons between providers. Teleconferences were structured around proposals by the project team regarding how best to meet the goals of the group. These proposals were based on review of academic literature, review of existing practices among spine registries, and in some cases, direct input from working group members and other experts in the field. Decisions were made by surveys, which were designed based on the project team’s proposals and the relevant discussion held during the teleconference. Surveys were circulated by email following teleconferences to all working group members along with detailed minutes. In a small number of cases, live votes were orchestrated during a call. For surveys and votes with less than a two-thirds majority or with a particularly vigorous debate, the issue was revisited by the project team and a new proposal was presented to the working group for consideration. Several recurrent themes emerged throughout this process, and developed into guiding principles for the group’s collaboration. First, we aimed to identify outcome metrics that are most important to patients, which often resulted in favoring subjective information reported by patients rather than objective clinical information traditionally followed by physicians. Second, we sought genuine outcome metrics to gauge quality, not process metrics, which are often used as inexact proxies for quality, as they are frequently easier to track. Third, a consistent effort was made to simplify the set of outcomes and associated data, especially the information requested from physicians in order to boost compliance. As such, we acknowledge that the goal of the standard set should be to allow comparisons of clinical outcomes and, while it will be sufficient to answer certain research questions, many academic pursuits will require collection of additional data points. Fourth, when possible, existing tools with proven validity and reliability such as the Oswestry Disability Index (ODI) and EQ-5D were selected in their original format to preserve their proven psychometric properties. Finally, a conscious effort was made to be continually aware of potential bias favoring surgical patients, given the predominance of surgeons in the working group, which reflects the predominating focus on surgical patients in the existing spine registries. ICHOM had access to all data during the project, but neither ICHOM nor its funders had editorial control over the final publication. The manuscript was drafted by the project team’s research fellow (RC) and subsequently edited based on input of all the experts and co-authors. Proposed set of metrics for LBP 115 Results Response rates for the 5 working group surveys among the 22 working group members were 21, 20, 21, 20, and 21, respectively. Two original working group members participated in less than half of teleconferences and surveys and are not included either in these response rates or in the final list of members. Scope: Degenerative lumbar conditions The standard set targets degenerative lumbar conditions, which comprise the vast majority of all of lumbar pathology [22]. Other areas of spine care involve different patient populations, treatment approaches, and outcomes - and should be addressed in the future by analogous condition-specific outcome sets. Formal inclusion criteria selected by the working group consist of lumbar spinal stenosis, lumbar spondylolisthesis, degenerative disc disorders including disc herniation, degenerative scoliosis, other degenerative lumbar disorders, and acute and chronic lumbar back pain and back-related leg pain without a clear etiology (often colloquially termed mechanical or non-specific pain). The relevant corresponding exclusion criteria include spinal infection, tumor, fracture, traumatic dislocation, congenital or idiopathic scoliosis, and age under 18 years. Outcome domains (Table 6.1) Traditionally, the 6 domains most commonly used to study outcomes among patients with degenerative lumbar conditions have been function, pain, health-related quality of life (HRQOL), work status, treatment complications and medication requirements [19]. This pattern suggests that historically, spine providers have felt that these domains most accurately reflect success rather than failure in this field. Furthermore, after careful consideration including discussion with the group’s patient representative, the working group considered these are the factors that matter most to patients. The group also agreed that the combination of these factors provides adequate domain coverage for comprehensive assessment of treatment outcomes in this population. Other metrics that have been used to study LBP care - including psychosocial factors such as depression and ‘global effect’ [19] - were excluded from the set, as historically they have been studied with inconsistent definitions [19] and they are probably reflected in other domains such as HRQOL. 06 116 Proposed set of metrics for LBP Table 6.1 Patient-reported outcome measures Outcome Measurement Tool Definition/Wording Answer options Pain Numeric pain rating Scale How would you rate your average back pain over the last week? 0 (no pain) - 10 (worst pain imaginable) How would you rate your average leg pain over the last week? Disability Oswestry disability index 06 Quality of Life EQ5D-3L EQ-VAS Work Status Analgesic Use Pain intensity Personal Care (washing, dressing, etc.) Lifting Walking Sitting Standing Sleeping Sex Life (if applicable) Social life Traveling 0 (no pain) - 10 (worst pain imaginable), verbal or visual (horizontal) 6 options for each domain ranging from no problem to severe impairment (see appendix) Mobility Self-Care Usual Activities Pain/Discomfort Anxiety/Depression 3 options for each domain ranging from no problem to severe impairment (see appendix) Indicate on this scale how good or bad your health is today Vertical visual analog scale: 0 (Worst imaginable health state) - 100 (Best imaginable health state) What is your current work status? Working full time, working part time, Seeking employment (I consider myself able to work but can't find a job), Not working by choice (retired, student, homemaker, etc.), Unable to work due to problem other than my back and/or leg pain, Unable to work due to back and/or leg pain Are you working at a physically less demanding job now because of your back and/or leg pain? Yes, No, N/A How long after you received treatment for low back pain did you return to work? (if applicable) < 3 months, 3-6 months, 6-9 months, 9-12 months, 1-2 years, >2 years Do you take nonnarcotic pain relieving medication or tablets for your back problems? Yes regularly, Yes sometimes, No Do you take narcotic pain relieving medication or tablets for your back problems? Yes regularly, Yes sometimes, No Timeframe for capturing Baseline, index event(s), 6 months, 1 year, 2years Baseline, index event(s), 6 months, 1 year, 2 years Baseline, index event(s), 6 months, 1 year, 2years Baseline, index event(s), 6 months, 1 year, 2years 6 months, 1 year, 2years Baseline, index event(s), 6 months, 1 year, 2 years Proposed set of metrics for LBP 117 Patient-reported outcome measures (PROMs) The core component of the standard set is a constellation of PROMs covering the 6 domains listed above, collected at the time of enrollment for treatment and then at regular time points. (As detailed below, some information on clinical complications also requires clinician reporting.) PROM instruments were chosen by the working group on the basis of clinical interpretability, feasibility of implementation, and psychometric properties (validity, reliability, and responsiveness) [20]. Common and well validated methods for measuring pain include the numeric rating scale (NRS) and the visual analog scale (VAS) [19,21], and the major existing spine registries are divided between those options [10-12]. While there is no gold standard, a VAS allows patients to provide a more specific response while a NRS is typically easier to use as it can be performed verbally and does not require exact size calibration when reprinted or generated on a monitor. The common 0-10 horizontal version asking for average pain over the last week has been shown to be valid, reliable and to allow adequately specific responses among spine patients [21-24]. This option was chosen by the working group (with 21 of 22 members in agreement) for inclusion in the standard set, both for back and leg pain individually. Numerous tools have been studied for measuring lumbar-related function in patients with low back pathology [9,19]. The ODI is the most commonly used and cited tool for this purpose, followed by the Roland Morris Disability Questionnaire (RMDQ) [19] and the Core Outcome Measures Index (COMI) [25]. While all of these have been shown to be valid, reliable and responsive in this population, the ODI is the most heavily studied, providing superior clinical interpretability [19]. We also felt the ODI to be the most feasible to implement as it is validated in 14 languages [as opposed to 9 for each of the RMDQ [19] and COMI [23]] and is relatively short [10 items as opposed to 24 in the RMDQ [26] and 7 in the COMI [27]]. All are free with online registration being required for use of the ODI [19]. For these reasons, the working group unanimously chose the ODI 2.1a for inclusion in the standard set. There are several tools for measurement of HRQOL in LBP patients exist [28-32], with the most common and heavily studied being the SF-36 followed by the EQ-5D and accompanying EQVAS, Nottingham Health Profile (NHP) and SF-12 [19]. The SF-36 has been shown to be valid, reliable and responsive in this population, while the NHP and SF-12 have been proven valid and reliable [19]. To our knowledge, these have not been studied for responsiveness and none of the psychometric properties of the EQ-5D have yet been examined among LBP patients. However, the EQ-5D tool has an excellent track record among other demographics as well as the general population [33,34], and has been shown to correlate well with the ODI in LBP patients [35]. Additionally, the volume of recent citations suggests a relatively rapid increase in the use and dissemination of this tool, which is consistent with the anecdotal experience of working group members. The EQ-5D and EQ-VAS also has the advantage of being relatively brief (6 items as opposed to 36 in the SF-36, 38 in the NHP, and 12 in the SF-12) and has proven psychometric properties in over 160 languages (in comparison to 155 for the SF-36, 2 for the NHP and 134 for the SF-12) [19]. The EuroQol tool is also inexpensive [36] relative to the SF tools [37,38], while use of the NHP is free. Lastly, the EQ-5D is superior for health economics evaluations as it is a preference-based tool that allows utility calculations and cost effectiveness analysis [19]For these reasons, the working group chose the EQ-5D for inclusion in the standard set, with 21 of 22 members in favor. 06 118 Proposed set of metrics for LBP Existing practices used by current registries for questioning patients about analgesic use and working status were reviewed, and the approach used by the international Spine Tango registry was felt to be the most concise and thorough [10]; the wording was modified slightly by the working group. 06 Complications and adverse events Adverse consequences of treatment, e.g. invasive procedures, comprise another category of outcomes. While no objective criteria were used, the working group aimed to include complications and adverse events that are relatively frequent, severe, avoidable and feasible to capture. Careful attention was paid to the balance between gathering sufficient data to allow comparisons between providers and keeping the collection process simple enough to facilitate a high level of compliance. The decision was made to request that providers report complications/adverse effects recognized at the time of an initial procedure or during the associated hospitalization, which is considered the index period. Subsequently, when completing PROMs questionnaires 6 months after an index period, patients should be asked to report specified complications that occurred after this period. The interventions of interest are surgeries and injection therapy, and for convenience, the same list of complications and timeframe for collection should be used for both. Early provider-reported complications selected for inclusion during the index period include death, nerve injury, dural tear, vascular injury, deep infection and pulmonary embolus (PE) (Table 6.2). In regions where reliable administrative death records are readily accessible, the working group recommends the use of such administrative data to more accurately track outof-hospital mortality within the first 30 days. At the time of follow-up PROM questionnaires, patients should be asked if they experienced a deep wound infection or PE as these can be particularly detrimental complications but may only occur or be recognized after the index period. As providers may not be made aware of unplanned re-hospitalizations within 30 days of the index period, which have become a popular healthcare quality metric, patients should also be asked to report such events [39,40]. In countries and practices with reliable administrative documentation of re-hospitalization, such as electronic medical records or insurance databases, the working group recommends using this administrative data to record such events. Reoperations after an index procedure, and the underlying cause, should be reported by providers (Table 2). Baseline characteristics and risk factors for case-mix adjustment In order to statistically adjust analyses for fair and meaningful calculations, relevant data on patients’ risk factors and initial conditions must be collected. The working group tried to balance the time and financial cost of collecting data with the need for accurate comparisons, while seeking internationally comparable data points. This information was addressed in 4 categories: demographics, baseline clinical status, baseline functional status and previous treatments (Table 6.3). Common demographics currently in use in international registries were reviewed and age, sex and socioeconomic status were chosen, with education level being used as an internationally acceptable proxy for the latter. Specifically, the United Nations Educational, Scientific and Cultural Organization (UNESCO) definitions of education levels, which allow for international and cross-cultural comparisons, were selected for use [41]. Race and ethnicity were discussed but they were ultimately felt to be of limited value as risk adjusters. Proposed set of metrics for LBP To define a patient’s baseline clinical status, the lumbar pathology criteria defined and studied by Glassman et al. [42] were selected, primarily for its applicability to both operative and conservatively-treated patients (Table 6.3). To our knowledge, no single tool has been validated to define the diagnoses of patients across the entire realm of degenerative lumbar pathology, and the Glassman criteria is the only such tool that has been shown to be reliable between providers. Additionally, our review suggests that providers will rapidly be able to learn and use these criteria. In addition to these clinical data, indications for surgery should be recorded to facilitate risk stratification. After review of the literature and current registries, the set of operative indications used by the Swespine registry [12] was felt to be the most complete yet concise example of such a list, and was chosen for inclusion in the standard set, to be completed by providers at the time of surgery (Table 3). Additionally, the American Society of Anesthesiologists (ASA) Physical Status Classification System has been shown to be prognostic for many surgical procedures [43-45] and the working group felt it should be reported before surgery. In addition to data related directly to the lumbar spine and surgical risk, a patient’s baseline clinical status also encompasses other comorbidities, which have historically been the basis for risk adjustment in large patient populations. Patient-reported responses to the Charlson comorbidity Index [46] have been proven predictive of both mortality and various PROMs [47,48]. To our knowledge, no comorbidity list has been validated for risk adjustment in LBP patients. For this purpose, we chose the collection of 13 conditions used by the UK National Health Service for risk stratification in total hip replacement [49]. This set was augmented with 2 conditions included in the Charlson index that the working group considered particularly prescient in the LBP population: paraplegia/hemiplegia and HIV/AIDS (Table 6.3). Smoking habits [50,51] and BMI [52-54] have been shown to provide prognostic value in lumbar patients and were therefore also designated for collection at baseline. It should be noted that depression - which is included among the patient-reported comorbidities described above and which has been shown to be predictive of outcomes among spine patients [55-57] - was discussed at length, and the working group concluded this information should be collected by patient report rather than formal depression screening or physician report, both for the sake of efficiency and because depression is likely reflected in other PROMs such as HRQOL. Lastly, some PROMs collected at baseline provide relevant information about a patient’s baseline clinical status and should be used for risk adjustment analyses - namely pain level, duration of symptoms, and current analgesic use. Similarly, a patient’s baseline functional status is delineated through initial PROMs collection, i.e. by measuring disability, HRQOL, work status and (when applicable) duration of sick leave. Finally, the working group felt strongly that information on previous treatments is essential for accurate risk adjustment, and selected previous surgery and injection therapy for collection at baseline (Table 6.3 and Figure6.1A), as history of each of these has been shown to be prognostic for subsequent treatment outcomes [58-61]. Stratification of previous operations as either discectomy, decompression, or fusion was deemed to be adequately simple for data collection purposes while being sufficiently detailed for risk adjustment. Additionally, while technically a process metric, the working group recommends providers record the types and levels of surgeries and injections performed at the time of intervention to further facilitate risk stratification (Figure 6.1B). Again, this level of detail is intended to be as brief as possible in order to streamline data collection while simultaneously allowing meaningful risk adjustment. 119 06 120 Proposed set of metrics for LBP Figure6.1A. A tool for recording the date and type of prior treatment 6.1B. A tool for recording interventions performed on an ongoing basis Figure 1A. Prior surgical interventions "Indicate those that apply by entering month/year of surgery in boxes below." Procedure type T12-L1 L1-L2 L2-L3 L3-L4 L4-L5 L5-S1 L3-L4 L4-L5 L5-S1 Discectomy Decompression (laminectomy) Fusion Figure 1B. Interventions 06 "Indicate type and level of current procedural intervention." Procedure type T12-L1 L1-L2 L2-L3 Discectomy Decompression (laminectomy) Fusion Other back surgery Injection therapy ‘Index events’ and timeframe of follow-up Regarding the timing of data collection, we elected to establish follow-up at 6 months, 1 year and 2 years after initiating treatment (Table 6.1 and Figure 6.2). Additional follow-up points at 3 months and 5 years were recommended, though not mandatory, as the former is probably meaningful in the management of non-operatively-treated patients but less so for surgical patients; and for the latter, the contrary is usually true. To simplify data collection and improve compliance, we decided to record complications only following index operations and not after reoperations, which would complicate the follow-up process substantially. Index events, a term adopted from the SweSpine Registry [12], are points in the course of care that should trigger the follow up schedule to be reset. The initiation of treatment for any new condition, whether managed surgically or not, clearly constitutes an index event. Reoperation for management of a complication or failure to attain the therapeutic goals of an initial surgery is not an index event. However, surgery for a new diagnosis or at a new vertebral level is considered to be a new index event and should cause follow up, including all measurement of PROMs, to be reset (Figure 6.3). At that point, the follow-up schedule started after the initial index event is discontinued, as it is not practical to simultaneously conduct 2 follow up schedules for a single patient. Proposed set of metrics for LBP 121 Figure 6.2. The recommended timeline for collection of each outcome measure Figure 2. Timeline of outcome measurements Data points Baseline Index period1 6 mts2 1 yr 2 yrs3 PROMs for pain, disability and Q.O.L. Work status Time to return to work Continuous oral analgesic use Mortality Need for reopera+on Captured when reoperation(s) occurs Cause of reopera+on Need for rehospitalisa+on Complica+ons: Nerve root injury, Vascular injury, Dural tear, Other complica+ons 06 Complica+ons: Deep wound infec+on, Pulmonary embolus Risk factors Descriptors of clinical condi+on Type and level of surgery and surgical indica+on Patient-‐reported Physician-‐reported Administratively-‐reported (when available) 1. Risk factors and descriptors of clinical condition reported pre-‐intervention, complications and mortality reported at discharge 2. Collection also recommended at 3 months, but only deemed mandatory as displayed 3. Collection also recommended at 5 years, but only deemed mandatory as displayed Figure 6.3. A classification scheme to define interventions as either index events or reoperations Figure 3. Defini-on of index events and reopera-ons Initiation of non-‐surgical treatment or 1st lumbar surgery (Always an index event) Data captured at this time: Risk factors, descriptors of clinical condition, and when applicable, surgical indication, in-‐hospital complications, type and level of procedure (Follow up: 6 mos, 1 yr, and 2 yrs) 2nd and following lumbar surgeries An index event if 1. Operation is on a different level than index surgery, regardless of diagnosis 2. Operation is on the same level as prior surgery, but for a different diagnosis Follow up: 6 mos, 1 yr, and 2 yrs (Follow up for prior index is discontinued) Considered reoperation (not an index event) if 1. Operation is on the same level for the same diagnosis as index event 2. Operation is on the same level as index event due to a complication 3. Operation is on another level but due to complication from index surgery Data captured at time of reoperation: Risk factors, descriptors of clinical condition, cause of reoperation, in-‐ hospital complications, type and level of procedure Follow up: Continues as planned from index surgery 122 Proposed set of metrics for LBP Table 6.2 Adverse outcomes of treatment 06 Outcome Definition/Wording a Answer Options Mortality Death in-hospital (all-cause mortality) Yes/No Nerve root injury (including cauda equina syndrome) Iatrogenic nerve root damage Yes/No Vascular injury Clinically significant iatrogenic damage to a vessel Yes/No Dural tear Iatrogenic damage of the dura with liqour emission Yes/No Other2 (e.g. hematoma, malpositioned implant, DVT without PE, device failure, persistent donor-site pain, other) Yes/No Deep wound infection b Post-intervention deep/subfascial wound infection Yes/No Pulmonary embolus c PE diagnosed by radiologic study after the intervention Yes/No Rehospitalization Were you admitted to an acute care facility as an in-patient within 30 days from the date of your intervention for ANY reason (do not include admissions to rehabilitation hospital or nursing home)? Yes/No, date(s) Time frame for capture Reported by While in-house for procedure Provider While in-house for procedure and again on next patient follow up questionnaire Provider reports if occuring inhouse, otherwise Patient reports at next follow up Next patient follow up questionnaire Patient At time of reoperation Provider Need for reoperation a b c (if yes, specify cause)3 Second or multiple performed interventions caused by complications after index surgery, not planned in advance Hardware removal Removal of implants: e.g. screws, rods Yes/No Symptomatic non-union Pain related to failure of bony consolidation of bridge/union at minimum 12 months after surgery Yes/No Neuro-compression Compression of neural structures with or without neurological deficits Yes/No Postoperative infection Superficial or deep (subfascial) wound/tissue infection after surgery Yes/No Implant malposition Incorrect position of the implant Yes/No Implant failure Problem due to an implant e.g., loosening, breakage Yes/No Wrong site surgery Unintentional intervention on the wrong level/site, not on level of main pathology Yes/No Sagittal imbalance Sagittal malalignment of the spine Yes/No CSF leakage Including CSF fistula, pseudomeningocele, etc. Yes/No Epidural hematoma Bleeding hematoma outside dural sac but inside bony spinal canal (with or without neuro-compression) Yes/No Other State reason for reoperation Complication definitions modified from Spine Tango registry Definition provided is designed for providers, a modified definition will be included on patient questionnaires Reoperation definition and definitions for causes of reoperation modified from Spine Tango registry Proposed set of metrics for LBP 123 Table 6.3 Risk factors and Initial conditions Categories and metrics Definition/wording Answer options Timeframe for capture Reported by Date of birth dd/mm/yyyy Baseline Patient Male/female Baseline Patient Please indicate your highest level of schooling completed* None, primary, secondary, tertiary Baseline Patient Symptoms Back pain dominant (acute), leg pain dominant (acute) back pain = leg pain (acute), back pain dominant (chronic), leg pain dominant (chronic) back pain = leg pain (chronic), neurogenic claudication, cauda equina syndrome Baseline and at time of any intervention Provider Structural pathology No study or interpretation available, age appropriate, disc pathology with normal height, disc space collapse, spondylolysis/ spondylolisthesis, scoliosis/kyphosis, facet pathology, non-union Compressive pathology No study or interpretation available, no clinically relevant compression, central compression, lateral compression, combined central & lateral compression, recurrent compression following surgery at the same level How would you rate your average back pain over the last week? 1-10, as per Table 1 How would you rate your average leg pain over the last week? 1-10, as per Table 1 How long have you had your current back pain? I don't have back pain, < 3 months, 3-12 months, 1-2 years, >2 years How long have you had pain radiating to your leg(s)? I don't have pain radiating to my legs, < 3 months, 3-12 months, 1-2 years, >2 years Smoking habits Do you smoke? BMI Indicate the patient's height Indicate the patient's mass Measured in kg Comorbidities Indicate if you have been diagnosed with each of the following conditions ASA score (surgical patients only) Physical Status Classification System Demographics Age Gender Education level Baseline clinical status Glassman criteria Pain Duration of symptoms Surgical indication 06 Baseline Patient Baseline Patient Yes/no Baseline Patient Measured in cm Baseline Provider Heart disease, hypertension, poor circulation, lung disease, diabetes, kidney disease, liver disease, nervous system disease, cancer, depression, arthritis, peptic ulcer disease, hemiplegia/paraplegia, AIDS Baseline Patient 1. healthy, 2. mild/moderate, 3. severe, 4. life threatening, 5. moribund, unknown At time of operation Provider Paramedian disc herniation, central disc herniation, central spinal stenosis with degenerative listhesis, central spinal stenosis without degenerative listhesis, lateral spinal stenosis, isthmic spondylolysis/spondylolisthesis, segmental pain (with or without degenerative listhesis, degenerative scoliosis, other At time of operation Provider 124 Proposed set of metrics for LBP Baseline functional status Disability ODI As per Table 1 Baseline Patient Quality of Life EQ-5D-3L As per Table 1 Baseline Patient Work status What is your current work status? As per Table 1 Baseline Patient Are you working at a physically less demanding job now because of your back and/or leg pain? As per Table 1 Are you currently on sick leave from work? Yes, full time for my back problems, Yes parttime for my back problems, Yes due to another disease, No Baseline Patient If yes, for how long? 1 week or less, 1-4 weeks, 1-3 months, 3-6 months, 6-9 months, 9-12 months, 1-2 years, > 2 years Surgery Please specify any prior procedure(s) and the level(s) by ticking one or several of the boxes below See Figure 3 Baseline Patient Injection therapy Have you previously received spinal injections for your current symptoms? (e.g. epidurals, specific nerve root injections, facet injections or discograms) Yes/no Baseline Patient Duration of sick leave (if applicable) 06 Prior treatment *Level of schooling using culture-specific definitions per ISCED (International Standard of Schooling Classification, UNESCO) [40] Discussion We present a standard set of clinical outcome metrics for the use in clinical practice for assessing the management of degenerative low back conditions based on the existing literature and on international expert opinion. The set includes patient-reported information on physical function, HRQOL, pain and work status as well as complications of treatment and baseline characteristics to facilitate risk adjustment. Several registries of spine patients already exist, tracking tens of thousands of patients in numerous countries [10-12]. While these undertakings have been beneficial in many regards, including providing descriptive information about spine care at a population level and answering research questions involving comparisons of various interventions, broader international comparisons have been limited because each existing registry has developed its own metrics for gauging outcomes and definitions for categorizing specific diseases and associated risk factors. Furthermore, registries often do not capture the complete patient population for various diseases because most, but not all [62], spine registries do not follow LBP patients who are managed non-operatively. This limitation precludes complete comparisons of all available treatment options. Moreover, existing registries often do not capture the entire cycle of care but instead tend to focus on the course of surgical care. Proposed set of metrics for LBP The proposal we present aims to overcome these shortcomings by establishing a standard terminology for measuring outcomes in LBP patients, largely based on well-validated tools that are available in numerous languages. This specific outcome set is particularly well suited to facilitating meaningful comparisons between providers, because it stratifies patients by disease and includes the entire patient population associated with a given diagnosis throughout the full course of their care. Several recommendations have previously been published for standardized outcome measurement in low back pain research, but not specifically for use in everyday clinical practice [14,15,63]. Most recently, a research task force chartered by the National Institute of Health (NIH) Pain Consortium described an outcome set for use in chronic LBP research centered on patient-reported outcomes and largely relying on the Patient Reported Outcome Measurement Information System (PROMIS) instrument [63]. While there is substantial overlap in the domains chosen by our working group and those selected by the NIH task force, the work of the latter is not adequately comprehensive to launch into clinical practice as it leaves several decisions to the discretion of future researchers, such as the timeline of patient follow up, the specific adverse events to be recorded, and even which PROMs tools should be used. Furthermore, while the PROMIS instrument offers great potential efficiency through computer adaptive testing and may eventually become favorable to the legacy measures recommended here, it is not yet broadly translated and validated beyond English [64] and is therefore not ready for international use. A similar effort is currently being conducted by the ‘International Steering Committee for the Core Outcome Set for Low Back Pain’ [16]. Initial findings presented at the ‘Core Outcome Measures in Effectiveness Trials’ (COMET) meeting in November 2014, prioritized 3 domains identical to those chosen by our working group: physical function, pain intensity, and HRQOL, with work ability ranked fourth. While useful for guiding researchers who are developing LBP outcome measures, these recommendations are not detailed enough for use in clinical practice. Another commendable effort was previously described as part of the Multinational Musculoskeletal Inception Cohort Study Collaboration (MMICS), which again showed substantial overlap with the domains and variables chosen by our working group [15]. While reasonable for research efforts, the MMICS outcome set and especially the associated timeline for collecting data would be overly burdensome for ongoing use in non-research settings. With the working group’s goal fulfilled, including a complete set of outcomes and associated data defined; the focus can be shifted to implementation. For providers, group practices, and registries that ascribe to the benefits of outcome measurement, this set will be available for voluntary adoption. Practices with existing data collection processes may be able to incorporate the standard set into ongoing workflows in ways that will minimize additional work after an initial learning curve. Other organizations may need to begin by establishing an infrastructure for prospective data collection. ICHOM is committed to facilitating broad adoption of this set and has made the full recommendations of this group freely available on its website, along with a reference guide to assist with technical aspects of implementation [65]. Looking forward, revisions to this outcome set will be needed. For example, computerized adaptive testing may provide efficiency gains in PROM collection as software progresses [66]. ICHOM and representatives from the working group plan to actively monitor use of this set through a steering committee comprised of representatives from several existing outcome measurement efforts. Their work will involve communication with users, including collection of direct feedback. The frustrations and innovations of providers employing this set will be 125 06 126 Proposed set of metrics for LBP crucial to its improvement, and structured revisions to the set will be reviewed on an annual basis. The steering committee will also be available to communicate with relevant third parties. For example, some instruments recommended in this outcome set, such as the EQ-5D, are proprietary and continued inclusion in the set may be dependent on future negotiated agreements. 06 Our work has a number of limitations that should be mentioned. Firstly, the proposed outcome set remains untested, and while it is largely based on existing tools and familiar data points, the specifics of survey circulation and the associated timeframes for data collection and reporting will inevitably lead to bumps in the road. Secondly, despite our best efforts to generate diverse international consensus, our outcome set surely is not equally applicable to all cultures. Much work in linguistic and cross-cultural validation remains. Still, we feel that our work represents a sufficient and important starting point. Thirdly, much work remains to be done on the practical issues of compiling and analyzing data, ultimately building robust risk-adjustment models with appropriate quality assurance to produce reports that accurately reflect provider performance while simultaneously protecting patient privacy. This will be particularly important if value-based reimbursement does indeed come to fruition. For example, in Sweden there are ongoing efforts in conjunction with the Swespine Registry to link reimbursement levels to postoperative patient-reported outcomes. ICHOM intends to continue its facilitative role to guide development of such models and their inclusion in quality reporting initiatives. In summary, the members of the working group feel that the introduction of this set of outcomes for the treatment of degenerative low back pathology is an essential step toward an international spine community that routinely measures and reports its performance in common and meaningful ways. We invite all providers caring for patients with low back pain to join us in measuring this set; the full list of metrics, contact information, and other resources to facilitate implementation are available on the ICHOM website [65]. Proposed set of metrics for LBP 127 References 1. Porter ME, Teisberg EO. Redefining health care; creating positive-sum competition to deliver value. Boston, Mass.: Harvard Business School Press; 2005 2. Institute of Medicine. Performance measurement: accelerating improvement. Washington DC: National Academies Press; 2006 3. Porter ME, Baron JF, Chacko JM, Tang RJ. The UCLA Medical Center: kidney transplantation. Harvard Business School Case 711-410 2010 4. Porter ME, Teisberg EO. Redefining competition in health care. Harv.Bus.Rev. 2004;82:64-76, 136 5. Porter ME. A strategy for health care reform--toward a value-based system. N.Engl.J.Med. 2009;361:109-112 6. McGirt MJ, Resnick D, Edwards N, Angevine P, Mroz T, Fehlings M. Background to understanding value-based surgical spine care. Spine (Phila Pa 1976.) 2014;39:S51-S52 7. McGirt MJ, Parker SL, Asher AL, Norvell D, Sherry N, Devin CJ. Role of prospective registries in defining the value and effectiveness of spine care. Spine (Phila Pa 1976.) 2014;39:S117-S128 8. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2197-2223 9. Longo UG, Loppini M, Denaro L, Maffulli N, Denaro V. Rating scales for low back pain. Br.Med.Bull. 2010;94:81144 10. Roder C, Chavanne A, Mannion AF, Grob D, Aebi M. SSE Spine Tango--content, workflow, set-up. www. eurospine.org-Spine Tango. Eur.Spine J. 2005;14:920-924 11. McGirt MJ, Speroff T, Dittus RS, Harrell FE, Jr., Asher AL. The National Neurosurgery Quality and Outcomes Database (N2QOD): general overview and pilot-year project description. Neurosurg.Focus. 2013;34:E6 12. Stromqvist B, Fritzell P, Hagg O, Jonsson B, Sanden B. Swespine: the Swedish spine register : the 2012 report. Eur.Spine J. 2013;22:953-974 13. van Hooff ML, Jacobs WC, Willems PC, Wouters MW, de Kleuver M, Peul WC et al. Evidence and practice in spine registries. Acta Orthop. 2015 [In press] 14. Deyo RA, Battie M, Beurskens AJ, Bombardier C, Croft P, Koes B et al. Outcome measures for low back pain research. A proposal for standardized use. Spine (Phila Pa 1976.) 1998;23:2003-2013 15. Pincus T, Santos R, Breen A, Burton AK, Underwood M. A review and proposal for a core set of factors for prospective cohorts in low back pain: a consensus statement. Arthritis Rheum. 2008;59:14-24 16. Chiarotto A, Terwee CB, Deyo RA, Boers M, Lin CW, Buchbinder R et al. A core outcome set for clinical trials on non-specific low back pain: study protocol for the development of a core domain set. Trials 2014;15:511 17. ICHOM Website. ICHOM - International Consortium for Health Outcomes Measurement - Who We Are [Internet] 2014; [cited 2014 Dec 22]. Available from: http://www.ichom.org/who-we-are/ 18. Pill J. The Delphi Method: Substance, context, a critique and an annotated bibliography. 2nd ed. 1971; p.57-71 19. Chapman JR, Norvell DC, Hermsmeyer JT, Bransford RJ, DeVine J, McGirt MJ et al. Evaluating common outcomes for measuring treatment success for chronic low back pain. Spine (Phila Pa 1976.) 2011;36:S54-S68 20. Cleland JA, Whitman JM, Houser JL, Wainner RS, Childs JD. Psychometric properties of selected tests in patients with lumbar spinal stenosis. Spine J. 2012;12:921-931 21. Jensen MP, Mardekian J, Lakshminarayanan M, Boye ME. Validity of 24-h recall ratings of pain severity: biasing effects of "Peak" and "End" pain. Pain 2008;137:422-427 22. Andersson G. The Epidemiology of Spinal Disorders. The Adult Spine; Principles and Practice, Frymoyer JW. Philadelphia, PA: Lippincott-Raven Publishers; 1997; p.93-141 23. Jamison RN, Raymond SA, Slawsby EA, McHugo GJ, Baird JC. Pain assessment in patients with low back pain: comparison of weekly recall and momentary electronic data. J.Pain 2006;7:192-199 06 128 Proposed set of metrics for LBP 24. Bolton JE, Humphreys BK, van Hedel HJ. Validity of weekly recall ratings of average pain intensity in neck pain patients. J.Manipulative Physiol Ther. 2010;33:612-617 25. PubMed. PubMed Search for "COMI, Back Pain, 2001-2011"; 2013 26. Roland M, Morris R. A study of the natural history of back pain. Part I: development of a reliable and sensitive measure of disability in low-back pain. Spine (Phila Pa 1976.) 1983;8:141-144 27. Mannion AF, Porchet F, Kleinstuck FS, Lattig F, Jeszenszky D, Bartanusz V et al. The quality of spine surgery from the patient's perspective. Part 1: the Core Outcome Measures Index in clinical practice. Eur.Spine J. 2009;18 Suppl 3:367-373 28. Bergner M, Bobbitt RA, Pollard WE, Martin DP, Gilson BS. The sickness impact profile: validation of a health status measure. Med.Care 1976;14:57-67 29. Hunt SM, McKenna SP, McEwen J, Backett EM, Williams J, Papp E. A quantitative approach to perceived health status: a validation study. J.Epidemiol.Community Health 1980;34:281-286 30. Ware JE, Jr.Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and 06 item selection. Med.Care 1992;30:473-483 31. Brooks R. EuroQol: the current state of play. Health Policy 1996;37:53-72 32. Hung M, Hon SD, Franklin JD, Kendall RW, Lawrence BD, Neese A et al. Psychometric properties of the PROMIS physical function item bank in patients with spinal disorders. Spine (Phila Pa 1976.) 2014;39:158-163 33. Hinz A, Kohlmann T, Stobel-Richter Y, Zenger M, Brahler E. The quality of life questionnaire EQ-5D-5L: psychometric properties and normative values for the general German population. Qual.Life Res. 2014;23:443447 34. Kim TH, Jo MW, Lee SI, Kim SH, Chung SM. Psychometric properties of the EQ-5D-5L in the general population of South Korea. Qual.Life Res. 2013;22:2245-2253 35. Mueller B, Carreon LY, Glassman SD. Comparison of the EuroQOL-5D with the Oswestry Disability Index, back and leg pain scores in patients with degenerative lumbar spine pathology. Spine (Phila Pa 1976.) 2013;38:757761 36. EuroQol Website. EuroQol - Home [Internet]. 2013 [cited 2013 Nov 25]. Available from: http://www.euroqol. org/ home.html 37. Ware JE, Jr., Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med.Care 1992;30:473-483 38. SF36 Website. The SF Community - offering information and discussion on health outcomes [Internet]. 2013 [cited 2013 Nov 25]. Available from: http://www.sf-36.org/ 39. Report to Congress. Promoting Greater Efficiency in Medicare: Chapter 5 [Internet]. 2007 [cited 2013 Nov 16]. Available from: http://www.medpac.gov/chapters/jun07_ch05.pdf 40. Axon RN, Williams MV. Hospital readmission as an accountability measure. JAMA 2011;305:504-505 41. UNESCO. United Nations Educational Scientific and Cultural Organization. ISCED: International Standard Classification of Education [Internet]. 2013 [cited 2013 Nov 16]. Available from: http://www.uis.unesco.org/ Education/Pages/International-standard-classification-of-education.aspx 42. Glassman SD, Carreon LY, Anderson PA, Resnick DK. A diagnostic classification for lumbar spine registry development. Spine J. 2011;11:1108-1116 43. Bo M, Cacello E, Ghiggia F, Corsinovi L, Bosco F. Predictive factors of clinical outcome in older surgical patients. Arch.Gerontol.Geriatr. 2007;44:215-224 44. Schoenfeld AJ, Carey PA, Cleveland AW, III, Bader JO, Bono CM. Patient factors, comorbidities, and surgical characteristics that increase mortality and complication risk after spinal arthrodesis: a prognostic study based on 5,887 patients. Spine J. 2013;13:1171-1179 45. Tabouret E, Cauvin C, Fuentes S, Esterni B, Adetchessi T, Salem N et al. Reassessment of scoring systems and prognostic factors for metastatic spinal cord compression. Spine J. 2015;15:944-950 Proposed set of metrics for LBP 129 46. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J.Chronic.Dis. 1987;40:373-383 47. Bayliss EA, Ellis JL, Steiner JF. Subjective assessments of comorbidity correlate with quality of life health outcomes: initial validation of a comorbidity assessment instrument. Health Qual.Life Outcomes. 2005;3:51 48. Chaudhry S, Jin L, Meltzer D. Use of a self-report-generated Charlson Comorbidity Index for predicting mortality. Med.Care 2005;43:607-615 49. Department of Health. Patient Reported Outcome Measures (PROMs) in England: A Methodology for Applying Casemix Adjustment, Annex C: Coefficients for Hip Replacement Models [Internet]. 2012 [cited 2013 Nov 25]. Available from: https://www.gov.uk/government/uploads/system/uploads/ attachment_data/file/216510/ dh_133452.pdf 50. Jenkins LT, Jones AL, Harms JJ. Prognostic factors in lumbar spinal fusion. Contemp.Orthop. 1994;29:173-180 51. Shimia M, Babaei-Ghazani A, Sadat BE, Habibi B, Habibzadeh A. Risk factors of recurrent lumbar disk herniation. Asian J.Neurosurg. 2013;8:93-96 52. Papavero L, Thiel M, Fritzsche E, Kunze C, Westphal M, Kothe R. Lumbar spinal stenosis: prognostic factors for bilateral microsurgical decompression using a unilateral approach. Neurosurgery 2009;65:182-187 53. Rihn JA, Radcliff K, Hilibrand AS, Anderson DT, Zhao W, Lurie J et al. Does obesity affect outcomes of treatment for lumbar stenosis and degenerative spondylolisthesis? Analysis of the Spine Patient Outcomes Research Trial (SPORT). Spine (Phila Pa 1976.) 2012;37:1933-1946 54. Rihn JA, Kurd M, Hilibrand AS, Lurie J, Zhao W, Albert T et al. The influence of obesity on the outcome of treatment of lumbar disc herniation: analysis of the Spine Patient Outcomes Research Trial (SPORT). J.Bone Joint Surg.Am. 2013;95:1-8 55. Trief PM, Ploutz-Snyder R, Fredrickson BE. Emotional health predicts pain and function after fusion: a prospective multicenter study. Spine (Phila Pa 1976.) 2006;31:823-830 56. Celestin J, Edwards RR, Jamison RN. Pretreatment psychosocial variables as predictors of outcomes following lumbar surgery and spinal cord stimulation: a systematic review and literature synthesis. Pain Med. 2009;10:639-653 57. Daubs MD, Norvell DC, McGuire R, Molinari R, Hermsmeyer JT, Fourney DR et al. Fusion versus nonoperative care for chronic low back pain: do psychological factors affect outcomes? Spine (Phila Pa 1976.) 2011;36:S96109 58. Herno A. Surgical results of lumbar spinal stenosis. Ann.Chir Gynaecol.Suppl 1995;210:1-969 59. Lee JC, Kim MS, Shin BJ. An analysis of the prognostic factors affecing the clinical outcomes of conventional lumbar open discectomy : clinical and radiological prognostic factors. Asian Spine J. 2010;4:23-31 60. MacVicar J, King W, Landers MH, Bogduk N. The effectiveness of lumbar transforaminal injection of steroids: a comprehensive review with systematic analysis of the published data. Pain Med. 2013;14:14-28 61. Mandel S, Schilling J, Peterson E, Rao DS, Sanders W. A retrospective analysis of vertebral body fractures following epidural steroid injections. J.Bone Joint Surg.Am. 2013;95:961-964 62. Kessler JT, Melloh M, Zweig T, Aghayev E, Roder C. Development of a documentation instrument for the conservative treatment of spinal disorders in the International Spine Registry, Spine Tango. Eur.Spine J. 2011;20:369-379 63. Deyo RA, Dworkin SF, Amtmann D, Andersson G, Borenstein D, Carragee E et al. Focus article: report of the NIH Task Force on Research Standards for Chronic Low Back Pain. Eur.Spine J. 2014;23:2028-2045 64. NIH PROMIS Website. PROMIS: Translations [Internet]. 2014 [cited 2014 Dec 12]. Available from: http://www. nihpromis.org/measures/translations 65. ICHOM Website. ICHOM - International Consortium for Health Outcomes Measurement - Low Back Pain [Internet]. 2014 [cited 2014 Dec 21]. Available from: http://www.ichom.org/project/low-back-pain/ 66. Fries JF, Bruce B, Cella D. The promise of PROMIS: using item response theory to improve assessment of patient-reported outcomes. Clin.Exp.Rheumatol. 2005;23:S53-S57 06 130 Guest editorial Guest editorial: Spinal disorders, quality-based healthcare and spinal registers Acta Orthop. 2015; 86 (5): 521–522 Jeremy Fairbank Professor of Spine Surgery, University of Oxford, UK 06 In this issue of Acta Orthopaedica 2 articles [1,2] represent waymarks on a road towards a conception of universal value-based healthcare for spine. This goal is expected to be a convergence of the interests of patients, payers, politicians and clinicians. The articles are focused on the painful lumbar spine, which represent the top ranking chronic healthcare complaint [3]. Clinicians’ interest in quality dates back to Florence Nightingale in the Crimea. For patients it goes back to time immemorial. For economists it has become an issue in the last 2 decades. All politicians should be interested in this topic, and this is kindled by public dissatisfaction and the rising costs of healthcare. ICHOM (International Consortium of Health Outcomes Measurement, www.ichom.org/) is an organisation recently founded by the Institute for Strategy and Competitiveness at Harvard Business School, the Boston Consulting Group, and the Karolinska Institute to enable the shift towards value-based health care. The concept of value-based health care has been described by Michael Porter, a co-founder of ICHOM, as the only strategy that will fix health care [4]. The central point of his work is the need to look at outcomes and cost together in driving clinical improvement and policy. Health registers play a central role in the measurement of outcomes and thereby enable the shift towards a value-based system: the Swedish Knee Arthroplasty Register (start 1975) (www.knee.se) and the Swedish Hip Arthroplasty Register (start 1979) (www.shpr.se), being the first and most notable in the musculoskeletal world. In 2012 ICHOM identified 4 areas of healthcare to define international standard sets of outcome measures. One of these was low back pain, and one output of this process is the paper by Clement et al. [1]. There have been earlier attempts to identify optimum outcome measures for research [5,6], but this is the first to search for an international consensus on quality measures for use in daily clinical practice. ICHOM is in the process of extending this exercise, which involves a defined methodology, to many other areas of healthcare: it has now completed 12 ‘standard sets’ (of outcome measures), and plans to have 50 completed by 2017. Since the establishment of the Swedish Spine Register (SweSpine, www.swespine.se) in 1992, spine registers have sprung up in other countries. The systematic review by van Hooff et al. [2] is a first attempt to see if these registers are influencing quality. Whilst the case remains unproven, they are able to cite a number of examples where it would appear that a register has altered behaviour and improved quality. I do not doubt that, as this movement evolves, so we shall see better evidence of impact. This can only be good from the patient perspective, but Registers do not yet to have the capacity to answer the universal question as to which doctor is likely to deliver an individual the optimum health care. The register should be able to confirm whether a given provider is an outlier nationally (and if ICHOM is successful) internationally. The ambitions of ICHOM are much grander: it is their intention that health care systems should reimburse providers on the basis of quality rather than quantity. This is beginning to happen Guest editorial 131 in Sweden, but I believe it will be some time before such systems evolve in other healthcare economies. There are early signs of this movement developing in my country. There is devil in the detail. Table 5.3 in van Hooff et al. gives a detailed list of recommendations for enhancing spine registers. Register models can only work with patient involvement and particularly the completion of follow-up questionnaires has to be high. Van Hooff et al. recommend 60 to 80% compliance. The reported completion rate from the National British Spine Register is only 20%. Subtle ‘sticks and carrots’ need to be developed to encourage patient completion. It means the forms have to be a short and clear. More qualitative research is needed to explore these issues. Consideration may be needed to offering adequate reimbursement for the time and trouble of completing outcome measures [7]. The outcome measures themselves require care and attention. Most have evolved since the 1970s. Some like EQ-5D, are managed by committees. Others like ODI, are managed by their original authors. Others, such as NRS pain, are essentially orphans since their original description by Huskisson in the 1970’s [8-10]. ICHOM has a duty to ensure that the measures they recommended are looked after and not subjected to alterations. It is vital that they are translated accurately by a standard protocol. ODI, for example, recommended as part of the ICHOM low back pain outcome set, is now licenced to the MAPI Trust in Lyon, which has pioneered good practice in this regard (www.proqolid.org/instruments/oswestry _disability_ index_odi). ICHOM should consider sponsoring orphan instruments (perhaps at MAPI) such as NRS to ensure that they are delivered in as consistent a way as possible. It must be noted that there are problems with registers and the interpretation of their data. Most are owned and managed by clinicians. So long as this model is transparent, it probably keeps the data safer from meddling than it would be in the hands of governments, who also serve as purchasers. Registers need funding, which also generates problems of long term viability and conflicts of interest [2]. Properly defining criteria of success and failure is an important challenge. The Swedish Knee and Hip Registers and its various international descendants have used revision rates as an important criterion of failure. When this is applied to the knee register, unicompartmental knee replacements are shown to have higher revision rates than total knee replacement, arguably not because they actually fail more frequently, but because surgeons are more ready and willing to revise a unicompartmental knee replacement than a total knee replacement [11]. Unsurprisingly this view is disputed by the directors of knee registers [12], but some complex issues of quality and cost are involved here. I see a parallel situation in the use of interspinous spacers to treat spinal stenosis. 2 RCT’s have shown similar outcomes between spacers and conventional decompressive surgery but with higher revision rates in the spacer group [13,14]. Both these examples are significant because the initial implant cost is high, but they do reflect the important point that defining outcome metrics is potentially treacherous. This matters in many ways, but particularly when reimbursement depends on it. Rigorous metrics defined based on established methodologies, arguably such as those presented in the ICHOM standard set, should present an answer to this challenge. Those registers that use mortality as an outcome, such as cardiac surgery, may make cardiac surgeons more risk averse so that they avoid high-risk patients [15,16]. Statistical variation becomes a major problem when these results are distilled down to individual surgeon data. 06 132 Guest editorial This has provoked problems previously in the US and currently in the UK where SSMD (Surgeon Specific Mortality Data) has recently become mandated. Discussion on this continues [15-19]. The quality movement in healthcare needs the strong support of everyone involved. We need the education and involvement of our public and politicians to make this happen. As van Hooff et al. spell out, registers are important, and need good methodology and design, with care and attention to deliver quality data. How these data are interpreted and presented will need continual scrutiny and innovation as their importance in the health economy increases. Conflict of interest Jeremy Fairbank is coauthor of the Clement et al. paper, and is a copyright holder of the Oswestry Disability Index 06 Guest editorial 133 References 1. Clement RC, Welander A, Stowell C, Cha TD, Chen JL, Davies M et al. A proposed set of metrics for standardized outcome reporting in the management of low back pain. Acta Orthop. 2015;86:523-533 2. van Hooff ML, Jacobs WC, Willems PC, Wouters MW, de Kleuver M, Peul WC et al. Evidence and practice in spine registries. Acta Orthop. 2015;86:534-544 3. Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, Ezzati M et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2163-2196 4. 5. Porter M, Lee T. The strategy that will fix health care. Harvard Business Review 2013;91:50-70 Deyo RA, Battie M, Beurskens AJ, Bombardier C, Croft P, Koes B et al. Outcome measures for low back pain research. A proposal for standardized use. Spine (Phila Pa 1976.) 1998;23:2003-2013 6. Deyo RA, Dworkin SF, Amtmann D, Andersson G, Borenstein D, Carragee E et al. Report of the NIH task force on research standards for chronic low back pain. Spine (Phila Pa 1976.) 2014;39:1128-1143 7. Williams CM, Maher CG, Hancock MJ, McAuley JH, Lin CW, Latimer J. Recruitment rate for a clinical trial was associated with particular operational procedures and clinician characteristics. J Clin.Epidemiol. 2014;67:169-175 8. Huskisson EC. Measurement of pain. Lancet 1974;2:1127-1131 9. Scott J, Huskisson EC. Graphic representation of pain. Pain 1976;2:175-184 10. Scott JHuskisson EC. Vertical or horizontal visual analogue scales. Ann.Rheum.Dis. 1979;38:560 11. Goodfellow JW, O'Connor JJ, Murray DW. A critique of revision rate as an outcome measure: re-interpretation of knee joint registry data. J.Bone Joint Surg.Br. 2010;92:1628-1631 12. Robertsson O, Graves S, Hooper G, Lidgren L, Rothwell A, de Steiger R. Letter: A critique on revision rate as an outcome measure (A critique on revision rate as an outcome measure: Re-interpretation of knee joint registry data. J.W. Goodfellow, J.J. O'Connor, and D.W. Murray. J Bone Joint Surg [Br] December 2010 92B:1628-1631). J Bone Joint Surg [Br] 2011;93-B 13. Moojen WA, Arts MP, Jacobs WC, van Zwet EW, van den Akker-van Marle ME, Koes BW et al. Interspinous process device versus standard conventional surgical decompression for lumbar spinal stenosis: randomized controlled trial. BMJ 2013;347:f6415 14. Stromqvist BH, Berg S, Gerdhem P, Johnsson R, Moller A, Sahlstrand T et al. X-stop versus decompressive surgery for lumbar neurogenic intermittent claudication: randomized controlled trial with 2-year followup. Spine (Phila Pa 1976.) 2013;38:1436-1442 15. Westaby S, Baig K, Pepper J. Publishing SSMD: the risks outweigh the benefits. Bulletin of the Royal College of Surgeons of England 2015;97:155-9 16. Westaby S, Baig K, Pepper J. Publishing SSMD: and another thing. Bulletin of the Royal College of Surgeons of England 2015;97:212-3 17. Bridgewater B. Patient-facing data is essential in the digital era. Bulletin of the Royal College of Surgeons of England 2015;97:160-3 18. Gottlieb G. Outcomes data: the road to expansion and enhancement. Bulletin of the Royal College of Surgeons of England 2015;97:214-5 19. MacFie J. FSSA statement on individual surgeons' outcomes data. Bulletin of the Royal College of Surgeons of England 2015;97:214-5 06 134 06 135 Chapter 07 The Oswestry Disability Index (Version 2.1a) Validation of a Dutch language version van Hooff ML Spruit M Fairbank JCT van Limbeek J Jacobs WCH Published in: Spine 2015;40(2):E83-90 136 Abstract Study design: A cross-sectional study on baseline data. Objective: To translate the ODI version 2.1a into the Dutch language and to validate its use in a cohort of chronic low back pain patients in secondary spine care. Summary of Background data: Patient-reported outcome measures (PROMs) are commonly accepted to evaluate the outcome of spine interventions. Functional status is an important outcome in spine research. The ODI is a recommended condition-specific PROM used to evaluate functional status in patients with back pain. As yet, no formal translated Dutch version exists. Methods: The ODI was translated according to established guidelines. The final version was built into the electronic web-based system in addition with the Roland & Morris Disability Questionnaire (RMDQ), the NRS for pain severity, SF36 for quality of life, and the Hospital Anxiety and Depression Scale (HADS). Baseline data were used of 244 patients with chronic low back pain who participated in a combined physical and psychological program. Floor and ceiling effects, internal consistency, and the construct validity were evaluated using quality criteria. Results: The mean ODI (standard deviation [SD]) was 39.6 (12.3); minimum 6, maximum 70. Most of the participants (88%) were moderately to severely disabled. Factor analysis determined a one-factor structure (36% explained variance) and the homogeneity of ODI items is shown (Cronbach’s α= 0.79). The construct validity is supported as all (6:6) the a priori hypotheses were confirmed. Moreover, the ODI and RMDQ, showed a strong significant correlation (r= 0.68, p< 0.001) and an overlap: mean difference of -18 (95% limits of agreement: -44 to 8). Conclusions: The Dutch ODI version 2.1a is a valid and valuable tool for the measurement of functional status and disability among Dutch patients with CLBP. This translated conditionspecific PROM-version is recommended for use in future back pain research and to evaluate outcome of back care in the Netherlands. Validation of Dutch ODI v2.1a 137 Introduction Low Back Pain (LBP) has the largest global burden of disease[1] and is associated with a substantial amount of morbidity. Functional status is an important outcome in research evaluating surgical and non-surgical interventions for LBP[2,3] and to assess patients’ progress in routine clinical practice. Limitations in functional status (disability) as assessed by patientreported outcome measures (PROMs) is widely accepted[4] and the Oswestry Disability Index (ODI version 2.1a)[5,6] is commonly used as a PROM. The ODI has been translated into various languages[7-22]. Recently, based on international consensus, the ODI is included as a PROM in the International Consortium for Health Outcomes Measurement (ICHOM) standard set for LBP[3,23]. Prior to its use, the questionnaire should be translated to the native language of users and should have undergone a cross-cultural adaptation and validation process. A multistep approach is recommended in order to reach equivalence between the original and the target language[24,25]. A systematic search of literature and a discussion with the initial developer of the ODI revealed that, as yet no formal Dutch version 2.1a exists. The objectives of this study were: (1) to translate and adapt the ODI version 2.1a into the Dutch language and (2) to investigate the validity and internal consistency of the translation in a cohort of chronic low back pain (CLBP). Materials and Methods Patients Orthopaedic spine surgeons of a specialized hospital for spine care recruited patients with CLBP. The patients were referred to and participated in a short, intensive two-week Combined Physical and Psychological (CPP) program, as provided by RealHealthNL Sint Maartenskliniek Nijmegen (Zevenheuvelenweg, Berg en Dal, The Netherlands)[26]. Translation and cross-cultural adaptation procedure We used a multistep approach [24,25] using published guidelines [24,27] recommended by the American Academy of Orthopedic Surgeons Outcomes Committee. The first step was a forward translation performed by two native Dutch speakers (T-1, T-2) with a background in health sciences, and familiar with the concepts being examined. A third native Dutch speaker (T-3) without a medical education assisted in the translation with an appreciation of the level of understanding of the typical patient that would ultimately be completing the questionnaire (naïve translator). The second step was a comparison of the translation with the original and consensus was reached in case any discrepancies existed. The versions were synthesised to one common Dutch version; T-123. The third step was a back translation of T-123 by two native English speakers (one American and one British), both bilingual with Dutch as their second language. One was familiar with the concepts of disability and the other was a professional translator in the medical field. Both carried out their back translation independently (BT-1, BT-2). The fourth step was to develop a pre-final Dutch version by an expert committee with access to all translations. The expert committee consisted of all translators, an orthopaedic spine surgeon, and an independent methodologist. The committee examined the translations 07 138 Validation of Dutch ODI v2.1a and discussed all parts of the questionnaire (instructions, items, and response options). The fifth step was to test the pre-final version in a group of 10 patients with CLBP. They were asked to complete this version and to give their comments. All findings of this phase of the adaptation process (face validity) were evaluated by the expert committee before the final Dutch version (ODI version 2.1a) was compiled and further tested for validity. Evaluation of the psychometric properties of the Dutch ODI version 2.1a Procedure and Data collection The Dutch ODI version 2.1a was built into the electronic web-based system of the CPP program, together with all other questionnaires. Outcome assessment is a routine part of the program and patients are systematically followed over time[28]. Therefore, we were able to extract all relevant pretreatment data from the database. All measures, except the ODI, have previously been validated in CLBP samples. 07 Self-report measures Oswestry Disability Index (ODI, version 2.1a) The ODI was originally developed in 1980 (version 1)[5] and slightly modified in 1989 [29] to the version 2.1, which is regularly used today[30]. A single word change in the introductory statement has led to the 2.1a version [31]. This ODI version measures the impact of LBP on the patients’ functional ability in 10 domains of activities in daily life (‘personal care’, ‘lifting’, ‘walking’, ‘sitting’, ‘standing’, ‘sleeping’, ‘sex life’, ‘social life’, and ‘travelling’). Each domain or item consists of 6 statements (scores 0-5) and is scored by the patient in reference to his/her current functional status. The total score is a sum of all completed items and expressed as a percentage of the maximum score[6]. To avoid confusion with percentages as mathematical expressions the percentage is referred to as a total score, ranging from 0 to 100, with higher scores indicating higher disability [11]. To validate the Dutch ODI we used several measures: (1) Roland & Morris Disability Questionnaire (RMDQ; score 0-24)[32], with higher scores indicating higher disability; (2) Numeric Rating Scale (NRSpain; score 0-100)[33] with higher scores indicating higher levels of pain severity; (3) Health-related quality of life as measured with the Short-Form 36 Health Survey Questionnaire (SF-36)[34], consisting of 2 summary scores of eight subscales: Physical and Mental Component Score (both scores 0-100; and (4) Hospital Anxiety and Depression Scale (HADS) containing 2 subscales to screen for disturbance of symptoms of anxiety and depression (HADS-A and HADS-D; both scores 0-21)[35]. Procedure and criteria ‘Floor and ceiling effects’ [36] were determined by calculating the number of individuals obtaining the lowest (0-10) or the highest (90-100) ODI-scores. We included range of 10 points referring to the minimal clinical important difference (MCID)[37], which is widely used to report a clinical relevant difference between assessments. Floor and ceiling effects are considered to be present if more than 15% of the patients achieved the extreme ends [36]. To examine ‘internal consistency’ an exploratory factor analysis (EFA) with a varimax rotation was conducted to determine whether the items form 1 predefined factor covering the overall scale [36]. A factor analysis with a principal component technique was chosen as the aim was to explore an underlying construct rather than to reduce the number of items[38]. To Validation of Dutch ODI v2.1a 139 test the factorability, inter-item correlations of the 10 items of the ODI were inspected and the significance level of the Bartlett test of sphericity and the Kaiser-Meyer-Olkin measure of sampling adequacy were calculated. A significant Kaiser-Meyer-Olkin measure of sampling adequacy value of >0.6 was considered acceptable [38,39]. The number of factors was determined by visual inspection of the scree plot, the percentage of unique variance (>5%), Eigenvalues more than 1, and factor interpretability. Only items with factor loadings more than 0.40 (20% explained variance) were retained and items with cross-loadings on more than 1 factor within 0.20 of the primary loading were dropped because of inadequate discrimination. Moreover, Cronbach α was calculated. A Cronbach’s α between 0.70 and 0.95 indicates good internal consistency [36]. ‘Construct validity’ is supported when at least 75% (≥5) of the predefined hypotheses are confirmed (Table 1)[36]. The strength of the correlations is interpreted as ‘weak’ (r=0.10-0.30), ‘moderate’ (r= 0.31-0.50), or ‘strong’ (r= 0.51-1.00)[40,41]. We found no studies in which the ODI was validated to the HADS-A and HADS-D (Table 7.1). Therefore, a priori hypothesises related to a weak correlation with HADS-A and HADS-D were based on the assumption that the ODI deliberately focussed on physical activities and not the psychological consequences of CLBP[6]. We expected a moderate association for the SF-36 Mental Component Scale as previous studies revealed that although the mental health subscale showed a weak correlation the remaining subscales showed a moderate relationship [8,12,14,18]. Statistical analyses The distributions of the ODI items, ODI total score, and all study variables were examined with the Kolmogorov-Smirnov test. Descriptive statistics are provided; mean and standard deviation (SD) for continuous variables and counts and percentages for categorical variables. The means (SDs), inter-item and item-total correlations were calculated for each item as well as for the total score. To assess internal consistency explanatory factor analysis (EFA) was conducted to explore the factor structure of the questionnaire as, as yet no clear hypothesis about the factor structure exists. Moreover, Cronbach α was calculated. Pearson’s r correlations were calculated in order to determine whether our a priori hypotheses were confirmed (Table 7.1). As the RMDQ measures the same construct, we assessed the mean difference and the 95% limits of agreement to explain the difference between the two scales, which is illustrated in the Bland-Altman plot [42,43]. To perform the analysis, the scaling of the RMDQ score was adjusted to the ODI scale (0-100). Furthermore, we compared to the results of the original developer [6]. A p< 0.05 was considered statistical significant. All statistical analyses were performed using STATA version 10.0 (StataCorp, College Station, TX). 07 140 Validation of Dutch ODI v2.1a Table 7.1 A priori hypotheses for evaluating the measurement properties of the Dutch ODI version 2.1a Hypothesis General aspects 1. The total score is normally distributed 2. The percentage of missing data per item and the total score is < 5% 3. Floor and ceiling effects in the total scores are less than ≤ 15% [37] Internal consistency 4. The Cronbach’s α is 0.70 ≤ α ≤ 0.95 [37] 5. The item-total correlation is strong (r 0.51-0.82) [11,20,21] Construct validity A. Strong positive correlation: 6. Disability (RMDQ; r 0.61-0.84) [6,7,9,11-15,17,20-22] B. Strong negative correlation: 07 7. Quality of Life – Physical component (SF36-PCS; r -0.75) [8,12] C. Moderate to strong positive correlation: 8. Pain severity (NRSpain; VAS r 0.33-0.87) [7-9,11-16,19-22] D. Moderate positive correlation: 9. Depression (HADS-D) E. Moderate negative correlation: 10. Quality of Life- Mental component (SF36-MCS) [8,12] F. Weak positive correlation: 11. Anxiety (HADS-A) ODI indicates Oswestry Disability Index; r Pearson correlation coefficient RMDQ Roland and Morris Disability Questionnaire; SF-36 36-item Short Form Health Survey Questionnaire; NRS Numeric Rating Scale; HADS-D Hospital Anxiety and Depression Scale for Depression; HADS-A Hospital Anxiety and Depression Scale for Anxiety; VAS Visual Analogue Scale; MCS Mental Component Score; PCS Physical Component Score. Validation of Dutch ODI v2.1a 141 Results A total of 244 patients (140 females [57.4%]), with mean age 45.6 (SD, 10.8 yr) years, and with longstanding CLBP (mean, 12.7 yr [SD, 10.4 yr]), participated in the CPP program (May 2012-March 2013). In total, 41.0% (n= 100) reported previous back surgery (Table 7.2). Based on the ODI, the majority of the participating patients (88.2%) were moderately to severely disabled (Table 7.3). General aspects and Floor and Ceiling effects Data on all variables were normally distributed between found minimum and maximum ranges of each self-report measure used (Table 7.2). Table 7.4 shows the means (SD) and the floor and ceiling values for the different ODI items in this patient sample. The floor value was reached more often in ‘personal care’ (41.8%), ‘walking’ (27.0%), and ‘sex life’ (16.0%). None of the patients left an item unanswered. One patient (0.4%) achieved a total score in the range 0-10 (ODI= 6) and the highest total score was 70 (1 patient [0.4%]). None of the patients reached highest total scores of 90-100. For the total score, no floor and ceiling effects seem to be present. Table 7.2 Pre-treatment patient characteristics Pre-treatment characteristics (n= 244) Sociodemographic Age; mean (SD, range min-max) in years 45.6 (± 10.8, 19-69) Gender; n (%), male : female 104 (42.6%) : 140 (57.4%) Work status; n (%) yes : no 192 (78.7%) : 52 (21.3%) CLBP History Duration of LBP; mean (SD, range min-max) in years 12.7 (± 10.4, 1-55) Previous surgery; n (%) yes : no 100 (41.0%) : 144 (59.0%) Pain medication; n (%) yes : no 214 (87.7%) : 30 (12.3%) Self-report measures mean (SD, range min-max) Functioning Disability in functional status (ODI) 39.6 (± 12.3, 6-70) Disability in functional status (RMDQ) 13.7 (± 4.1, 2-21) Quality of Life Physical component (SF36-PCS) 42.9 (± 15.7, 10-84) Mental component (SF36-MCS) 58.5 (± 21.5, 6-96) Pain Severity (NRSpain) 57.9 (± 23.5, 0-100) Cognition & Behaviour Catastrophizing cognitions (PCS) 21.2 (± 10.4, 0-51) Fear of movement behavior (TSK) 31.5 (± 5.0, 19-42) Self-efficacy (PSEQ) 11.8 (± 3.2, 2-21) Anxiety (HADS-A) 6.7 (± 3.3, 0-18) Depression (HADS-D) 6.3 (± 3.8, 0-18) SD indicates standard deviation; RMDQ Roland and Morris Disability Questionnaire; SF-36, 36-item Short Form Health Survey Questionnaire; SF-36-MCS Mental Component Score; SF-36-PCS Physical Component Score; NRS Numeric Rating Scale; HADS-D Hospital Anxiety and Depression Scale for Depression; HADS-A Hospital Anxiety and Depression Scale for Anxiety; PCS Pain Catastrophizing Scale; TSK Tampa Scale for Kinesiophobia; PSEQ Pain Self-Efficacy Questionnaire. 07 142 Validation of Dutch ODI v2.1a Table 7.3 Pre-treatment clinical characteristics; severity of disability of patients with CLBP* participating in a CPP program in secondary care. Category Description n (%) 0 - 20 Minimal disability 16 (6.6) 21 - 40 Moderate disability 117 (48.0) 41 - 60 Severe disability 98 (40.2) 61 - 80 Crippled 13 (5.3) 81 - 100 Bed bound 0 * Classification according to Fairbank et al.[5] ODI indicates Oswestry Disability Index; CPP Combined Physical and Psychological 07 Internal consistency The initial analysis of the 10 items of the ODI showed that the Bartlett test of sphericity was significant (χ2= 508.11, df= 45, p< 0.001) with a Kaiser-Maier-Olkin measure of sampling adequacy value of 0.85, indicating the appropriateness of conducting a factor analysis. The inter-item Pearson r correlations ranged from 0.10 (item 6 and item 7) to 0.44 (item 9 and item 10), indicating no multicollinearity. Table 4 shows that all items are similarly associated with the total score. The scree plot of Eigenvalues, the percentage of variance, and the number of Eigenvalues more than 1 indicated a 1-factor solution. Because of a 1-factor solution, no rotation could be performed. This factor with an Eigenvalue of 2.8 explains 35.6% of the total variance (Table 7.4). The Cronbach α was 0.79 and the item-total correlations ranged from 0.48 (for item 7; ‘sleeping’) to 0.68 (for items 9 and 10; ‘social life’ and ‘travelling’) (Table 7.4), confirming the internal consistency of the ODI in our patient sample. Table 7.4 Characteristics of the Dutch ODI version 2.1a Item Mean (SD) Floor, n (%) Ceiling, n (%) Missing, n (%) Item-Total correlation Factor Loading 1. Pain intensity 2.58 (0.82) 1 (0.4) 1 (0.4) 0 0.54 0.55 2. Personal care 0.86 (0.87) 102 (41.8) 1 (0.4) 0 0.56 0.57 3. Lifting 2.67 (1.18) 7 (2.9) 7 (2.9) 0 0.55 0.53 4. Walking 1.04 (0.87) 66 (27.0) 0 0 0.63 0.65 5. Sitting 2.38 (0.96) 4 (1.6) 3 (1.2) 0 0.58 0.58 6. Standing 2.87 (1.15) 2 (0.8) 7 (2.9) 0 0.59 0.57 7. Sleeping 1.45 (0.87) 16 (6.6) 3 (1.2) 0 0.48 0.47 8. Sex life (if appl.) 1.82 (1.40) 39 (16.0) 12 (4.9) 0 0.64 0.62 9. Social life 2.16 (1.01) 21 (8.6) 1 (0.4) 0 0.68 0.69 10. Travelling 1.95 (1.07) 5 (2.0) 4 (1.6) 0 0.68 0.69 Eigenvalue 2.8 % of variance 35.6% Cronbach α 0.79 ODI indicates Oswestry Disability Index; SD indicates standard deviation Validation of Dutch ODI v2.1a 143 Construct validity The data of the 2 disability scores, ODI and RMDQ, showed a strong significant correlation with one another (r= 0.68, p< 0.001) (Figure 7.1; Table 7.5). Using the regression equation of ODI on RMDQ (Figure 7.1; Y= 11.542 + 2.030 * X), extrapolation to the minimum RMDQ score (= 0) and the maximum RMDQ score (= 24), yielded ODI scores of 12 and 60. The Bland-Altman plot (Figure 7.2) shows a mean difference of -18 with wide 95% limits of agreement (95% LoA) (-44 to 8). A trend tends to be seen that the higher the mean values the more negative the differences are. The correlations of the ODI total score with measures of pain, quality of life, and psychological symptoms are displayed in Table 7.5. These relationships varied from r= 0.22 (p<0.001,’weak’; HADS-A) to r= -0.57 (P<0.001, ‘strong’; SF-36 Physical Component Score). As all (100%; 6:6) of the a priori hypotheses were confirmed, construct validity is supported (Table 7.5). Figure 7.1 Relationship between the total scores at pre-treatment assessment of ODI and RMDQ (n= 244) 07 Relationship between the total scores at pre-treatment assessment of ODI and RMDQ (n= 244). Regression formula: Y= 11.542 + 2.030 x X; R2= 0.45; Pearson r= 0.68 (p<0.001). ODI indicates Oswestry Disability Index; RMDQ Roland and Morris Disability Questionnaire 144 Validation of Dutch ODI v2.1a Figure 7.2 Bland-Altman plot of differences between ODI and RMDQ for patients with CLBP. 07 Bland-Altman plot of differences between ODI and RMDQ for patients with CLBP. Middle horizontal line indicates the mean difference (value = -18). Upper en lower horizontal lines indicate the upper and lower limits of agreement (2SD on either side of mean difference [value = 8 and -44 respectively]). SD indicates standard deviation; ODI Oswestry Disability Index; RMDQ Roland and Morris Disability Questionnaire; CLBP chronic low back pain. Table 7.5 Predefined hypotheses and correlations of variables with the ODI dimension Predefined hypothesis Correlation Pearson’s r* Confirmed (Yes/No) 0.68 Yes -0.57 Yes 0.40 Yes; moderate 0.40 Yes -0.43 Yes 0.22 Yes Strong positive correlation: Disability (RMDQ) Strong negative correlation: Quality of Life – Physical component (SF36-PCS) Moderate to strong positive correlation: Pain severity (NRSpain) Moderate positive correlation Depression (HADS-D) Moderate negative correlation: Quality of Life - Mental component (SF36-MCS) Weak positive correlation: Anxiety (HADS-A) * All correlations p<0.001 ODI indicates Oswestry Disability Index; RMDQ Roland and Morris Disability Questionnaire; SF-36, 36-item Short Form Health Survey Questionnaire; NRS Numeric Rating Scale; HADS-D Hospital Anxiety and Depression Scale for Depression; HADS-A Hospital Anxiety and Depression Scale for Anxiety; MCS Mental Component Score; PCS Physical Component Score. Validation of Dutch ODI v2.1a 145 Discussion In this study we translated and adapted the Oswestry Disability Index (ODI) version 2.1a as a condition-specific PROM, for Dutch-speaking patients. We examined and confirmed the validity and the internal consistency of the Dutch ODI in a cohort of patients with chronic low back pain (CLBP) in secondary care to have a formal ODI and to avoid future inappropriate use [31]. Translation and trans-cultural adaptation In line with the German translation [11], the Dutch and English languages are also closely related and the translation is carried out with almost literal equivalence for many of the terms used. We used the international SI unit of length as the metric equivalent for walking distances (i.e. 1km; 500m; 100m, recommended by the original author). The ‘walking’ itemtotal correlation was however comparable with other items (Table 7.4; r= 0.63) and 174 of the 244 patients (71%) selected 1 of the 3 distances. No changes were needed after testing the prefinal version. The final version is available from PROQOLID website [44]. General aspects & Floor and Ceiling effects Because of web-based assessments with compulsory items we had no missing values, even the item about sex life, which was a non-compulsory item. This is in contrast to previously reported missing values, ranging from 15% to 23% [11,15,16,21]. Although for the total score no floor and ceiling effects are shown, for the individual items ‘sex life’, ‘walking’ and ‘personal care’ we found floor effects with percentages exceeding the recommended 15%, which is comparable with a previous validation study [16]. Internal consistency As yet, no clear hypothesis of the factor structure of the ODI exists. We found that the items of the ODI loaded on 1 factor, explaining 36% of the variance, suggesting that other nonmeasured factors or domains are involved in the concept of functional status. This 1-factor structure was previously determined, though with a higher proportion of explained variance: 45%-55%[14,19,21]. Other studies found a 2-factor structure [15,16,45,46] and a difference between static physical activities and dynamic physical activities [45] or between painrelated activities and pain-related participation was suggested as a possible explanation [15,46]. Furthermore, homogeneity of the Dutch ODI-items is confirmed by Cronbach α (α= 0.79; 0.70 < α < 0.95) and although the α is lower it is similar to those previously reported (0.83 ≤ α ≤ 0.94)[7-22]. Construct validity We formulated several specific a priori hypotheses to confirm the construct validity of the Dutch ODI to avoid a possible risk of bias in explanations for the associations found[36]: all (100%) the hypothesized associations were confirmed. A strong relationship between the RMDQ and the ODI was shown (r= 0.68, p< 0.001), similar as previously reported (r= 0.61-0.84) [7,9-15,17,18,20-22], and suggesting interchangeability of the two measures. Because the use of correlation coefficients might be misleading[42], we calculated 95% limits of agreement (95% LoA). We found similar mean differences and wide but smaller 95% LoA than reported by Fairbank and Pynsent[6] (mean [95% LoA]: -18 [-44 to 8] and 17 [-50 to 15], respectively). The difference in precision of estimated LoA might be explained by a difference in patient samples 07 146 Validation of Dutch ODI v2.1a used[42]. Moreover, our results substantiate the trend that the greater the mean values the more negative the differences are. It was noticed that extrapolation of the regression equation for the relationship between ODI and RMDQ to a maximum score RMDQ score (24 points), yielded an ODI score of just 60. In our study a small proportion of patients were ‘crippled’ (5.3%; n= 13; Table 3). If these patients were to deteriorate after an intervention, the RMDQ might not be responsive enough to detect the change. Moreover, improvements on the RMDQ could only be detected in patients for whom the corresponding ODI-score is less than 60. Our finding tends to confirm previous suggestions that the ODI may be more appropriate than the RMDQ in use in patients with a higher degree of disability: at high levels of disability the ODI still detects change when the RMDQ points are at a maximum [11,30]. Frost et al. [47] concluded that the ODI is more responsive at the lower extreme end than the RMDQ and suggested to use the ODI in minimally disabled patients. Although an interesting recommendation, we cannot draw any firm conclusions regarding the lower end of the ODI since we only had 1 patient who scored less than 12. 07 To formulate an a priori hypothesis for association with pain severity we only found studies that included a VAS as measure. As visual analogue scale (VAS) and NRS are closely related [48], the VAS results can be interpreted as NRS results. We found a comparable moderate relationship between ODI and NRS as previously reported, suggesting that the constructs of disability and pain intensity only partially overlap. This is explained by the construction of the ODI; only 1 of the items is related to pain. The assumption is supported that the ODI assesses the physical component of health-related quality of life (SF-36 Physical Component Scale)[6,8,12,15]. The hypotheses for the psychological aspects (HADS ‘anxiety’ and HADS ‘depression’) are confirmed. This is consistent with the evidence found in a recently published study in which an overview is given of all factors influencing treatment outcome in CLBP (i.e disability, pain, and quality of life) [49]. Limitations A potential limitation of this study was that the sample consisted largely of patients with moderate to severe CLBP-related disability (n= 225, 88%; mean [SD], 40 [12]) and selected for a CPP program, meaning that the sample is not representative for the whole Dutch CLBP population. The Dutch ODI should also be formally tested in primary care settings, although other translations have shown to be valid in these settings [6,9,10,15-17,22]. We do realise that the test-retest reliability was not established. The Dutch language is closely related to the English and German languages and we expect no differences as these versions showed very strong agreement between measurements on 2 occasions (Pearson r= 0.89 [6] and ICC [intraclass correlation]= 0.96 [11], respectively). The test-retest reliability is commonly studied in other languages as well; the ICC’s varied between 0.88 and 0.99 [9-12,15-22]. Finally, other measurement properties as responsiveness and possible cut-off scores for successfulness of a spinal intervention still need to be determined in future longitudinal studies. Validation of Dutch ODI v2.1a 147 Conclusion We produced a valid Dutch version of the Oswestry Disability Index for the measurement of functional status and disability among Dutch patients with CLBP. The translated questionnaire is a valuable tool and the authors recommend this translated 2.1a version as a conditionspecific PROM for use in future back pain research purposes and to evaluate outcome of back care. The official version is implemented and used in the Dutch Spine Surgery Registry in the Netherlands. Key points • The Dutch version of ODI 2.1a is a valid and valuable tool to measure functional outcome in spine care. • Factor analysis suggested a 1-factor structure. • The Dutch ODI showed a strong correlation with the NRS pain severity as well as the Dutch RMDQ and the physical component of SF-36. • The Dutch ODI is recommended as a condition-specific PROM for use in future Dutch back pain research and to evaluate outcome of back care. • The Dutch ODI is implemented in the Dutch Spine Surgery Registry for the spine society in the Netherlands. Acknowledgements The authors thank the multidisciplinary team at RealHealth NL, who were responsible for the assessments of the participants in the CPP program. They also thank Frank Laumen (EasyFile) for his support in building and managing the electronic web- based system, including the database. 07 148 Validation of Dutch ODI v2.1a References 1. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2197-2223 2. McCormick JD, Werner BC, Shimer AL. Patient-reported outcome measures in spine surgery. J.Am.Acad. Orthop.Surg. 2013;21:99-107 3. ICHOM. Standard Set for Low Back Pain 2014 [cited 2014 September 1]. Available from: http://www.ichom. org/project/low-back-pain/ 4. Poolman RW, Swiontkowski MF, Fairbank JC, Schemitsch EH, Sprague S, de Vet HC. Outcome instruments: rationale for their use. J.Bone Joint Surg.Am. 2009;91 Suppl 3:41-49 5. Fairbank JC, Couper J, Davies JB, O'Brien JP. The Oswestry low back pain disability questionnaire. Physiotherapy. 1980;66:271-273 6. Fairbank JC, Pynsent PB. The Oswestry Disability Index. Spine (Phila Pa 1976.) 2000;25:2940-2952 7. Boscainos PJ, Sapkas G, Stilianessi E, Prouskas K, Papadakis SA. Greek versions of the Oswestry and RolandMorris Disability Questionnaires. Clin.Orthop.Relat Res. 2003:40-53 07 8. Grotle M, Brox JI, Vollestad NK. Cross-cultural adaptation of the Norwegian versions of the Roland-Morris Disability Questionnaire and the Oswestry Disability Index. J.Rehabil.Med. 2003;35:241-247 9. Yakut E, Duger T, Oksuz C, Yorukan S, Ureten K, Turan D et al. Validation of the Turkish version of the Oswestry Disability Index for patients with low back pain. Spine (Phila Pa 1976.) 2004;29:581-585 10. Lauridsen HH, Hartvigsen J, Manniche C, Korsholm L, Grunnet-Nilsson N. Danish version of the Oswestry Disability Index for patients with low back pain. Part 1: Cross-cultural adaptation, reliability and validity in two different populations. Eur.Spine J. 2006;15:1705-1716 11. Mannion AF, Junge A, Fairbank JC, Dvorak J, Grob D. Development of a German version of the Oswestry Disability Index. Part 1: cross-cultural adaptation, reliability, and validity. Eur.Spine J. 2006;15:55-65 12. Lue YJ, Hsieh CL, Huang MH, Lin GT, Lu YM. Development of a Chinese version of the Oswestry Disability Index version 2.1. Spine (Phila Pa 1976.) 2008;33:2354-2360 13. Vogler D, Paillex R, Norberg M, de GP, Cabri J. [Cross-cultural validation of the Oswestry disability index in French]. Ann.Readapt.Med.Phys. 2008;51:379-385 14. Monticone M, Baiardi P, Ferrari S, Foti C, Mugnai R, Pillastrini P et al. Development of the Italian version of the Oswestry Disability Index (ODI-I): A cross-cultural adaptation, reliability, and validity study. Spine (Phila Pa 1976.) 2009;34:2090-2095 15. Payares K, Lugo LH, Morales V, Londono A. Validation in Colombia of the Oswestry disability questionnaire in patients with low back pain. Spine (Phila Pa 1976.) 2011;36:E1730-E1735 16. Pekkanen L, Kautiainen H, Ylinen J, Salo P, Hakkinen A. Reliability and validity study of the Finnish version 2.0 of the oswestry disability index. Spine (Phila Pa 1976.) 2011;36:332-338 17. Denis I, Fortin L. Development of a French-Canadian version of the Oswestry Disability Index: cross-cultural adaptation and validation. Spine (Phila Pa 1976.) 2012;37:E439-E444 18. Vigatto R, Alexandre NM, Correa Filho HR. Development of a Brazilian Portuguese version of the Oswestry Disability Index: cross-cultural adaptation, reliability, and validity. Spine (Phila Pa 1976.) 2007;32:481-486 19. Valasek T, Varga PP, Szoverfi Z, Kumin M, Fairbank J, Lazary A. Reliability and validity study on the Hungarian versions of the oswestry disability index and the Quebec back pain disability scale. Eur.Spine J. 2013;22:10101018 20. Joshi VD, Raiturker PP, Kulkarni AA. Validity and reliability of English and Marathi Oswestry Disability Index (version 2.1a) in Indian population. Spine (Phila Pa 1976.) 2013;38:E662-E668 21. Miekisiak G, Kollataj M, Dobrogowski J, Kloc W, Libionka W, Banach M et al. Validation and cross-cultural adaptation of the Polish version of the Oswestry Disability Index. Spine (Phila Pa 1976.) 2013;38:E237-E243 Validation of Dutch ODI v2.1a 149 22. Vincent JI, Macdermid JC, Grewal R, Sekar VP, Balachandran D. Translation of oswestry disability index into Tamil with cross cultural adaptation and evaluation of reliability and validity( section sign). Open.Orthop.J. 2014;8:11-19 23. ICHOM Working Group LBP. Low Back Pain Flyer & Data Collection User Manual. International Consortium for Health Outcomes Measurement [ICHOM]. 2014 [cited 2015 September 1]. Available from: http://www. ichom.org/wp-content/uploads/ 2014/08/LBP-Reference-Guide-8.20.14-TP.pdf. 24. Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976.) 2000;25:3186-3191 25. Acquadro C, Conway K, Hareendran A, Aaronson N. Literature review of methods to translate health-related quality of life questionnaires for use in multinational clinical trials. Value.Health 2008;11:509-521 26. van Hooff ML, van der Merwe JD, O'Dowd J, Pavlov PW, Spruit M, de Kleuver M et al. Daily functioning and self-management in patients with chronic low back pain after an intensive cognitive behavioral programme for pain management. Eur.Spine J. 2010;19:1517-1526 27. Guillemin F, Bombardier C, Beaton D. Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines. J.Clin.Epidemiol. 1993;46:1417-1432 28. van Hooff ML, Spruit M, O'Dowd JK, van Lankveld W, Fairbank JC, van Limbeek J. Predictive factors for successful clinical outcome 1 year after an intensive combined physical and psychological programme for chronic low back pain. Eur.Spine J. 2014;23:102-112 29. Baker D, Pynsent P, Fairbank J. The Oswestry Disability Index revisited. Back Pain: New Approaches to Rehabilitation and Education 1989:174-86 30. Roland M, Fairbank J. The Roland-Morris Disability Questionnaire and the Oswestry Disability Questionnaire. Spine (Phila Pa 1976.) 2000;25:3115-3124 31. Fairbank JC. Use and abuse of Oswestry Disability Index. Spine (Phila Pa 1976.) 2007;32:2787-2789 32. Roland M, Morris R. A study of the natural history of back pain. Part I: development of a reliable and sensitive measure of disability in low-back pain. Spine (Phila Pa 1976.) 1983;8:141-144 33. Jensen MP, Karoly P. Pain-specific beliefs, perceived symptom severity, and adjustment to chronic pain. Clin.J.Pain 1992;8:123-130 34. Ware JE, Jr.Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med.Care 1992;30:473-483 35. Spinhoven P, Ormel J, Sloekers PP, Kempen GI, Speckens AE, Van Hemert AM. A validation study of the Hospital Anxiety and Depression Scale (HADS) in different groups of Dutch subjects. Psychol.Med. 1997;27:363-370 36. Terwee CB, Bot SD, de Boer MR, van der Windt DA, Knol DL, Dekker J et al. Quality criteria were proposed for measurement properties of health status questionnaires. J.Clin.Epidemiol. 2007;60:34-42 37. Ostelo RW, Deyo RA, Stratford P, Waddell G, Croft P, Von KM et al. Interpreting change scores for pain and functional status in low back pain: towards international consensus regarding minimal important change. Spine 2008;33:90-94 38. Beavers AS, Lounsbury JW, Richards JK, Huck SW, Skolitis GJ, Esquivel SL. Practical Considerations for Using Exploratory Factor Analysis in Educational Research. Practical Assessment, Research & Evaluation [A peerreviewed electronic journal] 2013;18:1-13 39. Pett MA, Lackey NR, Sullivan JJ. Making sense of factor analysis: the use of factor analysis for instrument development in health care research 2003 40. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Earlbaum Associate; 1988 41. Cohen J. A power primer. Psychol.Bull. 1992;112:155-159 42. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-310 07 150 Validation of Dutch ODI v2.1a 43. Bland JM, Altman DG. Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet 1995;346:1085-1087 44. PROQOLID. Patient-Reported Outcome and Quality of Life Instruments Database - Oswestry Disability Index (ODI), [Mapi Research Trust]. 2014 [cited 2014 September 1]] Available from: http://www.proqolid.org/ instruments/ oswestry_disability_index_odi 45. Guermazi M, Mezghani M, Ghroubi S, Elleuch M, Med AO, Poiraudeau S et al. [The Oswestry index for low back pain translated into Arabic and validated in a Arab population]. Ann.Readapt.Med.Phys. 2005;48:1-10 46. Osthus H, Cziske R, Jacobi E. Cross-cultural adaptation of a German version of the Oswestry Disability Index and evaluation of its measurement properties. Spine (Phila Pa 1976.) 2006;31:E448-E453 47. Frost H, Lamb SE, Stewart-Brown S. Responsiveness of a patient specific outcome measure compared with the Oswestry Disability Index v2.1 and Roland and Morris Disability Questionnaire for patients with subacute and chronic low back pain. Spine (Phila Pa 1976.) 2008;33:2450-2457 48. Von Korff M, Jensen MP, Karoly P. Assessing global pain severity by self-report in clinical and health services research. Spine (Phila Pa 1976.) 2000;25:3140-3151 49. van Hooff ML, van Lankveld J, van Limbeek J, de Kleuver M. The Nijmegen decision tool for chronic low back pain. Development of a clinical decision tool for secondary or tertiary spine care specialists. PLoS.One. 07 2014;9:e104226 151 Chapter 08 Determination of the Oswestry Disability Index score equivalent to a “satisfactory symptom state” in patients undergoing surgery for degenerative disorders of the lumbar spine A Spine Tango registry-based study van Hooff ML Mannion AF Staub LP Ostelo RWJG Fairbank JCT Published in: Spine J. 2016 Oct;16(10):1221-1230. Epub 2016 Jun 22 152 Abstract Background Context: The achievement of a given change score on a valid outcome instrument is commonly used to indicate whether a clinically relevant change has occurred after spine surgery. However, the achievement of such a change score can be dependent on baseline values and does not necessarily indicate whether the patient is satisfied with the current state. The achievement of an absolute score equivalent to a patient acceptable symptom state (PASS) may be a more stringent measure to indicate treatment success. Purpose: This study aimed to estimate the score on the Oswestry Disability Index (ODI, version 2.1a; 0-100) corresponding to a PASS in patients who had undergone surgery for degenerative disorders of the lumbar spine. Study Design / Setting: This is a cross-sectional study of diagnostic accuracy using follow-up data from an international spine surgery registry. Patient Sample: The sample includes 1,288 patients with degenerative lumbar spine disorders who had undergone elective spine surgery, registered in the EUROSPINE Spine Tango Spine Surgery Registry. Outcome Measures: The main outcome measure was the ODI (version 2.1a). Methods: Surgical data and data from the ODI and Core Outcome Measures Index (COMI) were included to determine the ODI threshold equivalent to PASS at 1 year (±1.5 months; n= 780) and 2 years (± 2 months; n= 508) postoperatively. The symptom-specific well-being item of the COMI was used as the external criterion in the receiver operating characteristic (ROC) analysis to determine the ODI threshold equivalent to PASS. Separate sensitivity analyses were performed based on the different definitions of an ‘acceptable state’ and for subgroups of patients. JF is a copyright holder of the ODI. Results: The ODI threshold for PASS was 22, irrespective of the time of follow up (area under the curve [AUC]: 0.89 [sensitivity (Se): 78.3%, specificity (Sp): 82.1%] and AUC: 0.91 [Se: 80.7%, Sp: 85.6], for the 1- and 2-year follow-ups, respectively). Sensitivity analyses showed that the absolute ODI-22 threshold for the two follow-up time-points were robust. A stricter definition of PASS resulted in lower ODI thresholds, varying from 16 (AUC=0.89; Se: 80.2%, Sp: 82.0%) to 18 (AUC=0.90; Se: 82.4%, Sp: 80.4%) depending on the time of follow up. Conclusions: An ODI score ≤22 indicates the achievement of an acceptable symptom state and can hence be used as a criterion of treatment success alongside the commonly used changescore measures. At the individual level the threshold could be used to indicate whether or not a patient with a lumbar spine disorder is a ‘responder’ after elective surgery. PASS for ODI 153 Introduction In Western societies, low back pain (LBP) has the largest disease burden [1]. It is associated with a substantial amount of morbidity, and complaints are multidimensional. Functional status is an important patient-related outcome when evaluating surgical and non-surgical interventions for LBP. One important feature of outcome instruments measuring functional status is their ability to detect meaningful change from the patient’s perspective. In the absence of appropriate objective clinical outcome measures, the use of patient-reported outcome measures (PROM) to assess treatment outcome is commonly accepted [2]. The Oswestry Disability Index (ODI) [3], and the ODI version 2.1a [4,5] in particular, is widely accepted and recommended as a condition-specific PROM in interventional studies [6]. As such, medical decision-making increasingly relies on this measure. Although most clinicians and researchers agree that the success of any intervention should be judged from the patient’s perspective, to date no consensus exists for criteria indicating ‘success’. In health services research it is important to define clear criteria for treatment ‘success’. Success can be conceptualized in two ways: (1) relevant change or improvement, and (2) achievement of an acceptable state. With the first concept, the emphasis is on whether or not an individual has improved after an intervention [7], whereas with the second the emphasis is on whether or not the achieved outcome is acceptable from the patient’s perspective [7]. The concept of change (minimum clinically important difference or change) is frequently used in spine research to assess treatment success. In relation to this it is important to specify whether the observed change in an individual’s scores is merely the result of measurement error or whether it constitutes a real change, and whether that change is also clinically relevant to the patient [8]. However, it is difficult to measure what is clinically relevant to patients [9], and methodological issues such as population-dependency and baseline dependency [10] are encountered. Moreover, assessment of change does not indicate whether a ‘normal’ or ‘healthy’ symptom state is reached. For these reasons, we have previously used a more stringent definition of success based on achievement of values seen in ‘normal’, healthy populations [11]. The threshold used was the achievement of an ODI-value, derived from ‘normal’ subjects with little or no back pain, of ≤22 [4,11]. The use of ‘normal, healthy population’ values as the reference might however be criticized, as the ODI is a condition-specific instrument. An alternative approach to measuring success is to identify the value beyond which patients consider themselves well or consider their health state to be acceptable, i.e. the concept of the patient acceptable symptom state (PASS) [12,13]. Determination of the absolute cutoff value (threshold) at follow up, equivalent to achievement of a PASS, would assist in interpreting scores at the individual level and would allow determination of the proportion of patients within a group who achieve this level, when evaluating the effectiveness or success of interventions. Achievement of this threshold might be more important than the achievement of a given change value and it probably reflects the ultimate goal of treatment from the patient’s perspective [12,14]. The concepts of ‘feeling better’ and ‘feeling good’ are complementary but distinctly different; a patient’s condition can be markedly improved by the intervention but can still be suboptimal [7,12,13]. The purpose of the present study was to estimate the score on the ODI (version 2.1a) corresponding to a ‘patient acceptable symptom state’ in patients undergoing surgery for 08 154 PASS for ODI degenerative disorders of the lumbar spine. To assess the robustness of the findings, we performed sensitivity analyses with different definitions for ‘acceptable state’ and in various subgroups of patients. Materials and Methods 08 Study population This cross-sectional study was performed using postoperative data from the Spine Tango Spine Surgery Registry of EUROSPINE, the Spine Society of Europe (SSE) [15,16], and according to the STARD statement for reporting studies of diagnostic accuracy [17]. The study dataset was prepared in August 2014 by linking the surgical data, recorded on the SSE Spine Tango Surgery 2006 form (ST-2006-form), with the last available follow-up ODI and Core Outcome Measures Index (COMI) [18,19], which had to have been completed on the same day (Figure 8.1). The surgical inclusion criteria were based on the data documented using the registry’s ST2006-form: lumbar, lumbo-sacral, sacral, or coccyx as the level of procedure, and degenerative disease (black disc or disc degeneration, spondylosis, spondylarthrosis, adjacent segment degeneration, spinal stenosis, disc herniation) or spondylolisthesis (degenerative) as main pathology. The data from 2,530 patients satisfied these inclusion criteria. The registry’s ST-2006-form was also used to derive descriptive information on the patient samples to perform sensitivity analyses. Data for the following variables were extracted: date of birth (age), gender, (number of) previous spine surgeries, morbidity state (American Society of Anesthesiologists physical status score [ASA score]), surgical measures (type of surgery), and surgical complications. Patient-reported complications recorded on the Spine Tango patient follow-up questionnaire were also used. Outcome measure The Oswestry Disability Index (ODI, version 2.1a) [5] was the main outcome measure. The ODI (10 items) measures the impact of LBP on patients’ functional ability in 10 aspects of daily life. The total ODI score is a sum score and ranges from 0 to 100; higher scores indicate greater disability. External criterion (anchor) The anchor used to assess the success of surgery, i.e. achievement of an acceptable symptom state at follow up, was the response on the symptom-specific well-being (SSWB) item of the COMI [19,20]: “If you had to spend the rest of your life with the symptoms you have now, how would you feel about it?” The responses are given on a 5-point Likert scale: ‘very satisfied’, ‘somewhat satisfied ‘, ‘neither satisfied nor dissatisfied’, ‘somewhat dissatisfied’, and ‘very dissatisfied’. PASS for ODI 155 Figure 8.1 Data flow. ST-2006-form completed n = 30,878 Follow-up ODI n = 27,203 Follow-up COMI n = 38,906 Included: ODI and COMI completed on the same day Follow-up ODI and COMI pair n = 15,358 Follow-up ODI and COMI pair, linked with ST-2006-form n = 2,787 Primary diagnosis: Degenerative lumbar spine disorder n = 2,530 Included: Last available follow-up ODI and COMI pair correctly linked with ST-2006-form Included: ST-2006-form ‘main pathology’: degenerative disease and degenerative spondylolisthesis Included: 1-year follow-up data (± 1,5 months) 2-year follow-up data (± 2months) 1 or 2-year follow-up data available n = 1,288 1 - year follow-up data available for analysis n = 780 2-year follow-up data available for analysis n = 508 ST-2006-form, SSE Spine Tango 2006 surgery form; COMI, Core Outcome Measures Index; ODI, Oswestry Disability Index. Values for success (PASS) For the analyses the 5-point Likert scale of the COMI SSWB item was collapsed to a dichotomous outcome variable (1=acceptable; 0=unacceptable). We performed two separate analyses. The main analysis considered patients who reported feeling ‘very satisfied’ and ‘somewhat satisfied’ as an acceptable symptom state (PASS 1), whereas the sensitivity analysis included only those who reported feeling ‘very satisfied’ (PASS 2). All other categories were considered to represent ‘unacceptable’. To determine PASS we defined two study samples: those with a 1-year follow up (±1.5 months) and those with a 2-year follow-up assessment (±2 months). 08 156 PASS for ODI Statistical analyses Baseline demographic and perioperative data of patients in both study samples were described as means and standard deviations (SD) for continuous variables and as counts (and percentages) for categorical variables. Differences between samples were examined with independent Student t-tests for continuous variables and with Pearson chi-square tests for categorical variables. Proportions (%) are presented for both the distribution of responses on the SSWB item and the % categorized as ‘acceptable’ and ‘unacceptable’. 08 The ODI threshold for the patient acceptable symptom state (PASS 1) was determined using receiver operating characteristics (ROC) analyses. The corresponding ROC curve is a plot for each cut-off value that represents the relation between the proportion of patients who were correctly classified in the ‘acceptable’ group, based on the COMI SSWB score (sensitivity [true-positive]; Y-axis) and the proportion of patients who were incorrectly classified in the ‘acceptable’ group (1-specificity [false-positive]; X-axis). The corresponding ROC tables show for each ODI cut-off value the relation between sensitivity, specificity, and the percentage correctly classified. The ODI ‘acceptable’ threshold is the value that provides the best balance between sensitivity and specificity, as it represents the lowest overall misclassification (i.e. minimum of false-positives and false-negatives, or the maximum sum of specificity and sensitivity [21]). The area under the curve (AUC) indicates the probability of correctly differentiating between an ‘acceptable’ and an ‘unacceptable’ state, and an AUC value of >0.7 is considered satisfactory [22]. For each PASS the AUCs at both follow-up assessments were compared for the discriminative ability of the ODI. When satisfactory, the ROC analyses were subsequently used to determine the ODI cut-off values. Separate sensitivity analyses were performed based on the second definition of ‘acceptable’ (PASS 2) and for subgroups of patients based on factors possibly influencing the surgical outcome. These factors were: gender (male; female), age group (<65 years; ≥65 years), previous surgery (yes; no), surgical procedure (decompression; fusion [i.e. fusion alone, decompression and fusion with or without stabilization]), surgical complications (yes; no; [wrong level, nerve root damage, cauda equina damage, spinal cord damage, bleeding in spinal canal, bleeding outside spinal canal, malposition of implant, dural lesion, wound infection, implant failure, other]), and patient-reported complications (yes; no). The statistical analyses were conducted using STATA version 12 for Windows (StataCorp, College Station, Texas, USA). Statistical significance was accepted at p<0.05. Results To determine the ODI threshold corresponding to PASS at the different predefined follow-ups, the data of 1,288 patients with degenerative lumbar spine disorders undergoing elective spine surgery were included, with 1-year (±1.5 months; n= 780) or 2-year (± 2 months; n= 508) follow up. The characteristics of the two study samples (mean age: 58.3 [standard deviation: 14.9] years) did not differ (Table 8.1). PASS for ODI 157 Table 8.1 Characteristics of the two study samples. Characteristics Total patient sample 1-year follow up (n= 780) Total patient sample 2-year follow up (n= 508) p value Gender, n (%), male : female 373 (47.8) : 407 (52.2) 243 (47.8) : 265 (52.2) 0.996a Age, mean ±SD, (range, min-max) (y) 58.1 ±15.1; (19-87) 59.1 ±15.2; (19-87) 0.481b 0.588a Age group < 65 n (%) 475 (60.9) 317 (62.4) ≥ 65 n (%) 305 (39.1) 191 (37.6) 180 (23.1) : 600 (76.9) 122 (24.0) : 386 (76.0) Previous surgery 0.698a n (%), yes : no 0.732a Number of previous surgeries, n (%) 1 137 (76.1) 96 (78.7) 2 29 (16.2) 18 (14.8) 3 8 (4.4) 4 (3.3) 4 4 (2.2) 2 (1.6) 5 2 (1.1) 2 (1.6) 107 (59.4) : 42 (23.3) : 31 (17.3) 60 (49.2) : 40 (32.8) : 22 (18.0) Previous surgery same level n (%), yes : no : partially Morbidity ASA classification, n (%) 0.233a 0.452a ASA 1 193 (24.7) 108 (21.3) ASA 2 308 (39.5) 205 (40.4) ASA 3 108 (13.9) 72 (14.2) ASA 4 1 (0.1) 3 (0.5) ASA 5 - - Unknown 170 (21.8) 120 (23.6) Decompression 487 (62.4) 335 (65.9) Fusion 293 (37.6) 173 (34.1) 36 (4.6) : 736 (94.4) : 8 (1.0) 31 (6.1) : 472 (92.9) : 5 (1.0) 0.241a 192 (24.6) : 588 (75.4) 133 (26.2) : 375 (73.8) 0.527a Surgical procedure, n (%) 0.201a Complications Surgical n (%), yes : no : missing Patient-reported n (%), yes : no ASA, American Society for Anaesthesiologists; SD, standard deviation Notes: Decompression included both anterior and posterior decompression. Fusion included fusion alone, decompression and fusion with or without stabilisation (anterior and/or posterior). Surgical complications included wrong level, nerve root damage, cauda equina damage, spinal cord damage, bleeding in spinal canal, bleeding outside spinal canal, malposition of implant, dura lesion, wound infection, implant failure, other. Patient-reported complications: Did any complication arise as a consequence of your operation in our hospital (e.g. problems with wound healing, paralysis, sensory disturbances)? Response categories: ‘yes’, ‘no’ a Chi-square test was used. b Student’s t test for independent samples was used. c p <0.05 for differences between the study samples. 08 158 PASS for ODI Distribution of ‘treatment success’ ratings Table 8.2 shows the distribution of responses for the COMI SSWB item. At the 1-year and 2-year follow-ups, a comparable proportion of patients reported an acceptable symptom state (PASS 1): 43.3% and 43.9%, respectively. For the PASS 2, the proportions were 24.0% and 24.6%, respectively. Table 8.2 Distribution of responses in relation to the COMI single item used to define the acceptability of the symptom state. Total patient sample 1-year follow up (n= 780) n (%) Total patient sample 2-year follow up (n= 508) n (%) Very satisfied (1) 187 (23.9) 125 (24.6) Somewhat satisfied (2) 151 (19.4) 98 (19.3) Neither satisfied nor dissatisfied (3) 113 (14.5) 61 (12.0) Somewhat dissatisfied (4) 152 (19.5) 78 (15.4) Very dissatisfied (5) Acceptability of current state SSWB 08 177 (22.7) 146 (28.7) Acceptable (1-2) : Unacceptable state (3-5) yes : no 338 (43.3) : 442 (56.7) 223 (43.9) : 285 (56.1) Acceptable (1) : Unacceptable state (2-5) yes : no 187 (23.9) : 593 (76.1) 125 (24.6) : 383 (75.4) COMI, Core Outcome Measures Index; SSWB symptom-specific well-being. Area under the curve (AUC) and threshold for PASS The ROC analyses revealed that the AUCs for each definition of PASS, for each subgroup, and for each follow-up assessment were >0.7 (0.83≤AUC≤0.96; Tables 8.3 and 8.4). The absolute ODI threshold for each definition of PASS was 22, irrespective of the time of follow up (sensitivity [Se]: 78.3%, specificity [Sp]: 82.1% and Se: 80.7%, Sp: 85.6%, with 79.6% and 83.5% correctly classified for the 1- and 2-year follow-ups respectively; Table 8.3 and Figure 8.2). Table 8.3 Results of ROC analyses AUC 95% CI Absolute ODI threshold Se % Sp % % Correctly classified PASS 1 0.89 0.86-0.91 22 78.3 82.1 79.6 PASS 2 0.89 0.86-0.91 16 80.2 82.0 81.5 PASS 1 0.91 0.89-0.93 22 80.7 85.6 83.5 PASS 2 0.90 0.87-0.92 18 82.4 80.7 81.1 1-year follow up (n=780) 2-year follow up (n=508) PASS 1, patient acceptable symptom state with COMI SSWB answers ‘very satisfied’ and ‘somewhat satisfied’; PASS 2, patient acceptable symptom state with COMI SSWB answer ‘very satisfied’; Se, sensitivity; Sp, specificity; ROC, receiver operating characteristic; AUC, area under the curve; 95% CI, 95% confidence interval; ODI, Oswestry Disability Index; COMI, Core Outcome Measures Index; SSWB symptom-specific well-being. PASS for ODI 159 Threshold for PASS Table 8.3 shows the absolute ODI threshold values for each definition of PASS and for each follow-up assessment. The ODI threshold for PASS was 22, irrespective of the time of follow up (AUC= 0.89 [Se: 78.3%, Sp: 82.1%] and AUC= 0.91 [Se: 80.7%, Sp: 85.6], with 84.0% and 84.2% correctly classified for the 1- and 2-year follow-ups, respectively; Figure 8.2). Figure 8.2 ROC for patient acceptable symptom states (PASS 1) using one and two-year follow-up ODI-values. 08 AUC, area under the curve; 95% CI, 95% confidence interval; PASS ,1 patient acceptable symptom state with ‘very satisfied’ and ‘somewhat satisfied’; ODI, Oswestry Disability Index; ROC, receiver operating characteristic. Sensitivity analyses Definition of success For PASS 2, the ODI threshold was either 16 (1-year follow up) or 18 (2-year follow up) (respectively, AUC= 0.91 [Se: 80.2%, Sp: 82.0%] and AUC= 0.90 [Se: 82.4%, Sp: 80.4%], with 81.5% and 81.1% correctly classified; Table 3). Subgroups The absolute ODI-22 threshold (PASS 1) for the two follow-up assessments was robust (Table 8.4a). Table 8.4a shows that the ODI threshold was slightly higher in patients with previous surgery (ODI=23) than in those with no previous surgery (ODI=22). Similarly, the ODI for decompression patients was slightly higher (ODI=23) than for fusion patients (ODI=22).At 2-year follow up the ODI threshold was 22 for all subgroups, except for those with perioperative surgical complications as documented by the surgeon (ODI=20) and those who self-reported no complications (ODI=24; Table 8.4a). For PASS 2 the ODI threshold values varied (14-18) depending on the subgroup under investigation and the follow up used (Table 8.4b). 407 female 305 ≥ 65 600 No 293 Fusion 736 No 192 588 - Yes - No Complications – patient 36 Yes Complications – surgical 487 Decompression Surgical procedure 180 Yes Previous surgery 475 < 65 Age group 373 male Gender 0.88 0.91 0.88 0.89 0.83 0.91 0.88 0.88 0.88 0.89 0.87 0.91 0.85-0.90 0.86-0.95 0.86-0.91 0.78-0.99 0.78-0.88 0.89-0.94 0.85-0.91 0.84-0.93 0.84-0.92 0.86-0.92 0.83-0.91 0.88-0.94 22 22 22 22 22 23 22 23 22 22 22 22 76.5 75.9 76.2 72.7 72.3 82.2 77.8 71.2 80.6 78.4 76.0 81.6 Se % 9.02 0.01 0.07 2.97 Chi2 value 80.4 87.6 82.0 1.27 80.0 0.02 77.9 84.6 82.6 81.0 77.3 85.3 82.3 81.9 Sp % 0.259 0.887 0.003* 0.917 0.795 0.085 p value 243 375 133 472 31 173 335 386 122 191 317 265 0.91 0.90 0.91 0.96 0.88 0.92 0.91 0.90 0.92 0.90 0.88 0.91 AUC 0.88-0.94 0.86-0.95 0.88-0.93 0.89-1.00 0.84-0.93 0.89-0.95 0.88-0.94 0.83-0.96 0.88-0.96 0.86-0.93 0.84-0.92 0.87-0.94 95% CI n Absolute ODI threshold 2-year follow up (n= 508) 95% CI 24 22 22 20 22 22 22 22 22 22 22 22 Absolute ODI threshold 81.2 84.5 79.9 90.7 80.7 83.6 80.6 81.3 82.7 79.4 80.0 81.3 Se % 82.9 84.0 85.9 79.0 82.2 87.2 84.9 87.5 81.8 81.7 85.7 85.5 Sp % 0.01 2.18 1.31 0.12 0.91 0.01 Chi2 value 0.934 0.872 0.252 0.726 0.340 0.996 p value AUC n 08 1-year follow up (n= 780) 160 PASS for ODI Table 8.4a. Sensitivity analyses for PASS 1 for subgroups of patients. 435 female 305 ≥ 65 600 No 293 Fusion 736 No 192 588 Yes No Complications – patient 36 Yes Complications – surgical 487 Decompression Surgical procedure 180 Yes Previous surgery 475 < 65 Age group 355 male Gender n 0.91 0.91 0.89 0.88 0.87 0.92 0.87 0.95 0.89 0.89 0.91 0.91 AUC 0.88-0.94 0.85-0.97 0.86-0.92 0.73-1.00 0.82-0.92 0.87-0.93 0.84-0.90 0.91-0.98 0.85-0.93 0.86-0.92 0.87-0.94 0.87-0.94 95% CI 1-year follow up (n= 780) 18 18 16 18 18 16 16 16 16 18 18 15 Absolute ODI threshold 84.6 91.4 80.8 71.4 80.6 81.7 79.3 85.7 82.1 82.4 80.2 81.4 Se % 84.9 80.4 81.6 72.4 79.2 80.7 81.0 84.7 79.0 80.9 80.1 84.3 Sp % 0.04 0.00 0.95 8.20 0.01 0.97 Chi value 2 0.851 0.968 0.230 0.004* 0.908 0.325 p value 375 133 472 31 173 335 386 122 191 317 265 243 n 95% CI 0.85-0.94 0.86-0.94 0.86-0.94 0.84-0.94 0.77-0.95 0.87-0.94 0.86-0.94 0.83-0.93 0.89-1.00 0.88-0.93 0.88-0.94 0.86-0.95 AUC 0.89 0.90 0.90 0.90 0.86 0.91 0.90 0.88 0.96 0.91 0.90 0.91 2-year follow up (n= 508) 18 14 18 12 16 16 16 16 18 16 18 16 Absolute ODI threshold 78.5 84.4 81.2 71.4 78.1 82.9 82.5 75.0 80.0 82.3 82.3 82.5 Se % 80.5 88.1 80.9 87.5 83.9 85.0 83.0 89.0 81.5 85.2 82.3 83.3 Sp % 0.02 0.17 0.44 0.80 0.01 0.05 Chi2 value 0.893 0.676 0.507 0.374 0.984 0.825 p value PASS for ODI 161 Table 8.4b. Sensitivity analyses for PASS 2 for subgroups of patients. 08 162 PASS for ODI Discussion In the present study we identified the Oswestry Disability Index (ODI; version 2.1a) score corresponding to a ‘patient acceptable symptom state’ (PASS). The absolute cut-off ODI value (threshold) was generally estimated to be ≤22 and seemed to be robust for different subgroups of patients, and at different follow-up assessments. This threshold is similar to the ‘normal’ value as recently defined by van Hooff et al. [11]. Our finding of the ODI threshold of ≤22 is in line with other studies. Tonosu et al. calculated the cut-off values for the presence or absence of low back pain (LBP) in a random sample of people working at a Japanese Internet research company (n=1,200). The authors found a similar threshold of ODI≥22 in those who had LBP and disability [23]. In a large heterogeneous sample (n=774) of inpatients with spinal disorders (e.g. acute and chronic LBP, herniation, stenosis, scoliosis) a discharge threshold of ODI≤30 was found [24]. To our knowledge, the present study is the first to report ODI threshold values based on PASS in patients with degenerative lumbar spine disorders who have undergone lumbar spine surgery. 08 To quantify the PASS for the ODI, we used different external criteria as the anchor for a successful outcome. When performing the sensitivity analyses with PASS 2 (‘very satisfied’), we found lower ODI thresholds (ODI≤16-19), but with similar satisfactory areas under the curve (AUC>0.7; Table 3). Overall, the results of the sensitivity analysis turned out to be robust; a more stringent definition of success yielded lower ODI threshold values, meaning that the anchor to perform the present study was well chosen. As the use of absolute thresholds might be population-dependent, we performed different sensitivity analyses for subgroups of patients. In the main analyses for PASS (‘very satisfied’ and ‘somewhat satisfied’), the AUCs were satisfactory (AUC>0.7; [22]). This means that for all subgroups of patients studied, the ODI discriminates between acceptable and unacceptable symptom states compared with the anchor used. The overall ODI threshold for each subgroup was ≤22. Only for ‘previous surgery (yes)’ and ‘surgical procedure (decompression; commonly performed for lumbar disc herniation and lumbar spinal stenosis)’ a slightly different ODI threshold was found (each ODI≤23; Table 8.4a). However, these differences in ODI thresholds were too small to be clinically meaningful. To establish whether the ODI score for the PASS was time independent we provided estimations for the ODI thresholds for two different commonly used times of follow up. We found no meaningful differences between the 1-year and 2-year follow-up thresholds: the absolute values were similar and the 95% confidence intervals of the AUCs showed overlap (Table 8.3). This means that the ODI-22 threshold associated with a satisfied symptom state remains consistent. Limitations Some limitations of the present study should be mentioned. First, the EUROSPINE Spine Tango Registry contains data from surgically treated patients in different centers and in various countries [16]. Participation in the registry is voluntary and the ODI is not a compulsory outcome measure for the majority of the centers, which instead use the COMI as standard. Therefore, selection bias might be introduced. The completion of the PROMs is dependent on patients’ willingness to cooperate. In a previous registry study on Spine Tango data it was suggested that the proportion of dural lesions in spinal stenosis was a good indicator of the PASS for ODI completeness and the ‘honesty’ of the data submitted [25]. The authors found comparable proportions to those seen in the Swedish SweSpine registry, which suggested credible or at least similarly honest reporting. Second, we performed several sensitivity analyses to test the robustness of the ODI-22 threshold. A previous study has shown that a higher preoperative pain level is associated with a higher acceptable pain level postoperatively [26]; as such, a sensitivity analysis including pre-defined subgroups with differing pre-operative disability would have been interesting. However, the ODI is not a compulsory outcome measure in the registry and insufficient baseline data were available for such an analysis. Further research is needed to explore the influence of baseline ODI scores on the acceptable ODI score postoperatively. Moreover, we did not perform a specific subgroup analysis based on underlying pathologies of degenerative disorders of the lumbar spine. Although based on the results of a recently published study [26] a slightly lower threshold might be expected for lumbar disc herniation, the clinical relevance of such a difference is uncertain and might be arbitrary as this value could be regarded as a value seen in ‘normal’ and healthy populations (i.e. ODI≤22) [11]. Third, one of the participating centers used a version of the COMI that had not been formally translated and validated. However, we do not think this would have introduced any major bias, as we used only the single ‘symptom-specific well-being’ item of the COMI, and the original item [19,20] is simply and clearly stated and not likely to be subject to different interpretations. Fourth, to create the dataset for the current study, we started with 30,878 surgically treated patients. Because of the strict inclusion criteria, such as an ODI and COMI completed on the same day, a large amount of data were excluded, leaving us with 780 and 508 patients at the 1-year and 2-year follow-ups, respectively. Moreover, in the present study we used the data of the last available follow up on both ODI and COMI. By using this criterion we might have lost the data of patients who completed the questionnaires at both timepoints, as only the last assessment was included. However, both samples were large enough for analyses and had similar baseline characteristics, with the exception of the proportion that had previous surgery. Fifth, the COMI SSWB item was used as an external anchor to assess the achievement of an acceptable symptom state at follow up. Although the COMI has been shown to be valid for patients with degenerative disorders of the lumbar spine undergoing surgery [19], the single SSWB item was not specifically tested for its unique psychometric properties. To evaluate the validity of the SSWB item as an anchor we carried out a post-hoc analysis of the strength of the correlations between the postoperative ODI scores and the responses on the SSWB (Pearson r) for the 1- and 2–year follow-up assessments: the coefficients were r=0.73 and r=0.77, respectively. These correlations are interpreted as ‘strong’ (0.51≤r≤1.00) [27,28]. In evaluating the validity of transition ratings, a strong correlation (r≥0.50) with change scores in a relevant health-related quality of life questionnaire suggests that the transition rating may be used as an anchor[29]. By analogy, we believe that, with correlation coefficients ≥0.50, the use of the COMI SSWB item as a reference standard (anchor) to define the ODI threshold for an acceptable symptom state is justified. Finally, for the main analysis we used the responses ‘very satisfied’ and ‘somewhat satisfied’ on the COMI SSWB item to define an acceptable symptom state. This was supported by the findings of a previous study in which ROC analysis using the original dichotomized PASS (acceptance of the ‘current state’ [yes or no]) as the external criterion revealed that ‘very satisfied, ‘somewhat satisfied’ and ‘neither satisfied / nor dissatisfied’ were considered as ‘acceptable’ [30], together with our considerations as to what should be considered minimally acceptable as a response for a ‘state for the rest of one’s life’. We performed a sensitivity analysis with an even more stringent definition, which, as expected, resulted in lower ODI cut-off values. However, the decision as to which definition of ‘acceptable state’ should be used remains somewhat arbitrary. 163 08 164 PASS for ODI Conclusions In the present study, we determined that an absolute ODI value of ≤22 best indicated a satisfactory symptom state in a large sample of patients from the Spine Tango Registry who had undergone surgery for degenerative disorders of the lumbar spine. As this threshold appeared to be fairly consistent across sub-populations, we suggest that the same common threshold could be used for all degenerative lumbar spine disorders when defining whether a patient has reached an acceptable state after spine interventions (i.e. is a ‘responder’). As the concepts of PASS and change are complementary, in line with another group [31,32], we recommend using this threshold alongside the commonly used ‘change score’ measure in reporting success. We suggest to report both measures and that the results should be expressed as the proportions of patients achieving both measures at follow-up assessment. 08 Acknowledgement The authors would like to thank Johanna E. Vriezekolk for her critical and independent review of the analyses performed and the final version of the manuscript. The participants of the Eurospine Spine Tango Register are acknowledged for their continuous contribution that makes these studies possible reflecting the daily practice of spine surgeons. The data of the following centres were used (in alphabetic order of country, city, hospital and department): Dept. of Spinal Surgery in Royal Adelaide Hospital (Australia); Dept. of Orthopedic Surgery and Traumatology in University Hospital of Cologne (Germany); Dept. of Special Spine Surgery in Leopoldina Hospital of Schweinfurt (Germany); Dept. of Orthopaedic surgery in Tan Tock Seng Hospital Singapore (Singapore); Dept. of Orthopedic Surgery in University Hospital of Ljubljana (Slovenia); Dept. of Neurosurgery in Bethesda Hospital of Basel (Switzerland); Dept. of Orthopedic Surgery in Salem Hospital of Bern (Switzerland); Dept. of Spine Surgery in The Spine Center Thun (Switzerland); MEMdoc Hospital of Thun-Simmental AG (Switzerland); Spine Unit of Nuffield Oxford Centre (UK); Division of Spine Surgery in NYU Hospital for Joint Diseases of New York (USA). PASS for ODI 165 References 1. Global Burden of Disease Study 2013 Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015;386:743-800 2. Poolman RW, Swiontkowski MF, Fairbank JC, Schemitsch EH, Sprague S, de Vet HC. Outcome instruments: rationale for their use. J.Bone Joint Surg.Am. 2009;91 Suppl 3:41-49 3. Fairbank JC, Couper J, Davies JB, O'Brien JP. The Oswestry low back pain disability questionnaire. Physiotherapy. 1980;66:271-273 4. 5. Fairbank JC, Pynsent PB. The Oswestry Disability Index. Spine (Phila Pa 1976.) 2000;25:2940-2952 PROQOLID. Patient-Reported Outcome and Quality of Life Instruments Database - Oswestry Disability Index (ODI), [Mapi Research Trust], 2014. [cited 2015 November]; Available from: http://www.proqolid.org/ instruments/ oswestry_disability_index_odi 6. Clement RC, Welander A, Stowell C, Cha TD, Chen JL, Davies M et al. A proposed set of metrics for standardized outcome reporting in the management of low back pain. Acta Orthop. 2015:523-533 7. Kvien TK, Heiberg T, Hagen KB. Minimal clinically important improvement/difference (MCII/MCID) and patient acceptable symptom state (PASS): what do these concepts mean? Ann.Rheum.Dis. 2007;66 Suppl 3:iii40-iii41 8. van der Roer N, Ostelo RW, Bekkering GE, van Tulder MW, de Vet HC. Minimal clinically important change for pain intensity, functional status, and general health status in patients with nonspecific low back pain. Spine (Phila Pa 1976.) 2006;31:578-582 9. Kirwan JR. Minimum clinically important difference: the crock of gold at the end of the rainbow? J.Rheumatol. 2001;28:439-444 10. de Vet HC, Foumani M, Scholten MA, Jacobs WC, Stiggelbout AM, Knol DL et al. Minimally important change values of a measurement instrument depend more on baseline values than on the type of intervention. J.Clin.Epidemiol. 2015;68:518-524 11. van Hooff ML, Spruit M, O'Dowd JK, van Lankveld W, Fairbank JC, van Limbeek J. Predictive factors for successful clinical outcome 1 year after an intensive combined physical and psychological programme for chronic low back pain. Eur.Spine J. 2014;23:102-112 12. Tubach F, Ravaud P, Baron G, Falissard B, Logeart I, Bellamy N et al. Evaluation of clinically relevant states in patient reported outcomes in knee and hip osteoarthritis: the patient acceptable symptom state. Ann. Rheum.Dis. 2005;64:34-37 13. Tubach F, Ravaud P, Martin-Mola E, Awada H, Bellamy N, Bombardier C et al. Minimum clinically important improvement and patient acceptable symptom state in pain and function in rheumatoid arthritis, ankylosing spondylitis, chronic back pain, hand osteoarthritis, and hip and knee osteoarthritis: Results from a prospective multinational study. Arthritis Care Res.(Hoboken.) 2012;64:1699-1707 14. Dougados M. It's good to feel better but it's better to feel good. J.Rheumatol. 2005;32:1-2 15. Roder C, Chavanne A, Mannion AF, Grob D, Aebi M. SSE Spine Tango--content, workflow, set-up. www. eurospine.org-Spine Tango. Eur.Spine J. 2005;14:920-924 16. Melloh M, Staub L, Aghayev E, Zweig T, Barz T, Theis JC et al. The international spine registry SPINE TANGO: status quo and first results. Eur.Spine J. 2008;17:1201-1209 17. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig LM et al. The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. Ann.Intern.Med. 2003;138:W1-12 18. Mannion AF, Elfering A, Staerkle R, Junge A, Grob D, Semmer NK et al. Outcome assessment in low back pain: how low can you go? Eur.Spine J. 2005;14:1014-1026 08 166 PASS for ODI 19. Mannion AF, Porchet F, Kleinstuck FS, Lattig F, Jeszenszky D, Bartanusz V et al. The quality of spine surgery from the patient's perspective: part 2. Minimal clinically important difference for improvement and deterioration as measured with the Core Outcome Measures Index. Eur.Spine J. 2009;18 Suppl 3:374-379 20. Mannion AF, Porchet F, Kleinstuck FS, Lattig F, Jeszenszky D, Bartanusz V et al. The quality of spine surgery from the patient's perspective. Part 1: the Core Outcome Measures Index in clinical practice. Eur.Spine J. 2009;18 Suppl 3:367-373 21. Altman DG, Bland JM. Diagnostic tests 3: receiver operating characteristic plots. BMJ 1994;309:188 22. de Vet HC, Ostelo RW, Terwee CB, van der Roer N, Knol DL, Beckerman H et al. Minimally important change determined by a visual method integrating an anchor-based and a distribution-based approach. Qual.Life Res. 2007;16:131-142 23. Tonosu J, Takeshita K, Hara N, Matsudaira K, Kato S, Masuda K et al. The normative score and the cut-off value of the Oswestry Disability Index (ODI). Eur.Spine J. 2012;21:1596-1602 24. Park SW, Shin YS, Kim HJ, Lee JH, Shin JS, Ha IH. The dischargeable cut-off score of Oswestry Disability Index (ODI) in the inpatient care for low back pain with disability. Eur.Spine J. 2014;23:2090-2096 25. Munting E, Roder C, Sobottke R, Dietrich D, Aghayev E. Patient outcomes after laminotomy, hemilaminectomy, laminectomy and laminectomy with instrumented fusion for spinal canal stenosis: a propensity scorebased study from the Spine Tango registry. Eur.Spine J. 2015;24:358-368 26. Fekete TF, Haschtmann D, Kleinstuck FS, Porchet F, Jeszenszky D, Mannion AF. What level of pain are patients 08 happy to live with after surgery for lumbar degenerative disorders? Spine J. 2016;16:S12-S18 27. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Earlbaum Associates; 1988 28. Cohen J. A power primer. Psychol.Bull. 1992;112:155-159 29. Guyatt GH, Norman GR, Juniper EF, Griffith LE. A critical look at transition ratings. J.Clin.Epidemiol. 2002;55:900-908 30. Impellizzeri FM, Mannion AF, Naal FD, Hersche O, Leunig M. The early outcome of surgical treatment for femoroacetabular impingement: success depends on how you measure it. Osteoarthritis.Cartilage. 2012;20:638-645 31. Escobar A, Gonzalez M, Quintana JM, Vrotsou K, Bilbao A, Herrera-Espineira C et al. Patient acceptable symptom state and OMERACT-OARSI set of responder criteria in joint replacement. Identification of cut-off values. Osteoarthritis.Cartilage. 2012;20:87-92 32. Escobar A, Riddle DL. Concordance between important change and acceptable symptom state following knee arthroplasty: the role of baseline scores. Osteoarthritis.Cartilage. 2014;22:1107-1110 167 THEME C: Prediction of outcomes 168 169 Chapter 09 The Nijmegen Decision Tool for Chronic Low Back Pain Development of a clinical decision tool for secondary or tertiary spine care specialists van Hooff ML van Loon J van Limbeek J de Kleuver M Published in: PLoS One. 2014;9(8):e104226 170 Abstract Background: In Western Europe low back pain has the greatest burden of all diseases. When back pain persists different medical specialists are involved and a lack of consensus exists among these specialists for medical decision-making in Chronic Low Back Pain (CLBP). Objective: To develop a decision tool for secondary or tertiary spine care specialists to decide which patients with CLBP should be seen by a spine surgeon or by other non-surgical medical specialists. Methods: A Delphi study was performed to identify indicators predicting the outcome of interventions. In the preparatory stage evidence from international guidelines and literature were summarized. Eligible studies were reviews and longitudinal studies. Inclusion criteria: surgical or non-surgical interventions and persistence of complaints, CLBP-patients aged 18-65 years, reported baseline measures of predictive indicators, and one or more reported outcomes had to assess functional status, quality of life, pain intensity, employment status or a composite score. Subsequently, a three-round Delphi procedure, to reach consensus on candidate indicators, was performed among a multidisciplinary panel of 29 CLBPprofessionals (>five years CLBP-experience). The pre-set threshold for general agreement was ≥70%. The final indicator set was used to develop a clinical decision tool. Results: A draft list with 53 candidate indicators (38 with conclusive evidence and 15 with inconclusive evidence) was included for the Delphi study. Consensus was reached to include 47 indicators. A first version of the decision tool was developed, consisting of a web-based screening questionnaire and a provisional decision algorithm. Conclusions: This is the first clinical decision tool, based on current scientific evidence and formal multidisciplinary consensus, that helps referring the patient for consultation to a spine surgeon or a non-surgical spine care specialist. We expect that this tool considerably helps in clinical decision-making spine care, thereby improving efficient use of scarce sources and the outcomes of spinal interventions. Development of NDT-CLBP 171 Introduction In Western Europe Low back pain (LBP) is considered to have the greatest burden of disease for society [1]. In this global burden of disease study LBP is ranked higher than for example cancer, heart disease, cerebrovascular disease, chronic obstructive pulmonary disease and asthma, osteoarthritis or diabetes. In the Netherlands, approximately 44% of the population experiences at least once an episode of LBP, with one in five reporting persistent back pain resulting in chronic low back pain (CLBP; LBP lasting for more than three months [2,3]) with substantial limitations in functional activities after one year [4,5]. As the prevalence of CLBP appears to be increasing [6], CLBP is not only a burden for the patient but the related healthcare costs and productivity due to absence of work have a high health and socioeconomic impact on western societies [7-9]. Not surprisingly, CLBP is among the most common complaints of patients visiting a medical specialist in secondary care, i.e. spine surgeons, physiatrists, rheumatologists, pain consultants. The high number of CLBP patients overwhelms these healthcare providers and a significant number of second opinions and re-interventions are evident. With the limited health care budgets and given the high prevalence of CLBP and its substantial socioeconomically impact, it is essential to use resources of healthcare providers efficiently and to triage CLBP patients adequately in order to make sure that these patients see the right care giver timely. However, as yet such a valid classification system or decision tool is lacking and secondary care medical specialists are failing to reliably identify which patients will benefit from which surgical or non-surgical intervention. One challenge in the development of a decision tool is that the CLBP population is heterogeneous. Therefore, it is unlikely that one intervention benefits all [10]. A longstanding duration of complaints is the only one common defining feature. It makes CLBP a complex problem and in fact it is a symptom referring to the location of the problem rather than a specific diagnosis [11]. The term itself is non-diagnostic for an underlying pathology and lacks specificity. Many authors have emphasized the biospychosocial influences on the development CLBP and persistence of symptoms [12,13] and a broad multidimensional approach is widely recognised. However, the failure to differentiate between underlying causes is one of the reasons that various surgical and non-surgical interventions exist for the same problem [14]. Moreover, studies evaluating these interventions for CLBP have led to inconsistent results [3,15-19] and rarely show more than a small to moderate overall benefit [3,20,21]. It is suggested that several different CLBP patient profiles might be identified which are likely to benefit from different recommended interventions [1,18,19,22-25]. These profiles are based on indicators modifying the effects of interventions [26] and with that related to the outcomes [24]. The ultimate outcomes of spinal interventions are patients’ improved quality of life, restored functional status and relieved pain [27]. However, due to methodological heterogeneity, the current evidence is inconclusive regarding predictive indicators for a successful treatment outcome. Even though it is recognized that CLBP ‘without biological causes’ has to be distinguished from other spinal disorders that respond reliably to surgery [25,28], a recently performed nationwide survey among Dutch spine surgeons showed that even in the group ‘with presumed biological causes’ a lack of consensus exists in surgical decision making [29]. To distinguish patient profiles several treatment outcome-based classifications for decision making exist. However, they are all developed and studied as a guide for non-surgical interventions applied in primary care [24]. As a challenge with 09 172 Development of NDT-CLBP probably the greatest potential for improving outcomes and efficiently guiding patients to the right secondary health care professional (e.g. spine surgeon, pain consultant, physiatrist, rheumatologist), it is recommended to develop a classification system to direct CLBP patients, presented in secondary or tertiary back care, to both surgical and non-surgical interventions, based on biomedical and psychosocial indicators [11,22-25,30,31]. Therefore, the purpose of this study is to develop a clinical decision tool for CLBP, based on evidence in international guidelines and literature, and expert panel consensus using indicators predicting a successful treatment outcome. The decision tool supports secondary or tertiary back care specialists to decide which patients should be considered for a surgical intervention and which patients for a non-surgical intervention and therefore, it aims to triage patients to the appropriate health care professional. The ultimate goal is to improve treatment outcomes and to reduce related costs for society. Methods 09 This study aimed to identify indicators predicting the outcome of interventions and the persistence of CLBP complaints by two stages: a preparatory stage followed by a threeround Delphi study. The preparatory stage consisted of a literature review. As we expected inconclusive evidence in the literature, a formal consensus (Delphi) procedure among a heterogeneous panel of experts in the CLBP field was planned and performed. We used a Modified Delphi Technique in order to realise an optimal integration of research-based knowledge and the clinical experience of experts [32] on this topic. Having identified the predictive indicators, a clinical decision tool, including a screening questionnaire and a provisional decision algorithm, was compiled. In the flow diagram of Figure 9.1 the overall process of the development of the Nijmegen decision tool for CLBP is presented. Figure 9.1 Flow diagram of the development of the Nijmegen Decision Tool for CLBP Purpose as outlined by project team: Development of a clinical decision tool for the triage of CLBP patients Preparatory stage: Evidence from literature Modified Delphi (3 rounds) The Nijmegen Decision Tool for CLBP Screening Questionnaire Implementation web-based in SweSpine Register All new patients prospectively registered (start population cohort study) Provisional decision algorithm Development of NDT-CLBP 173 Preparatory stage: Evidence from literature The indicator set from which a clinical decision tool can be constructed is based on evidence found in international guidelines and in the literature, as these guidelines are normative for evidence based daily practice. As a starting point, the clinical flag approach [33] for clinical decision-making in CLBP and the indicators as recommended in the guidelines [22,23,34], are used. We performed a literature review searching for indicators predicting outcome of invasive or non-invasive interventions and persistence of CLBP. Appropriate studies were traced using MedLine, EMBASE and the Cochrane Library. The most relevant used search terms were: ‘back pain’ [MesH], ‘chronic’, ‘predict’, ‘prognosis’ [MesH], ‘persistent’, ‘treatment outcome’ [MesH], ‘rehabilitation’ [MesH], ‘surgery’ [MesH]. The search was restricted to include systematic and narrative reviews, randomized controlled trials (RCT) and prospective cohort studies. Studies were included when 1) CLBP was the primary complaint; 2) published in the period 2000-2010; 3) involved either surgical interventions for CLBP or non-surgical interventions or persistence of CLBP complaints; 4) age between 18-65 years; 5) baseline measures of predictive indicators are reported, as the time of assessment may influence the prognostic value of treatment outcome [35,36]; 6) at least one of the reported outcome measures had to assess functional status, quality of life, pain intensity, employment status or a composite score. CLBP was defined as more than three months continual or recurrent episodes of LBP [2,3]. There were no language restrictions. Moreover, reference lists of included articles were scrutinized to identify articles not captured in the database search. When a systematic review was included, the original longitudinal studies (RCT or observational) of that systematic review were excluded from the current sample to avoid duplication or double use of the same data. We used four international guidelines [22,23,31] and one national guideline [34]. The literature search revealed 33 relevant papers: eight systematic reviews [36-43], four narrative reviews [44-47], three randomized studies [48-50], and eighteen observational studies [51-68]. All potential predictive indicators were classified into five main domains: sociodemographic; pain; somatic; psychological; and functioning & quality of life. Of each paper, data of available evidence was extracted regarding the predictive values of measured baseline determinants (indicators). The evidence is weighed according to the Levels of Evidence as defined by the Oxford Centre for Evidence-Based Medicine [69]. Per indicator the evidence is categorized into four categories: 1) indicator with proven predictive value (PV; evidence found that the concerning indicator has predictive value), 2) indicator with proven no predictive value (NP; evidence found that the concerning indicator has no predictive value), 3) indicator with inconclusive evidence (I; conflicting evidence found), and 4) indicator with no evidence found in literature (N). Subsequently, all indicators (PV, NP, and I) were selected and used for Phase 2 of this study (the Delphi Study). Indicators with non-predictive value and categorized NP were excluded from the sample as the evidence showed no predictive value for treatment outcome or for persistence of CLBP complaints. The included indicators are summarized in an evidence table, according to the design used in the related studies (data available in Table S1). Per indicator the evidence was summarized: the evidence is conclusive and of predictive value (C: PV) or the evidence is conclusive and of no predictive value (C: NP) or inconclusive evidence (I). These results are used in the Delphi study (Delphi-1 & 2). 09 174 Development of NDT-CLBP Delphi Study The Delphi technique is a commonly used method to develop clinical guidelines [70] and also used in healthcare indicator research [71]. The technique was originally developed in the 1950s by Dalkey and Helmer at the RAND Corporation as a method of eliciting and refining group judgements [72]. Delphi may be characterized as a systematic method for structuring a group communication process so that the process is effective in allowing a group of experts or ‘expert panel’, as a whole, to deal with a complex problem [73]. The method relies on three key features: 1) anonymous response to guarantee equality in experts opinions, 2) iteration and controlled feedback, and 3) statistical analysis of group responses [72]. The Delphi technique, as recently described [32,71], is a structured process that uses a recommended series of two or three rounds to gather expert opinions. When reaching consensus is difficult or consensus is unclear a physical panel meeting at the end is recommended, under the condition that the meeting should be well structured and should take place in favourable conditions (surrounding and environment) with a moderator (process leader), who is not one of the panellists, to contain the influence of dominant personalities (Modified Delphi Technique). 09 Project team A project team was formed to conduct the process and the research and comprised a methodologist who is also a physician and who has a background in statistics (JvL), an orthopaedic spine surgeon (MdK) and a health scientist (MvH). The responsibilities of this project team were performing a review of clinical predictive indicators, weighing the evidence for each indicator, selecting a panel of experts, developing the questionnaires, organisation and conduct of email rounds and consensus meeting, analysing the responses, and compiling a draft version of the clinical decision tool. Panellists In the area of CLBP treatment different medical specialists are involved and knowledge gaps exist between different medical specialties [24]. Therefore, a heterogeneous group of experts was selected for the expert panel. Moreover, it is known that when exploring areas of uncertainty, a heterogeneous group is appropriate [70] and it is expected that heterogeneity in a decision-making group may lead to better performance [71]. Panellists were asked based on their willingness to participate, their intention to commit to the process, and their recognised knowledge of the topic. They were recruited in one hospital and were included in the panel when they met the following criteria: (1) professional background as a orthopaedic spine surgeon, anaesthesiologist & pain consultant, physiatrist, rheumatologist, psychologist, physical therapist, occupational therapist, psychomotor therapist, nurse practitioner; (2) CLBP care and cure is the main area of professional attention; (3) more than five years of clinical experience in the field; and (4) ability and willingness to respond to each email Delphi round within one week and to join the final Delphi consensus meeting. Delphi procedure The panellists were emailed explaining the purpose and the content of the study. To increase participation the panellists were asked to reply if they were willing to join and whether they intended to commit to the procedure. The whole procedure was performed in two months, April-May 2011. Two email Delphi rounds were planned to reach consensus. Consensus was defined as a ’general agreement of a substantial majority’. The threshold for general agreement was set at ≥70%. If an indicator reached a second time disagreement, the indicator Development of NDT-CLBP 175 is rejected. For the two email rounds participants were asked to respond and reply within one week. A third round, the final consensus meeting was performed to reach consensus about the included items and to construct a first draft clinical decision tool. During this meeting participants were allowed to discuss issues and exchange views supported by evidence, with the aim to resolve issues for indicators that had not passed the threshold for consensus. In each round the purpose and procedure of the current Delphi round and following Delphi rounds were explained. Delphi-1 The initial draft list of indicators extracted from the literature review and arranged in a conceptual framework of domains was provided to the expert panel. They were asked to respond to three main sets of questions (Q). The Q1 set was based on international and national guidelines, which recommend an assessment of a minimal set of consistent prognostic indicators influencing the treatment outcome [22,23,31,34]. Compiled by the project team and supported by the literature review, this minimal set consisted of 32 indicators (‘red’ and ‘yellow’ flags, expectation of recovery, socio-economic status, sick leave, pain severity, prior episodes of LBP), for which agreement (YES or NO) was asked. The Q2 set was based on the results of the literature review and included 26 indicators with weighted evidence for which agreement for inclusion (YES or NO) was asked. Moreover, in Q3 the panellists were given the opportunity to suggest additional indicators for inclusion, based on scientific evidence and provided to the project team, and to write general comments. The items for which ≥70% agreement was reached were selected and included in the draft list for the final Consensus meeting (Delphi-3). The indicators for which consensus was not reached, were included in round 2 (Delphi-2). Delphi-2 In the second round an anonymous feedback report with a summary of results of Delphi-1 was provided. In this summary an overview of results for each question and each indicator was given in count and percentages of agreement. Moreover, all suggested and newly formulated indicators, including the arguments and comments were presented. The Delphi-2 questionnaire contained both those indicators that did not reach the pre-set agreement level of ≥70% (Q1 and Q2; Delphi-1) and those that were newly formulated by the panellists (Q3; Delphi-1). In this round the panellists were requested to indicate with YES or NO which of the indicators of Q1 and Q2 absolutely needed to be included in the list? In Q3 a possibility was given to mention new indicators. The level of agreement was set at ≥70% among the panellists, i.e. these indicators were selected and included in the draft list for the following Consensus meeting (Delphi-3). A second time lack of consensus led to rejection of the concerning indicator. Delphi-3 Consensus meeting Before the meeting all panellists received a covering summary of results on both Delphi rounds which was similarly described and drafted as for the results of the first round. Moreover, a draft list was provided with indicators for which consensus (≥70%), no consensus (<70%) was reached, and the rejected indicators. During the meeting all indicators for which previously no consensus had been reached were reconsidered. Only if new arguments based on scientific or clinical evidence were provided, an attempt to reach a new consensus on that item was made. Moreover, the panellists were encouraged to consider alternative views when consensus could not be achieved [70]. The meeting had a formal character to ensure that all panellists had a chance to express their views, all indicators were considered, no discussion was allowed 09 176 Development of NDT-CLBP and only arguments could be provided, and the panellists made judgements individually. Consensus was reached by voting; raising hands. Only those indicators with ≥70% agreement were included in the final screening questionnaire, all others were rejected. A dedicated and independent process leader is a key element for a successful consensus meeting; this person facilitates the exchange of relevant information [70]. One of the project team members (JvL) is an experienced Delphi round facilitator, who was not one of the panellists, but who ensured that the process ran smoothly and that good-quality un-biased decisions were made. The project leader (MvH), not a member of the expert panel, assisted the process leader in process monitoring, ensured that all procedures ran according to the rules, counted the votes, compiled the minutes during the meeting and provided a full report after the meeting. The report included the followed procedures, the results of the voting rounds, the course of the discussions, the decisions made, and the final list of ‘consensus indicators’. All panellists who joined the consensus meeting received a copy. 09 Development of the ‘Nijmegen decision tool for CLBP’ The final list of ‘consensus indicators’ was used to compile a first version of the clinical decision tool. For the screening questionnaire existing international patient reported outcome measures (PROMs) were screened to identify whether the indicators are covered by these PROMs. The indicators were compared to existing questions used in the Swedish Spine Register (Swespine [www.4S.nu]). These questions were translated and screened for unambiguity and whether they measured the construct as intended by the indicator. The remaining indicators were converted to new questions. The screening questionnaire was built in the Dutch patient interface of Swespine. Based on the list of consensus indicators, international guidelines, and current practice a provisional decision algorithm was constructed. Results Preparatory stage: Evidence from literature An initial draft list with 58 candidate indicators, categorized in five domains, and including the evidence was compiled. Table 9.1 shows the evidence summarized for all candidate indicators (the evidence and references per indicator are available in Table S1). For 38 (66%) candidate indicators conclusive evidence was found indicating a predictive value for treatment outcome or persistence of complaints (C: PV), 15 had inconclusive evidence for predicting outcome or persistence of pain complaints (I), and for five indicators conclusive evidence was found that the concerning indicator is of no predictive value (C: NP). These five indicators were removed from the initial draft list, leaving 53 candidate indicators and they were included in the Delphi study. Development of NDT-CLBP 177 Table 9.1 Results Preparatory stage: Evidence from literature Domain Sociodemographic Category 1 Study design 2 Evidence 3 SR n RCT n PC n NR n Personal Age Gender Ethnicity Body weight Marital status 4 4 4 1 3 3 - 8 9 1 - 4 3 2 I I C: PV I C: NP Health Smoking Previous back surgery Use of analgesics 5 1 1 - 4 2 1 1 1 I C:PV I Social Education Social status Functioning – leisure Social support 1 1 1 - - 3 1 - 3 1 2 I I I C: PV Work Socio-economic status * Work satisfaction Functioning – work Sick leave * Compensation Litigation Work ability Work adjustment Physical strenuousness 2 5 3 3 3 1 1 1 - 2 2 3 - 2 4 2 1 - C: PV I I C: PV I C: PV C: PV C: PV I Pain Duration Intensity * Intensity – back Intensity – leg Interference daily activities Frequency / preceding (prior) episodes * 4 5 2 2 1 1 - 3 11 3 2 1 1 3 1 2 I C: PV C: PV C: PV C: PV I Somatic Physical & Biological Diagnosis; co morbidities Bulging or protruded disc * Loss of neurological function * Red flags (n=10) * # Strength; endurance; mobility 4 Central sensitisation 4 Postural control; psychomotor speed 4 1 2 1 - 2 2 1 1 1 4 1 1 - 2 - - C: PV C: PV C: PV C: PV C: NP C: NP C: NP Psychic affect Distress * Anxiety * 6 3 2 2 3 2 4 4 C: PV C: PV Cognition Catastrophizing * Somatization * Coping * Intelligence 4 5 4 5 1 3 - 7 1 2 - 3 1 4 - C: PV C: PV C: PV C: PV C: NP Psychologic 09 178 Development of NDT-CLBP Behaviour Fear of movement / (re)injury * Expectations – work return * Expectations – outcome / recovery * Self-efficacy (incl. Readiness–to– change) Pain avoidance & pain persistence Functioning & Quality of Life Functioning in daily activities & walking Health-related physical functioning Health-related mental functioning General perceived health 3 1 2 - - 12 3 1 1 4 2 2 - C: PV C: PV C: PV C: PV - 1 - - C: PV 6 1 9 3 C: PV 3 6 1 - 5 1 2 1 1 1 C: PV I C: PV Initial draft list with indicators (n=58) indicating a predictive value for treatment outcome or persistence of back pain, categorized in domains, including the number of studies found per study design and the resulting evidential value. * Recommended in (inter-/) national guidelines; # Pain started age <20 or >50 years, recent trauma, constant progressive pain, history of malignancies, prolonged use of corticosteroid use, HIV, recent unexplained weight loss, structural deformity, infectious disease (CBO 2010) 2 SR Systematic Review; R Randomized Clinical Trial; PC Prospective Cohort study; NR Narrative Review; n number of studies 3 I Inconclusive evidence; C Conclusive evidence; PV Predictive Value; NP No Predictive value 4 Not included for phase 2 Delphi Study 1 09 Delphi Study Participants A panel of 29 experts met the inclusion criteria and agreed to participate (orthopaedic spine surgeon [n=7], anaesthesiologist & pain consultant [n=3], physiatrist [n=3], rheumatologist [n=1], psychologist [n=4], physical therapist [n=7], occupational therapist [n=1], psychomotor therapist [n=1], nurse practitioner [n=2]). The response rate for the first Delphi round (Delphi-1) was 76% (n=22) and for Delphi-2 69% (n=20). The main reason for not responding in the first two rounds was due to absence from work and none of the approached panellists did not respond on both email rounds. All 29 panellists (100%) attended the final consensus meeting (Delphi-3). Delphi-1 As shown in Figure 9.2, 48 indicators were selected in the first round based on consensus (≥70% agreement level). For five indicators consensus was not reached. Moreover, 26 indicators were newly formulated by the panel in the open end question (sociodemographic n=9; pain n=4; somatic n=7; psychologic n=3; functioning and quality of life n=3) and these indicators were added to the Delphi-2 questionnaire. These 26 indicators consisted of: 1. six indicators mentioned in the Dutch guidelines for general practitioners: self-management of complaints, previous interventions, daily course of pain complaints, influence of rest, mobility and posture, previous episodes, and comorbidities (range and severity) [74]. The last two show overlap with previously identified indicators. 2. one indicator for inflammatory LBP (Calin criteria [75]). 3. ten newly formulated indicators with overlap with indicators of the initial draft list. 4. nine indicators with no predictive evidence and were rejected from the sample. No further comments were made. Development of NDT-CLBP 179 Delphi-2 As shown in Figure 9.2, 22 indicators were presented, including the 17 newly formulated and the five indicators for which no consensus was reached in Delphi-1. Of these, 14 reached the pre-set ≥70% agreement level for consensus. Five indicators were rejected as for the second time no consensus was reached. No new indicators were suggested and no further comments were made. Delphi-3 Consensus meeting All indicators on which consensus were reached in either Delphi-1 or 2 (62 indicators; 48+14) and those indicators no consensus was reached in Delphi-2 (3 indicators) were briefly discussed (Figure 9.2). After each indicator the panellists voted whether they still agreed or not. As shown in Table 2, at the meeting consensus was reached for 47 indicators (pre-set ≥70% agreement level), whereas eight indicators reached no consensus. These indicators were rejected, as well as ten indicators showing an overlap with the initial indicators. Consensus was reached to re-formulate one indicator ‘Body weight’ into ‘Body weight & BMI’ and another indicator ‘Pain-interference daily activities’ switched domains from ‘Pain’ to the domain ‘Functioning & Quality of Life’. The remaining 47 indicators formed the backbone of the screenings questionnaire (36 with conclusive evidence for predictive value [77%] and 11 with inconclusive evidence; Table 9.2). 09 180 Development of NDT-CLBP Figure 9.2 Results of the Delphi Study. At each stage of the Delphi study the expert panel consensus for presented indicators is shown. To reach consensus the level of agreement was set at ≥70%. Indicators reaching full consensus were included in the Nijmegen Decision Tool. Preparatory stage Evidence from literature Initial draft list 58 candidate indicators Predictive value Rejected: n = 5 NO YES Final list for Delphi 53 indicators (n = 53) Delphi-1 Email round Newly suggested n = 26 Consensus (≥ 70%) n = 48 YES Delphi-1 Consensus? n=53 Predictive value Rejected: n = 9 YES NO No consensus (< 70%) n=5 09 NO n = 17 (Overlap * n=10) Delphi-3 Consensus meeting Delphi-2 Email round YES Consensus (≥ 70%) n = 14 YES Delphi-2 Consensus? n=22 NO Rejected: n = 5 NO No consensus (< 70%) n=3 Delphi-3 Consensus meeting n=65 ≥ 70% n=62 (Delphi-1&2) ≥ 70% n=3 (Delphi-2) YES Inclusion in Nijmegen Decision Tool Consensus (≥ 70%): 47 indicators n=36 conclusive evidence for predictive value n=11 inconclusive evidence * overlap doublure in the definition of indicators NO Rejected: n = 18 Overlap * n=10 No consensus < 70% n=8 Development of NDT-CLBP 181 Table 9.2 Results of the Delphi Study Domain Category Evidence Sociodemographic Personal Age Gender Body weight & BMI I I I Health Smoking Previous back surgery Use of analgesics Self-management of complaints Interventions in the past I C I C C Social Social status Functioning – leisure Social support I I C Work Socio-economic status * Work satisfaction Functioning – work Sick leave * Litigation C I I C C Duration Intensity * Intensity – back Intensity – leg Frequency / preceding (prior) episodes * Daily course of pain complaints Influence of rest, mobility, and posture I C C C I C C Diagnosis; co morbidities (Red Flag) Bulging or protruded disc * (Red Flag) Loss of neurological function * (Red Flag) Red flags (n=11) * Pain started age < 20 or > 50 years Significant trauma Pain is constant and non-mechanical Pain in thoracic spine Deformities (i.e. scoliosis, lumbar kyphosis) Previous history of malignities/cancer History of intravenous drug use AIDS / HIV Currently steroid use Recent unexplained weight loss Calin criteria for axial spondylarthritis C C C C Pain Somatic Physical & Biological Psychologic C Psychic affect Distress * Anxiety * C C Cognition Catastrophizing * Somatization * Coping * C C C 09 182 Development of NDT-CLBP Behaviour Fear of movement / (re)injury * Expectations – work return * Expectations – outcome / recovery * Functioning & Quality of Life Functioning in daily activities & walking Pain-interference daily activities Health-related physical functioning C C C C C * Recommended in international guidelines Newly formulated indicators are printed in italics I Inconclusive evidence; C Conclusive evidence Final list with ‘full consensus’ indicators categorized in domains, including the resulting evidential value. The ‘Nijmegen decision tool for CLBP’ A first version of a clinical decision tool consisting of two parts was drafted by the project team: (1) A screening questionnaire, including all 47 indicators, and (2) a provisional decision algorithm. 09 1. The screening questionnaire For the backbone of the screening questionnaire existing international patient reported outcome measures (PROMs) with well-established psychometric properties were screened to identify whether the 47 identified indicators were covered by these existing questionnaires (Table 9.3). Four of the 47 indicators are outcome indicators and adequately measured by the Oswestry Disability Index (ODI, version 2.1a) for functional status, the Short-Form 36 Health Survey Questionnaire (SF36) and the EuroQol (EQ-5D) for quality of life, and the Numeric Rating Scale (NRS) for back and leg pain. The STarT back is used as a screening tool for identifying the amount of risk for three psychological indicators (distress, catastrophizing, and fear of movement / (re)injury; i.e. ‘yellow flags’). The remaining 40 indicators were compared to existing questions in the Swespine register. Analogous questions were translated and the remaining indicators were added as dichotomous or multiple choice questions in the final questionnaire. The complete screening questionnaire is available from the authors. 2. The provisional decision algorithm The provisional decision algorithm is based on the flag approach and based on current practice. The red flag signs are thought to be associated with underlying pathology. Therefore, in the algorithm the presence of ≥ 1 red flag (e.g. previous history of malignancies, trauma) is indicative for a consultation by a spinal surgeon, whereas a high risk on yellow flags (i.e. distress, catastrophizing cognitions) is second most decisive as a high risk on yellow flags might be predictive for treatment failure Development of NDT-CLBP 183 Table 9.3 Backbone screening questionnaire Domains Flag approach [33] Results Phase 2 (current study) Indicators (n=47) Questions Sociodemographic Blue & Black 13 Multiple choice 3 Dichotomous 5 Multiple choice 2 NRS (0-10) * Pain n.a. Somatic Red 14 Dichotomous Psychosocial Yellow 3 STarT back screening tool [88] 4 Multiple choice 1 Dichotomous 2 ODI (v2.1a); SF36; EQ-5D * Functioning & Quality of Life n.a. * standard & agreed to implement in Swespine register www.4s.nu Discussion The purpose of this study was to develop a clinical decision tool for secondary or tertiary care specialists to decide which patients with chronic low back pain (CLBP) should be seen by a spine surgeon for consideration of a beneficial surgical intervention (including invasive pain management), and which patients in the future should best be seen by other medical specialists, e.g. physiatrists, rheumatologists or pain consultants. A study, consisting of a preparatory stage in which evidence from literature was summarized followed by a threeround Delphi study, contributed to the developed Nijmegen clinical decision tool for CLBP, which includes 1) a patient-based and web-based screening questionnaire and 2) a provisional decision algorithm. In the preparatory stage of this study, we included in the literature review the evidence found in international guidelines [22,23,31] and the evidence from one national guideline [34], as these guidelines are normative for evidence based daily practice. However, studies included in these guidelines have led to inconsistent results and rarely show more than small to moderate overall benefit for different types of interventions, which makes it difficult to interpret which patient benefits from which intervention. Therefore, we performed a literature search covering the whole spectrum of CLBP ignoring specific medical specialties (explicit knowledge). This is supplemented by professional state-of-the-art knowledge derived from experiences in daily practice and collegial meetings and conferences, in a formal consensus (Delphi) study (implicit knowledge). 09 184 Development of NDT-CLBP The screening questionnaire The literature search revealed a large number of published studies (n=33) related to the identification of predictive indicators for a successful treatment outcome or the prediction of persistence of CLBP complaints. As expected the result of this study is a long list of predictive indicators (n=47), with most of them (77%) having scientific evidence for predictive value. To list and classify the indicators in Table 9.2 and 9.3 we used the conceptual model of patient outcomes [76] and identified five main domains: Sociodemographic, Pain, Somatic, Psychologic, and Functioning and Quality of Life. Overall, we found strong predictive evidence for successful outcome of spinal surgery for: previous back surgery and biological indicators (i.e. diagnosis; co-morbidities as diabetes, bulging or protruded disc, loss of neurological function, and ‘red flags’). In this study consensus was reached to add BMI and smoking as indicators in the screening questionnaire and to evaluate their contribution to outcome of surgical interventions over time. Although the evidence is growing that high BMI [77] and smoking [63,64,78-80] are predictive for a poor outcome after surgery, the current scientific evidence is still inconclusive. Along with the predictive value of psychological indicators (yellow flags) [38,39,48] and expectations for treatment outcome [37,43,78] and work return [37,43,56], predictive indicators as high disability [36,38,39,48,80], being unemployed [37,43,64], and being involved in litigation and/or compensation claims [36,39,41,51,63,66,78] seem to lead to unfavourable outcome for all CLBP interventions. 09 The provisional decision algorithm For this study we used the recommended clinical flag approach [33] for clinical decisionmaking in CLBP as a starting point [22,23,34]. A diagnostic triage based on ‘red flag’ signs is recommended [22,34,81] as red flag signs are features thought to be associated with a high risk of serious underlying disorders, such as infection, inflammatory disease, cancer or fracture [33,82] or nerve root disease [46]. The presence of a red flag alerts clinicians to the need for further examination and specific management [82-84]. In this study consensus was reached that the presence of one or more red flag signs is indicative for a consultation by a spinal surgeon, which was incorporated as a first step in the provisional decision algorithm. However, the guideline recommendations on diagnostic triage based on red flags are still not very strong [81]. Most of the patients with back pain show at least one positive red flag and do not have a serious underlying condition. Taking the guideline recommendations literally could cause harm. These harms include unnecessary diagnostics, unnecessary exposure to radiation, as well as unnecessary treatments, including surgery [85]. Moreover, a summary [86] of two recently published Cochrane reviews aiming to detect the diagnostic accuracy of red flags to screen for vertebral fracture [84] and malignancy [83] concluded that a lack of evidence exists that one red flag used in isolation can be used to aid a clinician’s judgement. We expect that combinations of red flags and clinical features might appear more informative to assist clinical decision-making [83,84,86]. Even though it is recommended to assess the socalled yellow flags [22,23] as well, it remains unclear what these indicators contribute to actual clinical decision-making. Large prospective studies are needed to evaluate the contribution of these indicators to successful treatment outcome. In this study consensus was reached that a high risk on yellow flags is the second most decisive for surgical or non-surgical interventions. We currently perform further studies to examine multifactorial diagnostic models and with that, the scientific value of combinations of flags and indicators, collected by means of the screening questionnaire, in clinical decision-making for further diagnostics and/or treatment. Development of NDT-CLBP 185 CLBP is a multifactorial health condition and therefore, it has been widely recommended to develop a classification system or a decision tool to direct CLBP patients to interventions based on biomedical and psychosocial indicators [11,22-25,30,31]. To date we are not aware of any study covering the whole spectrum and to our knowledge the Nijmegen decision tool for CLBP is the first published patient screening questionnaire and provisional decision algorithm. The backbone of the screening questionnaire consists of Dutch versions of international validated PROMs. To be able to make our future study results comparable and to be able to perform benchmark studies in the future, we selected commonly used PROMs covering those indicators that are treatment outcome-related (functioning in daily activities with ODI, quality of life with SF36 and EQ5D, and pain intensity with NRS) [87]. These PROMs are also used in the Swedish Spine Register (Swespine [www.4S.nu]). To screen yellow flags and determine the risk of psychological influence on treatment outcome we implemented the Dutch version of the STarT back screening tool [88-90]. Although validated and useful in primary care [88,91-93] further research is needed to evaluate the validity and feasibility of prognostic screening with this tool in secondary or tertiary back care. To our knowledge, for the remaining indicators of the screening questionnaire no validated and reliable questionnaires exist. Large and methodological sound studies are needed for the feasibility and validity of these questions and whether (a combination of) these indicators contribute to successful treatment outcome. In March 2012 the screening questionnaire was implemented in the Dutch patient interface of the Swespine. Swespine was chosen as it is one of the largest, oldest and most studied national registries, which covers both PROMs and clinical results [94], which allows benchmarking data in future. After pilot testing and some minor adjustments (e.g. grammatical and spelling mistakes, wording of questions, and technical issues related to the system), the web-based register started in May 2012. The registry is an ideal instrument to obtain meaningful data prospectively, to define normative values, to identify patient profiles, to confirm differences in treatment outcomes for subpopulations [25,95]. The results can be used for quality assurance, quality improvement and for research purposes [94]. To study the provisional clinical decision algorithm, since May 2012 all LBP patients referred to our clinic complete the screening questionnaire web-based and treated patients are systematically followed over time for two years by completing the same PROMs at predefined follow-up moments. With that, in future it should be possible to identify patient profiles (phenotypes} predicting a beneficial treatment outcome for each type of surgical or non-surgical intervention, for all the referred, treated and untreated patients. At the same time data of the individual patients are presented in PDFformat in the electronic medical record (EMR) of the patient and contributes to individual decision-making in the clinic. Strengths and Weaknesses Although for 36 of the 47 indicators conclusive evidence is available in the literature that they have predictive value for treatment outcome in patients with CLBP, for 11 indicators the evidence was inconclusive. These indicators were included in the Delphi study, based on the expert opinion of a panel of LBP clinicians. However, the formal, structured, and systematic character of the Modified Delphi Technique is of great value in indicator research when scientific evidence for indicators is inconclusive or lacking [71]. Moreover, to overcome the knowledge gaps existing between different medical specialties in the CLBP field [24], we used this technique in a multidisciplinary panel of specialists as it is argued to successfully bring together and to synthesize the knowledge of the whole expert group [71]. All health 09 186 Development of NDT-CLBP professionals came from one hospital specialised in spine care and the generalisation to other secondary or tertiary spine practices in other countries and healthcare environments might be limited. Strength of this study is that the panel included diverse professionals covering the secondary surgical and non-surgical CLBP care. Moreover, the decision tool is based on international accepted guidelines and evidence published in literature, covering the whole spectrum of CLBP. We weighed the evidence in literature according to the Levels of Evidence [69]. By combining this explicit knowledge with the implicit knowledge of the expert panel in the three-round Delphi study, after refinement of the decision algorithm, and after validation of the tool in other settings, we expect that the Nijmegen Decision Tool for CLBP could be used in general secondary and tertiary spine care. Conclusion This study has provided the first clinical decision tool for CLBP patients, based on current scientific evidence and formal consensus, covering the whole spectrum of CLBP. We expect that this relatively simple tool will considerably help a daily spine practice in clinical decisionmaking 1) to select the right CLBP patients for the right interventions, thereby improving the outcomes of spinal interventions, and 2) lead to a reduction in healthcare costs by reducing the number of inappropriate referrals to spine care professionals. 09 Functioning & Quality of Life Psychologic Somatic Physical & Biological Pain N PV Previous back surgery Use of analgesics PV PV PV Social status Functioning - leisure Social support PV N I PV PV N Diagnosis; comorbidities * Bulging or protruded disc * Loss of neurological function * Red Flags (n= 10) * PV PV Anxiety * PV N PV Somatization * Intelligence Coping * N General perceived health IN OUT * International Guidelines [22,23,31] I Health (related mental QoL) PV Expectations - outcome / recovery * I PV Expectations - work return * Health (related physical QoL) PV Avoidance & persistence PV PV Fear of Movement / (re)injury * Functioning in daily activities & walking PV Self-efficacy (including Readiness to Change) Behaviour PV Catastrophizing * Cognition Distress * Psychic affect PV N PV = Predictive value NP = No predictive value I = Inconclusive evidence N = No evidence available PV PV PV PV PV I PV PV N PV N PV PV PV PV PV PV N N I Postural control, psychomotor speed I N Centralization phenomenon I Frequency / preceding (prior) episodes * PV PV PV PV PV N I PV NP N PV I N PV PV N PV I N PV I Strength, endurance, mobility PV Interference daily activities PV Litigation I PV Compensation N PV Sick leave * Intensity - leg N Physical strenuousness Intensity - back N Work adjustment PV PV Functioning - work Intensity * I Work satisfaction PV N Work abiltity Duration PV Socio-economic status * Work PV Education Social I Smoking N NP NP I N N PV PV N N N PV N PV I N PV PV PV PV PV N NP NP NP NP N PV PV PV PV NP N I I N PV PV PV PV N N PV NP N I N NP N NP N NP PV (W) I (S) PV (S) PV (S) PV (S) PV (S) PV (W) PV (W) PV (W) PV (S) I (S) PV (S) PV (S) PV (S) PV (S) PV (S) PV (S) PV (S) PV (S) NP (S) NP (S) NP (S) I (S) PV (S) PV (S) PV (W) PV (S) I (S) PV (W) I (S) PV (S) NP (S) PV (S) I (S) I (S) PV (S) PV (S) PV (W) I (S) I (S) I (S) I (S) PV (S) I (S) PV (W) I (S) NP (S) I (S) PV [38]; I [39] PV [38] NP [41,45,47]; PV [37,44,46] PV [41] NP [43]; PV [38,45] PV [51, 63]; I [39} PV [41] PV [66] PV [38] PV [38] PV [38-39,43,65] PV [38-39] PV [39,56] PV [38-39] PV [38-39,55] PV [41,52] PV [46] NP [67]; PV [42,44-47,49-50,53] NP [47]; PV [42,44-46; 49-50,53,66] PV [41-42,44-45,47,49-50,54,58,60-62,66] NP [66]; PV [41-42,45] NP [47]; PV [41-42,44-46] PV [38-39,51,55-56,63](functioning),[43,65] (walking) PV [43,56,63-64] PV [43,51,56,63-64] PV [56] NP [67]; PV [41,44,46-47,52-53] NP [53,67]; PV [41,45] NP [53]; PV [41,45] NP [41]; PV [53] PV [36] PV [36,40,48], [66](walking] PV [40,66] PV [36,40] I [36] PV [40,48] PV [66] PV [40]; I [36] PV [66] NP [66] NP [36] NP [36] NP [36,40] PV [36] Refences used: Systematic reviews [36-43] Narrative reviews [44-47] Randomised studies [48-50] Observational studies [51-68] PV [43,55] PV [37,53] (W) weak = studies or narrative reviews (S) strong = studies & reviews or systematic reviews NP [55]; PV [56] PV [37,44-45] PV [50] PV [41-42,44-47(pain),49,52,54,57-62,66] PV [55-56] PV [43](comorbidity),[65](comorbidity) NP [66]; PV [44] PV [59](self-efficacy) NP [55]; I [39] NP [41]; PV [53] NP [41,52]; PV [44,46] PV [47](bothersomeness) PV [36,66] PV [36,40,48] PV [51,56] PV [56] PV [38-39,51,55,65] PV [41,44,46-47,52-53,57-62,66] NP [47]; PV [41] PV [38-39,43] NP [36] PV [66]; I [36] I [36} PV [36] PV [36] PV [36] NP [61,66]; PV [44,52] PV [46] PV [44,47,53] NP [43] PV [43,64] NP [53,67]; PV [37,46-47] PV [36] PV [36] PV [44-45] NP [36] I [36] NP [36} PV [52] NP [64]; PV [38] PV [51,64]; I [39] NP [38,43]; PV [51,63-64]; I [39] NP [36] NP [36] NP [36]; PV [66] Non-surgical PV [47] NP [46]; PV [45,47,52,53]; I [66] PV [53] NP [41,53]; PV [46] PV [67] NP [43](weight),[63](weight) PV NP [41](weight); I [46](weight) I Ethnicity Health Body weight NP [38,43,65] NP [52,67] NP Marital status NP [55,65]; PV [39,43,51,56] NP [41,44,47,52,58,60-61,67]; PV [45,66] I Gender NP [38,63]; PV [39,43,55,65] I (S) Surgical NP NP [41,44,47,60-61,67]; PV [45-46,52,66] I Persistence LBP I Personal Sociodemographic Overall (weighted) REFERENCES Non-surgical Persistence LBP Surgical EVIDENCE Age INDICATORS DOMAIN Development of NDT-CLBP 187 Supporting Information Table S1. Preparatory stage – Summarized evidence and references per indicator 09 188 Development of NDT-CLBP References 1. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2197-2223 2. Frymoyer JW. Predicting disability from low back pain. Clin.Orthop.Relat Res. 1992:101-109 3. Henschke N, Kuijpers T, Rubinstein SM, van Middelkoop M, Ostelo R, Verhagen A et al. Trends over time in the size and quality of randomised controlled trials of interventions for chronic low-back pain. Eur.Spine J. 2012;21:375-381 4. Von Korff M, Saunders K. The course of back pain in primary care. Spine (Phila Pa 1976.) 1996;21:2833-2837 5. Picavet HS, Schouten JS. Musculoskeletal pain in the Netherlands: prevalences, consequences and risk groups, the DMC(3)-study. Pain 2003;102:167-178 6. Freburger JK, Holmes GM, Agans RP, Jackman AM, Darter JD, Wallace AS et al. The rising prevalence of chronic low back pain. Arch.Intern.Med. 2009;169:251-258 7. Dagenais S, Caro J, Haldeman S. A systematic review of low back pain cost of illness studies in the United States and internationally. Spine J. 2008;8:8-20 8. Lambeek LC, van Tulder MW, Swinkels IC, Koppes LL, Anema JR, van Mechelen W. The trend in total cost of back pain in The Netherlands in the period 2002 to 2007. Spine (Phila Pa 1976.) 2011;36:1050-1058 9. van Tulder MW. Health technology assessment (HTA) increasingly important in spine research. Eur.Spine J. 2011;20:999-1000 10. Turk DC. The potential of treatment matching for subgroups of patients with chronic pain: lumping versus 09 splitting. Clin.J.Pain 2005;21:44-55 11. Bederman SS. Predicting prognosis in sick-listed low back pain patients: sneaking a peak inside the black box. Spine J. 2010;10:728-730 12. Truchon M. Determinants of chronic disability related to low back pain: towards an integrative biopsychosocial model. Disabil.Rehabil. 2001;23:758-767 13. Weiner BK. Spine update: the biopsychosocial model and spine care. Spine (Phila Pa 1976.) 2008;33:219-223 14. Haldeman S, Dagenais S. A supermarket approach to the evidence-informed management of chronic low back pain. Spine J. 2008;8:1-7 15. Fairbank J, Frost H, Wilson-MacDonald J, Yu LM, Barker K, Collins R. Randomised controlled trial to compare surgical stabilisation of the lumbar spine with an intensive rehabilitation programme for patients with chronic low back pain: the MRC spine stabilisation trial. BMJ 2005;330:1233 16. Mirza SK, Deyo RA. Systematic review of randomized trials comparing lumbar fusion surgery to nonoperative care for treatment of chronic back pain. Spine (Phila Pa 1976.) 2007;32:816-823 17. Brox JI, Nygaard OP, Holm I, Keller A, Ingebrigtsen T, Reikeras O. Four-year follow-up of surgical versus nonsurgical therapy for chronic low back pain. Ann.Rheum.Dis. 2010;69:1643-1648 18. van Middelkoop M, Rubinstein SM, Kuijpers T, Verhagen AP, Ostelo R, Koes BW et al. A systematic review on the effectiveness of physical and rehabilitation interventions for chronic non-specific low back pain. Eur. Spine J. 2011;20:19-39 19. Jacobs WC, Rubinstein SM, Koes B, van Tulder MW, Peul WC. Evidence for surgery in degenerative lumbar spine disorders. Best.Pract.Res.Clin.Rheumatol. 2013;27:673-684 20. Hall H, McIntosh G. Low back pain (chronic). Clin.Evid.(Online.) 2008 21. Wand BM, O'Connell NE. Chronic non-specific low back pain - sub-groups or a single mechanism? BMC. Musculoskelet.Disord. 2008;9:11 22. Airaksinen O, Brox JI, Cedraschi C, Hildebrandt J, Klaber-Moffett J, Kovacs F et al. Chapter 4. European guidelines for the management of chronic nonspecific low back pain. Eur.Spine J. 2006;15 Suppl 2:S192-S300 Development of NDT-CLBP 189 23. National Institute for Health and Clinical Excellence. Low back pain: early management of persistent non-specific low back pain [Report]. NICE Clinical Guideline 88. London, National Collaborating Centre for Primary Care; 2009 24. Fairbank J, Gwilym SE, France JC, Daffner SD, Dettori J, Hermsmeyer J et al. The role of classification of chronic low back pain. Spine (Phila Pa 1976.) 2011;36:S19-S42 25. Fourney DR, Andersson G, Arnold PM, Dettori J, Cahana A, Fehlings MG et al. Chronic low back pain: a heterogeneous condition with challenges for an evidence-based approach. Spine (Phila Pa 1976.) 2011;36:S1S9 26. Kamper SJ, Maher CG, Hancock MJ, Koes BW, Croft PR, Hay E. Treatment-based subgroups of low back pain: a guide to appraisal of research studies and a summary of current evidence. Best.Pract.Res.Clin.Rheumatol. 2010;24:181-191 27. McCormick JD, Werner BC, Shimer AL. Patient-reported outcome measures in spine surgery. J.Am.Acad. Orthop.Surg. 2013;21:99-107 28. Glassman SD, Carreon LY, Anderson PA, Resnick DK. A diagnostic classification for lumbar spine registry development. Spine J. 2011;11:1108-1116 29. Willems P, de Bie R, Oner C, Castelein R, de Kleuver M. Clinical decision making in spinal fusion for chronic low back pain. Results of a nationwide survey among spine surgeons. BMJ Open. 2011;1:e000391 30. Koes BW, van Tulder MW, Thomas S. Diagnosis and treatment of low back pain. BMJ 2006;332:1430-1434 31. Chou R, Loeser JD, Owens DK, Rosenquist RW, Atlas SJ, Baisden J et al. Interventional therapies, surgery, and interdisciplinary rehabilitation for low back pain: an evidence-based clinical practice guideline from the American Pain Society. Spine (Phila Pa 1976.) 2009;34:1066-1077 32. Meijer R, Ihnenfeldt D, Vermeulen M, De Haan R, van Limbeek J. The use of a modified Delphi procedure for the determination of 26 prognostic factors in the sub-acute stage of stroke. Int.J.Rehabil.Res. 2003;26:265270 33. Main CJ, Williams AC. Musculoskeletal pain. BMJ 2002;325:534-537 34. CBO Kwaliteitsinstituut voor de Gezondheidszorg. Ketenzorgrichtlijn Aspecifiek Lage Rugpijn [Report in Dutch], 2009; [cited 2014 March 25]. Available from: http://www.diliguide.nl/document/3272/ ketenzorgrichtlijn-aspecifieke-lage-rugklachten.html 35. Hasenbring M. Predictors of efficacy in treatment of chronic low back pain. Curr.Opin.Anaesthesiol. 1998;11:553-558 36. van der Hulst M, Vollenbroek-Hutten MM, Ijzerman MJ. A systematic review of sociodemographic, physical, and psychological predictors of multidisciplinary rehabilitation-or, back school treatment outcome in patients with chronic low back pain. Spine (Phila Pa 1976.) 2005;30:813-825 37. Heitz CA, Hilfiker R, Bachmann LM, Joronen H, Lorenz T, Uebelhart D et al. Comparison of risk factors predicting return to work between patients with subacute and chronic non-specific low back pain: systematic review. Eur.Spine J. 2009;18:1829-1835 38. den Boer JJ, Oostendorp RA, Beems T, Munneke M, Evers AW. Continued disability and pain after lumbar disc surgery: the role of cognitive-behavioral factors. Pain 2006;123:45-52 39. Celestin J, Edwards RR, Jamison RN. Pretreatment psychosocial variables as predictors of outcomes following lumbar surgery and spinal cord stimulation: a systematic review and literature synthesis. Pain Med. 2009;10:639-653 40. Wessels T, van Tulder M, Sigl T, Ewert T, Limm H, Stucki G. What predicts outcome in non-operative treatments of chronic low back pain? A systematic review. Eur.Spine J. 2006;15:1633-1644 41. Chou R, Shekelle P. Will this patient develop persistent disabling low back pain? JAMA 2010;303:1295-1302 42. Pincus T, Burton AK, Vogel S, Field AP. A systematic review of psychological factors as predictors of chronicity/ disability in prospective cohorts of low back pain. Spine (Phila Pa 1976.) 2002;27:E109-E120 09 190 Development of NDT-CLBP 43. Aalto TJ, Malmivaara A, Kovacs F, Herno A, Alen M, Salmi L et al. Preoperative predictors for postoperative clinical outcome in lumbar spinal stenosis: systematic review. Spine (Phila Pa 1976.) 2006;31:E648-E663 44. Melloh M, Elfering A, Egli PC, Roeder C, Barz T, Rolli SC et al. Identification of prognostic factors for chronicity in patients with low back pain: a review of screening instruments. Int.Orthop. 2009;33:301-313 45. Linton SJ. A review of psychological risk factors in back and neck pain. Spine (Phila Pa 1976.) 2000;25:11481156 46. Cohen SP, Argoff CE, Carragee EJ. Management of low back pain. BMJ 2008;337:a2718 47. Grotle M, Foster NE, Dunn KM, Croft P. Are prognostic indicators for poor outcome different for acute and chronic low back pain consulters in primary care? Pain 2010;151:790-797 48. Smeets RJ, Vlaeyen JW, Kester AD, Knottnerus JA. Reduction of pain catastrophizing mediates the outcome of both physical and cognitive-behavioral treatment in chronic low back pain. J.Pain 2006;7:261-271 49. Flink IK, Boersma K, Linton SJ. Catastrophizing moderates the effect of exposure in vivo for back pain patients with pain-related fear. Eur.J.Pain 2010;14:887-892 50. Hasenbring MI, Hallner D, Rusu AC. Fear-avoidance- and endurance-related responses to pain: development and validation of the Avoidance-Endurance Questionnaire (AEQ). Eur.J.Pain 2009;13:620-628 51. Trief PM, Ploutz-Snyder R, Fredrickson BE. Emotional health predicts pain and function after fusion: a prospective multicenter study. Spine (Phila Pa 1976.) 2006;31:823-830 52. Swinkels-Meewisse IE, Roelofs J, Schouten EG, Verbeek AL, Oostendorp RA, Vlaeyen JW. Fear of movement/ (re)injury predicting chronic disabling low back pain: a prospective inception cohort study. Spine (Phila Pa 1976.) 2006;31:658-664 53. Costa LC, Maher CG, McAuley JH, Hancock MJ, Herbert RD, Refshauge KM et al. Prognosis for patients with 09 chronic low back pain: inception cohort study. BMJ 2009;339:b3829 54. Picavet HS, Vlaeyen JW, Schouten JS. Pain catastrophizing and kinesiophobia: predictors of chronic low back pain. Am.J.Epidemiol. 2002;156:1028-1034 55. Hasenbring MI, Plaas H, Fischbein B, Willburger R. The relationship between activity and pain in patients 6 months after lumbar disc surgery: do pain-related coping modes act as moderator variables? Eur.J.Pain 2006;10:701-709 56. Johansson AC, Linton SJ, Rosenblad A, Bergkvist L, Nilsson O. A prospective study of cognitive behavioural factors as predictors of pain, disability and quality of life one year after lumbar disc surgery. Disabil.Rehabil. 2010;32:521-529 57. Gheldof EL, Crombez G, Van den Bussche E, Vinck J, Van NA, Moens G et al. Pain-related fear predicts disability, but not pain severity: a path analytic approach of the fear-avoidance model. Eur.J.Pain 2010;14:870-879 58. Martel MO, Thibault P, Sullivan MJ. The persistence of pain behaviors in patients with chronic back pain is independent of pain and psychological factors. Pain 2010;151:330-336 59. Costa LC, Maher CG, McAuley JH, Hancock MJ, Smeets RJ. Self-efficacy is more important than fear of movement in mediating the relationship between pain and disability in chronic low back pain. Eur.J.Pain 2011;15:213-219 60. Woby SR, Watson PJ, Roach NK, Urmston M. Are changes in fear-avoidance beliefs, catastrophizing, and appraisals of control, predictive of changes in chronic low back pain and disability? Eur.J.Pain 2004;8:201210 61. Goubert L, Crombez G, Van Damme S. The role of neuroticism, pain catastrophizing and pain-related fear in vigilance to pain: a structural equations approach. Pain 2004;107:234-241 62. Wideman TH, Adams H, Sullivan MJ. A prospective sequential analysis of the fear-avoidance model of pain. Pain 2009;145:45-51 63. Carreon LY, Glassman SD, Djurasovic M, Dimar JR, Johnson JR, Puno RM et al. Are preoperative health-related quality of life scores predictive of clinical outcomes after lumbar fusion? Spine (Phila Pa 1976.) 2009;34:725730 Development of NDT-CLBP 191 64. Atlas SJ, Keller RB, Wu YA, Deyo RA, Singer DE. Long-term outcomes of surgical and nonsurgical management of lumbar spinal stenosis: 8 to 10 year results from the maine lumbar spine study. Spine (Phila Pa 1976.) 2005;30:936-943 65. Sinikallio S, Aalto T, Lehto SM, Airaksinen O, Herno A, Kroger H et al. Depressive symptoms predict postoperative disability among patients with lumbar spinal stenosis: a two-year prospective study comparing two age groups. Disabil.Rehabil. 2010;32:462-468 66. Jensen OK, Nielsen CV, Stengaard-Pedersen K. One-year prognosis in sick-listed low back pain patients with and without radiculopathy. Prognostic factors influencing pain and disability. Spine J. 2010;10:659-675 67. Peters ML, Vlaeyen JW, Weber WE. The joint contribution of physical pathology, pain-related fear and catastrophizing to chronic back pain disability. Pain 2005;113:45-50 68. Harms MC, Peers CE, Chase D. Low back pain: what determines functional outcome at six months? An observational study. BMC.Musculoskelet.Disord. 2010;11:236 69. Oxford Centre for Evidence-based Medicine Levels of Evidence. Levels of Evidence [Online source], 2009 [cited 2014 March 25]. Available from: http://www.cebm.net/ 70. Murphy MK, Black NA, Lamping DL, McKee CM, Sanderson CF, Askham J et al. Consensus development methods, and their use in clinical guideline development. Health Technol.Assess. 1998;2:i-88 71. Boulkedid R, Abdoul H, Loustau M, Sibony O, Alberti C. Using and reporting the Delphi method for selecting healthcare quality indicators: a systematic review. PLoS.One. 2011;6:e20476 72. Dalkey NC. The Delphi Method: An experimental study of group opinion. The Rand Corporatoion; 1969 73. Linstone AH, Turoff M. The Delphi Method; Techniques and Applications. 2002 74. Chavannes AW, Mens JMA, Koes BW, Lubbers WJ, Ostelo R, Spinnewijn WEM et al. NHG-Standaard Aspecifieke LageRugpijn. Huisarts en Wetenschap 2005;48:113-123 75. Rudwaleit M, Sieper J. [Diagnosis and early diagnosis of ankylosing spondylitis]. Z.Rheumatol. 2004;63:193202 76. Poolman RW, Swiontkowski MF, Fairbank JC, Schemitsch EH, Sprague S, de Vet HC. Outcome instruments: rationale for their use. J.Bone Joint Surg.Am. 2009;91 Suppl 3:41-49 77. Athiviraham A, Wali ZA, Yen D. Predictive factors influencing clinical outcome with operative management of lumbar spinal stenosis. Spine J. 2011;11:613-617 78. Soriano JC, Revuelta MS, Fuente MF, Diaz IC, Urena PM, Meneses RD. Predictors of outcome after decompressive lumbar surgery and instrumented posterolateral fusion. Eur.Spine J. 2010;19:1841-1848 79. Sanden B, Forsth P, Michaelsson K. Smokers show less improvement than nonsmokers two years after surgery for lumbar spinal stenosis: a study of 4555 patients from the Swedish spine register. Spine (Phila Pa 1976.) 2011;36:1059-1064 80. Pearson A, Lurie J, Tosteson T, Zhao W, Abdu W, Weinstein JN. Who should have surgery for spinal stenosis? Treatment effect predictors in SPORT. Spine (Phila Pa 1976.) 2012;37:1791-1802 81. Koes BW, van Tulder M, Lin CW, Macedo LG, McAuley J, Maher C. An updated overview of clinical guidelines for the management of non-specific low back pain in primary care. Eur.Spine J. 2010;19:2075-2094 82. Centeno CJ, Elkins WL, Freeman M. Waddell's signs revisited? Spine (Phila Pa 1976.) 2004;29:1392 83. Henschke N, Maher CG, Ostelo RW, de Vet HC, Macaskill P, Irwig L. Red flags to screen for malignancy in patients with low-back pain. Cochrane.Database.Syst.Rev. 2013;2:CD008686 84. Williams CM, Henschke N, Maher CG, van Tulder MW, Koes BW, Macaskill P et al. Red flags to screen for vertebral fracture in patients presenting with low-back pain. Cochrane.Database.Syst.Rev. 2013;1:CD008643 85. Underwood M, Buchbinder R. Red flags for back pain. BMJ 2013;347:f7432 86. Downie A, Williams CM, Henschke N, Hancock MJ, Ostelo RW, de Vet HC et al. Red flags to screen for malignancy and fracture in patients with low back pain: systematic review. BMJ 2013;347:f7095 87. Rihn JA, Berven S, Allen T, Phillips FM, Currier BL, Glassman SD et al. Defining value in spine care. Am.J.Med. Qual. 2009;24:4S-14S 09 192 Development of NDT-CLBP 88. Hill JC, Whitehurst DG, Lewis M, Bryan S, Dunn KM, Foster NE et al. Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial. Lancet 2011;378:1560-1571 89. van Hooff ML, van Lankveld W, Anderson P, Apeldoorn A, van Hartingveld F, Ostelo RWJG. The STarT Back Screening Tool: Dutch version 2011. [cited 2014 March 24]; Available from: http://www.keele.ac.uk/media/ keeleuniversity/ri/primarycare/startbackmicro/Dutch_tool.pdf 90. Apeldoorn A, van Hooff ML, Ostelo RWJG. De STarT Back Screening Tool. FysioPraxis 2013:32-33 91. Morso L, Albert H, Kent P, Manniche C, Hill J. Translation and discriminative validation of the STarT Back Screening Tool into Danish. Eur.Spine J. 2011;20:2166-2173 92. Kongsted A, Johannesen E, Leboeuf-Yde C. Feasibility of the STarT back screening tool in chiropractic clinics: a cross-sectional study of patients with low back pain. Chiropr.Man.Therap. 2011;19:10 93. Bruyere O, Demoulin M, Brereton C, Humblet F, Flynn D, Hill JC et al. Translation validation of a new back pain screening questionnaire (the STarT Back Screening Tool) in French. Arch.Public Health 2012;70:12 94. Stromqvist B, Fritzell P, Hagg O, Jonsson B, Sanden B. Swespine: the Swedish spine register: the 2012 report. Eur.Spine J. 2013;22:953-974 95. Fritzell P, Stromqvist B, Hagg O. A practical approach to spine registers in Europe: the Swedish experience. Eur.Spine J. 2006;15 Suppl 1:S57-S63 09 193 Chapter 10 Patient-reported factors partly predicted referral to spinal surgery in a consecutive cohort of 4,987 chronic low back pain patients van Dongen JM van Hooff ML Spruit M de Kleuver M Ostelo RWJG Under review 194 Abstract Purpose: To determine which patient-reported factors are predictive of spinal surgery referral among chronic low back pain (CLBP) patients. Methods: 4,987 CLBP patients were included. Possible predictive factors were explored using a screening questionnaire. Referral data were derived from hospital records. A prediction model was obtained by backwards selection. The model’s performance and validation were assessed. Results: Female gender, previous back surgery, high intensity leg pain, somatization, and positive treatment expectations increased the odds of being referred to spinal surgery, while being obese, having comorbidities, pain in the thoracic spine, reduced walking distance, and consultation location decreased the odds. The model’s fit was good, its discriminative ability was poor, and its explained variance was low. A post-hoc analysis indicated that consultation location was significantly associated with spinal surgery referral, even after correcting for case-mix variables. Conclusion: Some patient-reported factors could be identified that are predictive of spinal surgery referral. Although the identified factors are known as common predictive factors of surgery outcome, they could only partly predict spinal surgery referral. 215 Chapter 11 Prognostic patient-reported profiles for referral to secondary or tertiary spine specialists The Nijmegen Decision Tool for Chronic Low Back Pain van Hooff ML van Dongen JM Coupé VMH Spruit M Ostelo RWJG de Kleuver M Submitted for publication 216 Abstract Objective: To develop and internally validate pre-treatment prognostic models for patient triage to the right secondary care specialist, by identifying patient-reported profiles for chronic low back pain (CLBP) that either predict ‘response’ or ‘non-response’ to elective lumbar spine surgery, or to a multidisciplinary bio-psychosocial pain management programme. Design: Observational study, using baseline and one-year follow-up data from a large institutional spine outcome registry. Data on forty-seven evidence-based potential indicators acquired before consultation, predicting ‘response’ or ‘non-response’ to treatment, were used. Setting: Secondary referral spine centre. Participants: Two treatment cohorts were selected from a source population of 3,410 referred CLBP-patients: a spine surgery cohort (n=217 [6.4%]) and a pain management programme cohort (n=171 [5.0%]). Main inclusion criteria were: age ≥18, CLBP (≥6 months), not responding to primary care treatment. Main outcome measures: The primary outcomes were ‘response’ and ‘non-response’ to treatment in terms of functional ability. ‘Response’ (i.e. successful treatment outcome) was defined as Oswestry Disability Index (ODI)≤22. ‘Non-response’ (i.e. failure after treatment) was defined as ODI≥41. Results: The different models’ explained variances (R2) were low for the pain management models (23%-26%) and were modest for the surgical models (R2 30-39%). Although all the models’ overall performances were acceptable (c-index 0.72-0.83), the surgical non-response model performed best (R2 39%; c-index 0.83). Conclusions: This study was the first to identify different patient profiles based on pretreatment patient-reported characteristics that predict response to treatment for CLBP. In general the performances of all models were acceptable, but the ‘non-response’ model to elective lumbar spine surgery performed remarkably well, suggesting that this profile might contribute to avoiding unnecessary future surgeries. After including more patients and after external validation in other secondary spine practices, these patient profiles could potentially enhance timely patient triage to the right secondary care specialist, and ultimately contributing to improved outcomes and a more efficient use of healthcare resources. Trial registration: The Dutch Trial Register (NTR5946) 247 Chapter 12 Summary & General discussion 248 Summary and General discussion 249 The ultimate aim of the research presented in this thesis is to contribute to the body of knowledge on outcomes of interventions for chronic low back pain (CLBP) and to identify outcome-based subgroups of patients having different profiles. Based on work included in this thesis, an article was published in a provincial newspaper in 2014 highlighting the development of a tool to triage the right patient to the right medical specialist, based on subgroups with different patient profiles, i.e. the Nijmegen Decision Tool for CLBP. The headline stated ‘A questionnaire helps in low back pain’ [1]. Whilst we think we have taken an important step forward in terms of the knowledge gained and identifying pre-treatment profiles related to outcomes of interventions, a questionnaire as such is only a small piece of the puzzle towards solving the tremendous worldwide burden of low back pain. Despite decades of research, honed expertise, and improved quality of clinical trials, the evidence regarding the effectiveness of the treatments offered to patients with CLBP is still inconsistent [2-8], rarely shows more than a small to moderate overall benefit [2,7,9-12], and demonstrates a lack of long-term efficacy in changing the prognostic paths [10]. Most patients recover, but in around 20% the complaints persist for more than three months, resulting in disabling CLBP. These patients bear the greatest proportion of the disease burden. One reason for the overall disappointing outcomes is that the CLBP population is heterogeneous because the condition lacks diagnostic clarity; as a consequence, there exists a plethora of invasive and non-invasive interventions in secondary or tertiary healthcare for the same symptom. To reduce the global societal burden of CLBP it is crucial to improve treatment outcomes and to reduce the related costs, to improve the value of delivered healthcare. To achieve this it is essential to know which outcomes are relevant to both patients and medical specialists and to know which patients will benefit from spine surgery or from non-surgical treatments. In the General introduction of this thesis the aims are described using three separate, though related themes. In the first theme a non-surgical combined physical and psychological (CPP) programme was evaluated and insight is given as to who could benefit from this programme. In the second theme the focus is on the methodology used for outcomes assessment in the evaluation of clinical practice and research of degenerative lumbar spine disorders. In the third theme the development of a clinical decision tool for patient triage to a surgical or a non-surgical medical specialist is described. In this Summary & General discussion the overall research questions are answered by summarising and discussing the main findings per theme. Considerations are described that warrant further exploration in relationship to the methodology used, and some implications and recommendations for clinical practice and for future research are presented. The discussion ends with concluding remarks. 12 250 Summary and General discussion Theme A: Introduction of a combined physical and psychological programme Research questions 1. Does the novel CPP programme for CLBP improve patient outcomes and reduce healthcare consumption? 2.Is it possible to identify a subgroup of patients that benefits most from the novel CPP programme so that selection criteria can be optimised? Summary of main findings In the one- and two-year follow-up cohort studies presented in Chapter 2 and 3, selected and motivated patients with longstanding CLBP participated in an intensive multidisciplinary CPP programme. Participating patients were moderately to severely disabled, which is comparable to patients being treated in secondary spine care. They learned to manage their CLBP, and their daily functioning and quality of life improved meaningfully. The magnitude of the improvement is comparable to that achieved with spinal surgery (Standardised Morbidity Ratio [SMR] 98%) and better than that achieved with less intensive rehabilitation programmes (SMR 136%) (Chapter 2). At the two-year follow-up of this cohort, the significant and clinically relevant improvements in functional ability, pain and health-related quality of life achieved at the one-year follow-up assessment were maintained. Above all, most of the participants were employed and the results indicate that the use of both pain medication and healthcare decreased substantially (Chapter 3). In Chapter 4, pre-treatment indicators of a successful treatment outcome were identified. Successful treatment outcome was defined as a one-year follow-up functional disability (disability) score falling to values seen in healthy populations (Oswestry Disability Index, ODI ≤22). Patients who are employed at pre-treatment [OR 3.61 (95 % CI 1.80–7.26) and who are mild to moderately disabled at the start of a CPP programme [OR 0.94 (95% CI 0.92–0.97)] are most likely to benefit from this programme (R2=22%; 67% correctly classified). No interaction effects between pre-treatment characteristics were found and, to our surprise, no predictive value was found for psychological distress. 12 As continuous outcomes monitoring is part of the CPP programme, this gave us the opportunity to further substantiate the ‘pilot’ results presented in Chapter 2 with one-year follow-up results of a large cohort (n=848). The results of this recent study showed that these patients had similarly good results: patients improved during the programme, showed further improvement at the one-year follow-up, and half of the patients (51%, n=433) improved such that their functional status was comparable to that of the healthy population [13]. Discussion In a recent systematic review, moderate-quality evidence for moderate effects of multidisciplinary bio-psychosocial treatments was found compared to usual care [12]. These treatments are recommended for CLBP patients [14-16], but not often implemented. In this thesis the clinical relevance of the CPP programme has been demonstrated: patients improved meaningfully, healthcare consumption was reduced, and a relevant treatment effect was found. We found large effect sizes for functional ability at the one-year follow-up (Chapter 2) and the two-year follow-up (Chapter 3), meaning that the programme shows relevant effects. The two-year follow-up findings presented in Chapter 3 were further substantiated Summary and General discussion 251 by the results of a recently performed long-term follow-up post-marketing surveillance study involving 277 ex-participants (mean follow-up of 6.5 years [range: 5.5–7.5]; response 85%; no baseline differences between responders and non-responders to the survey); positive results were maintained after 6.5 years on average, and 80% of the participants were satisfied with the treatment results [17]. We studied the intervention as an integral programme. The programme uses a wide range of techniques based on cognitive behavioural principles. As yet, the working mechanisms are unknown, so it is unclear which techniques or parts of the intervention are the effective elements. In Chapter 4 we speculate that several aspects contribute to the success of this programme, such as the programme’s structure (a. the intensity or dose of the programme), the programme entrance criteria (b. motivation to change behaviour), and the content of the programme (c. improvement of dysfunctional cognitive behavioural factors). a)Intensity of the programme. Although conflicting evidence exists concerning stability over time [18,19], it is assumed that dysfunctional behavioural cognitions in patients with persistent pain of long duration are resistant to change [20]. In a systematic review, Guzman et al. recommend 100 hours or more of intensive multidisciplinary rehabilitation including cognitive behavioural interventions to improve functionality [21]. The CPP programme studied in this thesis follows this recommendation in a ‘pressure-cooker’ structure. This ‘pressure-cooker’ structure might moderate the dysfunctional behavioural cognitions. Patients with longstanding CLBP (mean 12 years, SD 11) benefit from this programme (Chapter 2-3), but the influence of duration and intensity of the programme as moderating factors remains elusive and needs to be further explored. b) Motivation of participants to change behaviour. Although motivation is a selection criterion for the programme, we neither assessed this factor in a clearly valid and reproducible way at pre-treatment nor assessed it systematically over time. As ‘motivation’ is viewed as a state that is amenable to change rather than a trait that is constant [22], it is possible that those patients who were unsuccessful after having followed this programme were actually not ready or motivated to change their pain-related behaviour. Motivation or readiness to change pain-related behaviour is important for treatment compliance; it plays an important role in accomplishing and achieving treatment goals and it positively influences quality of life and may predict healthcare costs [23,24], completion of a treatment programme [25], and treatment outcome [23,26,27]. The pain Readiness to Change (RtC) model is used for conceptualising the process of adopting a self-management approach to chronic pain [22]. According to the RtC model, individuals vary in their degree of readiness to adopt a self-management approach. In Chapter 2 it is shown that behavioural change is possible and that participants adopt self-management strategies to cope with back pain complaints. The Multidimensional Pain Readiness to Change Questionnaire (MPRCQ) [28,29] is designed to measure RtC related to specific components of self-management targeted in multidisciplinary treatment programmes [30], completion of treatment, and to predict treatment outcomes [29]. However, a recent study shows that, in patients with CLBP at pre-treatment, coping behaviours instead of readiness to engage those behaviours are associated with pain-related functioning [31]. Further research is needed to assess this indicator in a clearly valid and reproducible way and to evaluate its contribution to the outcome over time. 12 252 Summary and General discussion c)Improvement of dysfunctional cognitive behavioural factors. It has been suggested that improvement of such factors as catastrophising cognitions (i.e. exaggeration of the threat value of pain sensations) and fear of movement behaviour might contribute to the development of CLBP [32], to persistence of CLBP [33,34], and jeopardize successful treatment outcomes [12,35-38]. This phenomenon is described in the fear avoidance model (FAM) [32]. The model postulates a causal relationship between pain catastrophising (a sign of serious injury or pathology [39]), fear of movement, disability, and experienced pain severity [32,39]. Some studies have concluded that the impact of these dysfunctional cognitive behavioural factors on outcome is diminished [40,41] or is even absent [42], which is consistent with the results presented in Chapter 4. These findings corroborate the suggestion that the sequence and relationships between pain catastrophising, fear of movement, and disability postulated in the FAM maybe different for the development of CLBP than for the recovery of disability as a result of an intervention [43]. The CPP programme has a beneficial impact on cognitive behavioural variables [44], but a closer exploration of these cognitive behavioural factors and their impact on functional ability is needed. 12 Methodological considerations The studies presented in Chapter 2 and 3 followed an observational study design. To enhance the internal validity of the studies, several precautions in the study methodology were taken to minimise the potential influence of confounding on the outcomes. In evidence-based medicine the randomised controlled trial (RCT) is regarded as the gold standard to secure the internal validity of the study, because only the RCT is thought to resemble a true, pure experiment from which causal inference can be concluded. However, in the last decade the value of the RCT has been questioned for decisions about the use of interventions due to drawbacks of the design and available evidence from RCTs [45]. In certain situations, as in an RCT, ‘experimentation’ may be unnecessary, inappropriate, impossible, or inadequate [46]. Some of these are relevant in both surgical and conservative treatments. For example, an RCT may be inappropriate because of the random allocation and blinding procedures used. When the clinician and patient agreed upon the treatment, the clinician or patient (or both) have their preferences. This arises when the effectiveness of the intervention depends on patients’ active participation in conservative programmes, which, in turn, depends on patients’ beliefs and preferences. In (spine) surgery the results and effects also depend on surgical skills as well as surgeons’ beliefs and preferences. As a consequence, the lack of any subsequent difference in outcome between comparison groups may underestimate the benefits of the intervention. To overcome these problems, alternatives have been suggested for which the key is to use all available study designs, depending on the research question, and to perform every study with scientific rigour [47]. One of the alternatives to the RCT is routine outcome monitoring with an outcome registry, set up with an observational study design (Chapter 5). Recent spine-related studies comparing RCTs with observational study designs show comparable results [48-52], which suggests that observational study designs are complementary to RCTs. The external validity of the study results presented in the Chapters 2-4 might be limited because we studied the prospective cohort with carefully selected patients in a secondary care setting. The conclusions of the studies performed in Chapter 2 to 4 are based on data gathered through routine outcome monitoring rather than an RCT. The outcome monitoring is performed through the web-based outcome registry of the CPP programme. The outcomes registry follows the guidelines of observational studies [53] and the recommendations listed in Summary and General discussion 253 Chapter 5. To further substantiate the effects found in Chapter 2, an historical controlled trial was performed to compare the magnitude of treatment effects to those of other published studies for similar populations. For this comparison an SMR was used, which is a rate ratio to compare estimates of relative treatment improvement. We were limited to the external references used to calculate these SMRs, but we found comparable effects on functional outcome between the CPP programme and surgical interventions, and beneficial effects of the CPP programme compared to less intensive treatment programmes (Chapter 2). The main characteristics of the study populations were comparable, but unmeasured discrepancies are still likely. Implications for clinical practice and future research Because of the issues discussed above regarding generalisability, implementation of this CPP programme throughout the Netherlands could be challenging and the results of implementation will need to be confirmed. Therefore, with implementation in other healthcare settings, on-site continuous outcome monitoring is needed to demonstrate continuous quality of care and to benchmark results. Cost-effectiveness studies are needed to examine the impact and the value (i.e. outcomes relative to the costs [54]) of the delivered care. For a subgroup of participants in the CPP programme, i.e. those with previous lumbar spine surgery, the efficacy and impact of this programme compared to spine surgery is not yet clear. In the studies presented in Chapter 2-4, almost a third of the participants had previous back surgery (i.e. failed back surgery syndrome [FBSS]). To study the cost-effectiveness in this selected subgroup, a pragmatic RCT design is proposed with outcome monitoring over time. Before random assignment, two recommendations are offered: a waitlist condition to wash out previous treatment effects, and standardised management of expectations, because expectation of the outcome is predictive of a successful surgical outcome (Chapter 10). In Chapter 4, other moderating process factors were suggested which potentially predict a successful outcome, such as a clear treatment rationale, a highly structured programme, providing a pressure-cooker model programme, the intensity or dose of treatment, and a skilful staff (e.g. the impact of the spine surgeon in the educational part of the programme). These aspects should be further explored as potential working mechanisms of the programme. Regarding the inclusion criteria, motivation or readiness to change behaviour and outcome expectations might be relevant characteristics that need to be further studied. 12 254 Summary and General discussion Theme B: Outcomes assessment Research questions 1. What is the current value and methodology of spine outcome registries in clinical practice? 2.Which patient-related outcome measures should be used for outcomes assessment for degenerative lumbar spine disorders? 3. Which criterion can be used to define a successful outcome of interventions for patients with degenerative lumbar spine disorders? 12 Summary of main findings In Chapter 5 a systematic review is presented, in which 25 spine registries from around the world were identified. No conclusions can be drawn on the value or the impact of these registries on the quality of spine care, regardless of whether the intervention was non-surgical and/or surgical. The 25 registries were heterogeneous. To improve the quality of evidence published with registry data, we presented 14 recommendations. Two recognised shortcomings are that different outcome measures are used and different core or standard outcome sets for CLBP exist. However, going forward, consensus is needed in order to standardise outcomes to be able to compare and benchmark outcomes for degenerative lumbar spine disorders. In Chapter 6 a standard set of outcomes and influencing (risk) factors for use in clinical practice and research is described. The set was compiled through a literature review and a worldwide formal (modified Delphi) consensus procedure. One of the standard outcome instruments agreed upon to assess functional ability is the Oswestry Disability Index version 2.1a (ODI) as a patient-reported outcome measure (PROM). Because in the Netherlands several informal translations exist, we translated this version of the ODI into the Dutch language according to established guidelines and then evaluated the main methodological quality properties. In Chapter 7 we describe this process and report that the Dutch version of the ODI proved to be a valid and useful tool, with good measurement properties for the assessment of functional ability amongst patients with CLBP. To indicate treatment success after spine surgery, a Patient Acceptable Symptom State (PASS), equivalent to an absolute score on the ODI version 2.1a, was estimated in Chapter 8. Using follow-up data from patients with degenerative lumbar spine registered in the Eurospine Spine Tango Spine Surgery Registry, we estimated the PASS to be 22, irrespective of the time of follow-up. We recommend using this PASS as a threshold (ODI ≤22) to define treatment success alongside the commonly used change-score values. Discussion In order to improve consistency between registries, several recommendations were made (Chapter 5). First, outcome registries should be methodologically well constructed, which requires the use of observational study methods and the identification of best practices in existing registries so that a standardised approach to registering and analysis can be achieved. This effort will depend on international collaboration and benchmarking, and will contribute to future value-based spine care. Second, to improve the quality of spine care (i.e. outcomes of interventions; see General Introduction, 3. Outcomes of interventions for CLBP), consistent improvement strategies are needed (Chapter 5). For example, providing frequent continuous feedback (audit cycles) of outcomes captured in registries raises awareness and is recommended to enhance improvement of quality of care [55-59]. However, only two registry representatives reported Summary and General discussion 255 doing monthly (or more frequent) audits (i.e. Texas Back Institute and the Dutch Spine Surgery Registry; Chapter 5). However, these registries are new and started only recently, so no results are currently available and therefore no firm conclusions can be drawn. In the past, data to compare the performance of different healthcare providers were scarce. With Sweden as a pioneer and the United States following with the National Surgical Quality Improvement Program (NSQIP) [60], which was set up to continuously monitor and enhance the quality of surgical care, clinical registries (audits) have been initiated at both regional and national levels. This has led to a demonstrable improvement in clinical outcomes and smaller variation between providers [9,58,61]. However, so far, we could not show this impact in spine care (Chapter 5). The fact that registries can have an important effect on outcomes of interventions (i.e. quality of healthcare) was reported in a study of 13 outcome registries in five countries in other medical fields (e.g. hip arthroplasty, acute myocardial infarction and cataract surgery). That study demonstrated that registries have great potential to improve health outcomes and lower healthcare costs [58]. Studies describing the relationship between improvement of outcomes and reduction of hospital costs by quality improvement programmes are scarce [62], but a recent Dutch nationwide study presented evidence for simultaneous quality improvement and cost reduction in colorectal surgery. The authors concluded that participation in a nationwide quality improvement initiative with continuous quality measurement and benchmarked feedback reveals opportunities for targeted improvements [63]. A third recommendation presented in Chapter 5 is to use a standard set of patient-related outcome measures with good measurement properties applied in a systematic approach, such as presented in Chapter 6, to make future comparisons and benchmarking possible. Currently, broad international comparisons are limited because there exists large variation in the definitions of specific diseases, in included patient-related outcomes (PROMs and clinical outcomes), and in associated influencing (risk) factors. In the literature, several recommendations have previously been published for standardised outcome measurement in low back pain research [64-67], but not specifically for use in the full cycle of care in everyday clinical practice. An outcome set for use in everyday clinical practice and for continuous improvement of the quality of spine care requires availability and validity in many languages, capacity for case-mix adjustment to ensure that comparisons are made fairly, and should focus on the outcomes that matter most to patients (Chapter 6). The proposed outcome set in Chapter 6, which was achieved with formal international consensus, seems to fulfil these requirements. Further research is needed to validate this set, including linguistic and crosscultural translation and adaptation. In order to improve outcomes of treatment and with it the quality of spine care, research should focus on alignment of existing standard or core outcome sets with the standard set presented in Chapter 6, which is also relevant for patients and patient evaluation, and with the International Classification of Functioning, Disability, and Health (ICF) [68]. Methodological considerations When using outcome measures, clear criteria are required for the definition of treatment success. This is a methodological challenge, as the interpretation of pre- and post-treatment scores of PROMs has been a topic of research for more than two decades [69-71] and a methodological concern remains on how best to estimate and interpret outcomes and effects of interventions. Two different concepts to define treatment success are currently commonly used and methodologically discussed: a) relative change values (i.e. relevant 12 256 Summary and General discussion change; improvement or deterioration) and b) achievement of an acceptable symptom state by reaching absolute values. a)Change measures are widely recommended and commonly used in spine research. These relative measures are often referred to as a percentage baseline difference or a minimal important change (MIC) value (i.e. minimal clinical important difference [MCID] or change [MCIC]). A MIC value depicts a change, which is considered to be minimally important by patients, clinicians, or relevant others [72]. Several drawbacks concerning the use of change measures are acknowledged. For example, it is difficult to measure what is a clinically relevant change to patients [73] because the definition of what is clinically important or relevant or meaningful to patients is subjective. So far, there is no agreement on what constitutes operational definitions of ‘important’ and ‘meaningful’ as applied to clinical changes [74]. Another drawback is that the achievement of change scores is highly dependent on baseline values [75-77]. Furthermore, to determine the MIC value, global perceived effect (GPE) or recovery (GPR) scales are used as the anchor (i.e. external reference). The GPE scale asks the patient to rate, on a numerical Likert scale, how much their condition has improved or deteriorated since some predefined time point. Some validity concerns are acknowledged and it might be unsuitable for use as an external reference because patients can have difficulty recalling their previous status, and their estimates of transition are biased by their current health status [78]. Finally, a change value does not indicate whether the patient is satisfied with the current state. Therefore, research into the concept of reaching absolute values is needed. An example of such an absolute measure is the achievement of an absolute score equivalent to a PASS. 12 b)The PASS as an absolute value or threshold might be a more stringent and adequate measure to indicate treatment success. A PASS might be more important to patients than change values, as it probably reflects the ultimate goal of a treatment from the patient’s perspective: ‘It’s good to feel better but it’s better to feel good.’ [79,80]. It is a measure related to the current state of the patient and it overcomes the baseline dependency as encountered for change measures. The rationale for us to use a strict absolute threshold was that patients with CLBP presenting in secondary or tertiary spine care are severely disabled. When patients with such a high ODI-value at pre-treatment assessment improve and reach a recommended change value of 10 points [81] or 30% change [81,82], they could be classified as ‘successful’ whilst in fact they are still (severely) disabled. In treatments where recovery from disability is a goal, a more stringent and absolute cut-off value, comparable to ODI values seen in ‘normal’ healthy populations, should be a measure of treatment success. The score associated with achieving PASS could be considered as a cut-off point for determining whether patients are ‘responders’ after spine surgery and this threshold could also be considered as a clinical treatment target. In this respect, treatment success would be defined as the value beyond which patients can consider themselves well [83,84] or good [80], an approach that is frequently used in the field of rheumatology [85]. In Chapter 8, we determined the PASS for the ODI to be 22 (out of 100) and used an anchor-based approach. As the external reference (anchor) the symptom-specific well-being (SSWB) Likert scale of the Core Outcome Measures Index (COMI) was used: ‘If you had to spend the rest of your life with the symptoms you have now, how would you feel about it?’ [86,87] (i.e. current status, instead of asking patients). With this approach, potential recall bias is avoided as patients are asked about their current status instead of how much their condition has changed since some predefined time point. Summary and General discussion 257 Implications for clinical practice and further research Spine outcome registries can be used for continuous outcome monitoring. These registries might be useful in post-marketing surveillance for both innovative surgical and non-surgical treatments, after clinical introduction to the market, for monitoring the quality of care delivered to improve treatment outcomes, for benchmarking, and for research purposes (e.g. comparative effectiveness research). Moreover, comparing results of a strict multi-centre RCT with ‘real-life’ outcome data from routine practice can be possible if the RCT is performed within the framework of the registry, using the same questionnaires. This approach could contribute to a better understanding of the effect, both efficacy and effectiveness, of (new) interventions, and the external validity of the RCT can then be assessed. A further recommendation proposed in Chapter 5, to improve the methodology of outcome registries, is based on the finding that various analytical approaches have been used to explain any possible differences in outcomes between spine practices. To prevent selection bias [88] and to explain real differences in outcomes between institutions, multivariate approaches with adjustment for covariates (i.e. corrections for differences in characteristics of patients treated in hospitals; ‘case-mix adjustments’) and correction for chance variation (reliability adjustments) are needed [89-92]. Owing to the fact that neither the aetiology (and thus the underlying mechanisms) nor the set of case-mix indicators of CLBP is known, any comparison of outcomes between institutions is a challenge. Advanced statistical techniques such as multilevel random-effects regression models are recommended, as these models apportion some of the variation between hospitals as being due to just chance [93,94], and need to be further explored for future use in multicentre studies comparing treatment outcomes in CLBP patients. In the treatment of CLBP, an important outcome domain is functional ability. In the absence of objective measures and, more importantly, to incorporate patient experience, PROMs are used. To improve objectivity, measures of body function, such as mobility and muscle strength, have been used, although the correlation between these measures and the level of activity in daily life is very weak [95,96]. Promising preliminary results were obtained in studies involving continuous measurement of patients wearing activity sensors whilst performing activities of daily living [97-99]. Future research could quantify daily functioning by continuous activity monitoring and explore its use in evaluating treatments for CLBP as a complement to the commonly used PROMs. To be able to define a successful outcome of an intervention from the patient’s perspective, further research is warranted to determine which method is beneficial. Using PASS to define treatment success, as equivalent to a ‘normal’ healthy symptom state seems promising for evaluating spine surgery for lumbar spine disorders. We think the ‘ODI ≤22’ threshold might be valuable for use in conservative treatments as well, but this needs to be further explored. The concept of PASS gets closest to the patient’s perspective, approaching ‘It’s good to feel better but it’s better to feel good.’ [79,80]. The PASS threshold is still a determination made by the clinician or researcher rather than what the patient considers to be satisfactory, whereas in evaluating treatment outcomes it is important to recognise the patient’s view as well. Anchor-based methods do not account for the risks and costs of treatment, and rarely define effects of intervention in terms of the difference in outcome with and without intervention. The Smallest Worthwhile Effect (SWE) is a concept that uses a benefit−harm trade-off 12 258 Summary and General discussion method to determine the smallest effect that justifies the costs, risks and inconveniences of an intervention [100-102]. The SWE is defined as an important change whilst considering the burden of the intervention and focusses on whether it is worthwhile for the patient [100]. This novel method seems promising and needs to be further explored as it allows patients to weigh the benefits of treatment against the risks, costs, and inconveniences of treatment; and potentially provides estimates that are based on an intervention versus control comparison. Another research area is the evolution of outcomes assessment with PROMs. A myriad of PROMs exist that measure functional ability in patients with CLBP [103]. Even when the standard or core outcome sets are aligned, multiple valid PROMs covering the different outcome domains are still needed for evaluating spine treatments. It is essential to explore whether it is valuable to combine PROMs into one outcome measure. A promising new approach in this area is the Patient Reported Outcomes Measurement Information System (PROMIS NIH). PROMIS is a system of reliable and precise measures of patient-reported health status for physical, mental and social well-being [104]. PROMIS scales, once validated for a specific patient group, may be calibrated and built into computer adaptive tests (CATs). A CAT integrates the advantage of measurement theory and the power of computer technology to administer a PROM that selects questions on the basis of a patient's response to previously administered questions or other prior information [104]. Highly informative questions are selected and scores are estimated that represent a person’s level in a domain (e.g. physical functioning, quality of life, and pain) with the minimal number of questions without a loss in measurement precision [104,105]. It will be interesting to explore whether these tailored and individualised CATs are feasible and valid for use in future studies evaluating treatment outcomes of CLBP. 12 Summary and General discussion 259 Theme C: Prediction of outcomes Research question 1. Is it possible to develop a triage tool for CLBP, which enables valid and reliable identification of patient profiles that supports triage of the patients to a spine surgeon or to non-surgical specialists? Summary of main findings Chapter 9 describes the development of a decision tool for secondary or tertiary spine care specialists to decide which patients with CLBP should be seen by a spine surgeon or by non-surgical medical specialists. Based on a literature review, consensus was reached to include 47 indicators. A first version of the decision tool was developed, consisting of a web-based screening questionnaire, systematic outcomes assessment, and a provisional decision algorithm. In Chapter 10, ten patient-reported factors were found to be predictive of referral to spinal surgery amongst CLBP patients (i.e. female gender, previous back surgery, high leg pain intensity, somatisation, positive treatment expectations, being obese, having comorbidities, pain in the thoracic spine, reduced walking distance, and consultation location). The explained variance of the model was low (6%), which means that the model could only partly predict spinal surgery referral amongst CLBP patients. A longitudinal study is presented in Chapter 11, in which different patient profiles are identified for patient triage, based on pre-treatment patient-reported characteristics and one-year follow-up outcomes. Different pre-consultation profiles were determined that could predict ‘response’ and ‘nonresponse’ to spine surgery and to a CPP programme at follow-up. In general, the performances of all models were acceptable; in particular, the ‘non-response’ model to elective lumbar spine surgery performed remarkably well (R2 39%; c-index 0.83). Discussion Currently, a valid pre-treatment classification is lacking that accurately predicts a consistent beneficial outcome after lumbar spine surgery and non-surgical treatments [106,107]. To our knowledge, we present the first internally valid classification: a ‘proof-of-principle’ version of the Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP), developed for secondary care (Chapter 9-11). The current proof-of-principle tool consists of a comprehensive screening questionnaire, systematic outcome monitoring (Chapter 9), and prognostic patient-reported profiles related to treatment outcome (Chapter 11). The ultimate purpose of our research programme is to reliably identify, in two phases, patients who would most likely benefit from certain interventions. The first phase is aimed at identifying prognostic patient-reported profiles to enhance timely patient triage to a spine surgeon or a non-surgical specialist for consultation, as studied in Chapter 11. In the second phase, based on further diagnostics (e.g. imaging), the profiles will be refined to include both the indicators from the first phase and those from the diagnostic phase, to reliably refer the patient to the most appropriate treatment. This second phase is planned and part of future research, but is beyond the scope of this thesis. International guidelines [14,15] recommend using the clinical flag approach [108] for clinical decision-making in CLBP. A diagnostic triage based on ‘red flag’ signs is recommended [14,15,109] as they are thought to be associated with a high risk of serious underlying disorders, such as infection, inflammatory disease, cancer or fracture [108,110] or nerve root disease [111]. 12 260 Summary and General discussion The presence of a red flag alerts clinicians to the need for further examination and specific management [110,112,113]. Thus, we chose the red flag approach as a starting point for the preliminary version of the decision algorithm, based on multidisciplinary consensus (Chapter 9). In Chapter 11 we report that most of the patients with CLBP (92%) show at least one positive red flag but do not have a serious underlying condition. Taking the guideline recommendations literally could cause harm (e.g. unnecessary diagnostics, unnecessary exposure to radiation, unnecessary treatments, including surgery [114]), and these recommendations should be reconsidered. To evaluate indicators predicting treatment outcome, in Chapter 11 we determined different pre-treatment patient-reported profiles. The combination of indicators determined in a profile is used to predict the individual probability of treatment outcome. Owing to the high number of potential predictive indicators and the limited number of events (events per variable [EPV]), we were not able to include interaction terms (e.g. combination of red flags with yellow flags [e.g. somatisation or distress]). More detailed analyses including these interaction terms are planned when more new patients are enrolled. Moreover, we expect that combinations of red flags and clinical features, determined in the diagnostic phase, might be more informative to assist in clinical decision-making [112,113,115]. This needs to be further explored in the second phase of the NDT-CLBP when patient profiles for decision to treatment, rather than triage alone, are to be analysed and built. 12 Methodological considerations The patient-reported profiles determined in Chapter 10 and 11 were studied and reported in line with the recommendations of the ‘prognosis research strategy’ (PROGRESS) [116] and the ‘transparent of a multivariable prediction model for individual prognosis or diagnosis’ (TRIPOD) [117]. The patient-reported profiles are based on prognostic models. The establishment of such profiles in clinical practice requires three distinct phases [118]: 1) Development (i.e. identification of important predictive indicators from an observational study); 2) Validation (i.e. testing of the profile’s predictive performance in new patients to determine whether it remains reliable and stable); and 3) Impact analysis on daily practice (i.e. assessment of the usefulness of the profile in the clinical setting to identify whether the validated profile is likely to have meaningful, beneficial consequences). Such benefits may include more accurate clinical decision-making in terms of selection and prioritisation of patients requiring intervention, improved patient outcomes, and reduced costs of care [107,119-121]. Adams et al. added an important fourth phase: Implementation (i.e. widespread acceptance and adoption of the profile in clinical practice) [120]. In the studies of Chapter 10 and 11, the models were developed and internally validated to correct for over-fit and optimism of performance measures [119,121-123]. In Chapter 10 we identified ten prognostic patient-reported indicators for referral to lumbar spine surgery. These indicators are not externally validated yet, but they are known as common predictive indicators for surgical outcome, as previous studies have identified them, too (e.g. [124-126]). This (preliminary) result gave us the confidence and arguments to further explore and examine the patient-reported profiles longitudinally, based on treatment outcomes (Chapter 11). Due to the observational study design used in Chapter 11, confounding by indication might be present. Confounding by indication is where allocation to treatment is subject to a black box of an unrecognised or unmeasured process associated with those who are treated, which is guided by the experience and preferences of clinicians who use their expert judgment to decide whether to treat a patient [127]. Concealed randomisation has been suggested to interfere in Summary and General discussion 261 the relationship of prognosis and prescription [128]. Due to patient and clinician preferences, and as lumbar spine surgery and CPP programmes are regarded as mutually exclusive, randomisation is not feasible. We therefore described the source population and both the cohorts and performed subgroup analyses to discover patterns and the patient profiles. As mentioned above, owing to the relatively high amount of potential predictive variables and relatively few events, expressed in EPV, we were not able to introduce interaction terms. As such, we cannot rule out that patients might actually fit into both profiles, e.g. patients who responded well to spine surgery might have responded well in the CPP programme as well. However, one should bear in mind that the patient profiles are used for decision-making in patient triage to a surgical or non-surgical specialist instead of the actual treatment. Future studies are needed to validate the prognostic profiles using interaction terms for type of treatment, and to further substantiate the decision process. Implications for clinical practice and further research Before widespread implementation and use of the patient profiles for triage to a surgical or non-surgical specialist, as determined in Chapter 11, the underlying prognostic models need to be further validated in new patients and in other, similar secondary spine practices, and impact studies exploring the cost-effectiveness and the clinical usefulness are needed. Including more patients with follow-up outcomes would allow studying interaction effects between combinations of potential prognostic indicators and interaction effects between types of treatment, which could be used to further refine the prognostic profiles. In the study of Chapter 11, we analysed and described patient-reported profiles for elective lumbar spine surgery and multidisciplinary bio-psychosocial treatment (i.e. CPP programme). Further research is planned to explore patient profiles for other treatments, e.g. invasive pain management or ‘no treatment’ (i.e. counselling and physical therapy in primary care), to explore whether patients with features of axial spondylarthropathy could be determined by their profile for early referral to rheumatologists for further diagnostics and treatment, and to conduct a pilot study to determine whether the triage tool is feasible for use in primary care. To develop the patient profiles (Chapter 11), we examined associations between potential prognostic indicators and one outcome domain: functional ability. Strict evidence-based absolute thresholds were used to define the outcome. As yet, no consensus exists on how to define the outcome and which measure, relative or absolute change, to use to define success or failure. To be able to define ‘response’ and ‘non-response’ after treatment for CLBP, clear evidence-based and expert consensus-based criteria, including the measures that operationalise these definitions, are needed for ‘responder analysis’ to make future comparisons in both research and clinical practice possible. To define these criteria a research approach comparable with the Osteoarthritis Research Society International (OARSI) set of responder criteria for osteoarthritis [129] could be used. The triage profiles of the NDT-CLBP belong to the first phase of the decision-making process for a certain treatment. The next, second, diagnostic phase contributes to the final decision between clinician and patient as to which treatment is most appropriate. Future research should examine whether (combinations of) biomedical and psychosocial indicators that were not included in Chapter 11 should be incorporated into the models, to extend and further refine the different patient profiles. For this, relevant data of the diagnostic phase (e.g. specific diagnosis [e.g. spondylolisthesis spinal stenosis], physical characteristics) should be added to 12 262 Summary and General discussion the currently developed (patient-reported) triage tool. Growing interest exists to determine clinical phenotypes. Phenotypes refer to observable traits of an individual organism [130], consisting of biomedical and psychosocial factors. Based on novel techniques, preliminary evidence suggests that radiographic characteristics (Modic changes), biomarkers, and genetic profiles, might possibly explain CLBP bio-medically [131]. In line with the recommendation of Samartzis et al. [131], a global consortium (i.e. AO Personalized Spine Care consortium) has been set up, consisting of a multidisciplinary group of spine and pain specialists and researchers, to further refine the understanding of CLBP and to further study both the decision tool for patient triage and for treatment to enhance stratified and personalised spine care. Towards a paradigm shift in CLBP, away from stepped care in secondary spine care? Studying different prognostic patient profiles based on the NDT-CLBP might contribute to further refine and support future treatment decisions in CLBP. CLBP is multifactorial in nature and the underlying mechanisms remain largely unknown. As yet aetiological studies have contributed little to diagnostics in CLBP and corresponding treatments that are ultimately expected to lead to successful treatment outcomes. Although future studies may reveal some aetiological factors, we speculate that a shift from classic causal reasoning towards reasoning in prognostic patient profiles may form an alternative paradigm. Patient profiles may contribute meaningfully to the optimisation of decision-making in the treatment of CLBP, even without full knowledge of the aetiology of CLBP. 12 If the developed patient profiles are externally validated, the impact is shown and if they are proven to be of value in decision making (i.e. they can predict which patient will benefit from which treatment, even without a well-defined cause), then the recommendations in the (inter-) national guidelines will need to be reconsidered. In the National Institute for Health and Care Excellence (NICE) guideline [15] as well as in a recently released draft version of the Dutch national guideline for instrumented lumbar spine surgery [132], a stepped care approach is suggested: before lumbar spine surgery is considered, a CPP programme is indicated (consensus; low quality evidence). Although further research is needed to substantiate and validate the patient profiles developed in Chapter 11, the different profiles suggest that different subgroups of patients could be identified in the heterogeneous CLBP population that would benefit from treatments. This would mean — instead of the recommendation made in the guidelines for a serial (stepped care) approach, yielding spine surgery as an ‘end-of-line’ treatment — that a more parallel approach might be feasible. For example, some patients may be identified who will benefit from surgery, without having undergone a full nonoperative cycle of care, and some patients should never undergo surgery, despite failing all non-operative treatments. Then, a remarkable paradigm shift in clinical reasoning of medical specialists in spine care would be required. Summary and General discussion 263 Concluding remarks The CLBP population is heterogeneous and the condition lacks diagnostic clarity. The failure to differentiate between underlying causes is one of the reasons why a plethora of invasive and non-invasive interventions exist for the same symptom. Despite decades of research and improved quality of clinical trials, the reality is that the treatments offered to patients have led to inconsistent results. Based on the research in this thesis, it seems that: 1 The introduced CPP programme is effective for a selected subgroup of patients with CLBP. 2Using strict selection criteria for patient entrance to interventions, clear outcome definitions and continuous outcome monitoring of treatments using an outcome registry can contribute to improving the quality of delivered spine care. 3For the definition of treatment success in secondary spine care, the absolute ODI-22 threshold is recommended for use alongside the commonly used change measures. 4Different subgroups within the heterogeneous CLBP population are evident, based on different prognostic patient-reported profiles and response or non-response after treatments in secondary care. To our knowledge the developed prognostic patient-reported profiles of the NDT-CLBP are the first that seem to recognise these more homogeneous subgroups. 5 The non-response prognostic patient-reported profile for elective lumbar spine surgery performed remarkably well. This suggests that patients with a high probability for persisting severe disability after surgical intervention could be identified even before actual consultation, so that unnecessary and unhelpful surgery could be avoided. 6 In the future, when these patient profiles have been shown to remain stable after validation in new patients and in other secondary spine practices, they may be used to enhance timely patient triage to the right surgical or non-surgical specialist and contribute to the decisionmaking between clinician and patient. Subgrouping CLBP based on treatment outcomes still needs to be a research priority, and further high-quality research is needed in the screening and diagnostics of CLBP. Exploring the validity and the impact of the patient profiles presented in this thesis is required before firm conclusions can be drawn regarding the NDT-CLBP. Including the diagnostics of the second phase of the NDT-CLBP in the profiles of the decision tool might contribute to improvement of treatment outcomes. These profiles could lead to a paradigm shift in clinical reasoning and decision-making and ultimately to a more efficient use of healthcare resources and reduction of the tremendous worldwide burden of low back pain. 12 264 Summary and General discussion References 1. De Gelderlander. Vragenlijst helpt bij lage rugpijn. De Gelderlander 2014 Sept 24 [Newspaper and Internet Website in Dutch; cited 2016 July 20] Available from: http://www.gelderlander.nl/regio/nijmegen-e-o/ vragenlijst-helpt-bij-behandeling-van-lage-rugpijn-1.4546317 2. Henschke N, Kuijpers T, Rubinstein SM, van Middelkoop M, Ostelo R, Verhagen A et al. Trends over time in the size and quality of randomised controlled trials of interventions for chronic low-back pain. Eur.Spine J. 2012;21:375-381 3. Fairbank J, Frost H, Wilson-MacDonald J, Yu LM, Barker K, Collins R. Randomised controlled trial to compare surgical stabilisation of the lumbar spine with an intensive rehabilitation programme for patients with chronic low back pain: the MRC spine stabilisation trial. BMJ 2005;330:1233 4. Mirza SK, Deyo RA. Systematic review of randomized trials comparing lumbar fusion surgery to nonoperative care for treatment of chronic back pain. Spine (Phila Pa 1976.) 2007;32:816-823 5. Brox JI, Nygaard OP, Holm I, Keller A, Ingebrigtsen T, Reikeras O. Four-year follow-up of surgical versus nonsurgical therapy for chronic low back pain. Ann.Rheum.Dis. 2010;69:1643-1648 6. van Middelkoop M, Rubinstein SM, Kuijpers T, Verhagen AP, Ostelo R, Koes BW et al. A systematic review on the effectiveness of physical and rehabilitation interventions for chronic non-specific low back pain. Eur. Spine J. 2011;20:19-39 7. Jacobs WC, Rubinstein SM, Koes B, van Tulder MW, Peul WC. Evidence for surgery in degenerative lumbar spine disorders. Best.Pract.Res.Clin.Rheumatol. 2013;27:673-684 8. Mannion AF, Brox JI, Fairbank JC. Comparison of spinal fusion and nonoperative treatment in patients with chronic low back pain: long-term follow-up of three randomized controlled trials. Spine J. 2013;13:1438-1448 9. Hall H, McIntosh G. Low back pain (chronic). Clin.Evid.(Online.) 2008 10. Foster NE. Barriers and progress in the treatment of low back pain. BMC.Med. 2011;9:108 11. Wand BM, O'Connell NE. Chronic non-specific low back pain - sub-groups or a single mechanism? BMC. Musculoskelet.Disord. 2008;9:11 12. Kamper SJ, Apeldoorn AT, Chiarotto A, Smeets RJ, Ostelo RW, Guzman J et al. Multidisciplinary biopsychosocial rehabilitation for chronic low back pain: Cochrane systematic review and meta-analysis. BMJ 2015;350:h444 13. Van Hooff ML, O'Dowd JK, Spruit M, De Kleuver M, Fairbank J, Van Limbeek J. Clinical outcomes of a 12 Combined Physical and Psychological programme in a large cohort of longstanding chronic low back pain. BJJ Orthopaedic Proceedings 2014 14. Airaksinen O, Brox JI, Cedraschi C, Hildebrandt J, Klaber-Moffett J, Kovacs F et al. Chapter 4. European guidelines for the management of chronic nonspecific low back pain. Eur.Spine J. 2006;15 Suppl 2:S192-S300 15. National Institute for Health and Clinical Excellence. Low back pain: early management of persistent non-specific low back pain [Report]. NICE Clinical Guideline 88. London, National Collaborating Centre for Primary Care; 2009 16. Chou R, Loeser JD, Owens DK, Rosenquist RW, Atlas SJ, Baisden J et al. Interventional therapies, surgery, and interdisciplinary rehabilitation for low back pain: an evidence-based clinical practice guideline from the American Pain Society. Spine (Phila Pa 1976.) 2009;34:1066-1077 17. Van Hooff ML, O'Dowd JK, Van Loon J, Spruit M. Long-term follow-up of a Combined Physical and Psychological programme for patients with longstanding chronic low back pain. BJJ Orthopaedic Proceedings 2016 18. Turner JA, Mancl L, Aaron LA. Pain-related catastrophizing: a daily process study. Pain 2004;110:103-111 19. Lame IE, Peters ML, Kessels AG, Van KM, Patijn J. Test--retest stability of the Pain Catastrophizing Scale and the Tampa Scale for Kinesiophobia in chronic pain over a longer period of time. J.Health Psychol. 2008;13:820-826 20. Badke MB, Boissonnault WG. Changes in disability following physical therapy intervention for patients with low back pain: dependence on symptom duration. Arch.Phys.Med.Rehabil. 2006;87:749-756 Summary and General discussion 265 21. Guzman J, Esmail R, Karjalainen K, Malmivaara A, Irvin E, Bombardier C. Multidisciplinary bio-psycho-social rehabilitation for chronic low back pain. Cochrane.Database.Syst.Rev. 2002:CD000963 22. Jensen MP, Nielson WR, Kerns RD. Toward the development of a motivational model of pain selfmanagement. J.Pain 2003;4:477-492 23. Grahn B, Ekdahl C, Borgquist L. Motivation as a predictor of changes in quality of life and working ability in multidisciplinary rehabilitation. A two-year follow-up of a prospective controlled study in patients with prolonged musculoskeletal disorders. Disabil.Rehabil. 2000;22:639-654 24. Grahn BE, Borgquist LA, Ekdahl CS. Rehabilitation benefits highly motivated patients: a six-year prospective cost-effectiveness study. Int.J.Technol.Assess.Health Care 2004;20:214-221 25. Glenn B, Burns JW. Pain self-management in the process and outcome of multidisciplinary treatment of chronic pain: evaluation of a stage of change model. J.Behav.Med. 2003;26:417-433 26. Heapy A, Otis J, Marcus KS, Frantsve LM, Janke EA, Shulman M et al. Intersession coping skill practice mediates the relationship between readiness for self-management treatment and goal accomplishment. Pain 2005;118:360-368 27. Gersh E, Arnold C, Gibson SJ. The relationship between the readiness for change and clinical outcomes in response to multidisciplinary pain management. Pain Med. 2011;12:165-172 28. Nielson WR, Jensen MP, Ehde DM, Kerns RD, Molton IR. Further development of the multidimensional pain readiness to change questionnaire: the MPRCQ2. J.Pain 2008;9:552-565 29. Nielson WR, Armstrong JM, Jensen MP, Kerns RD. Two brief versions of the multidimensional pain readiness to change questionnaire, version 2 (MPRCQ2). Clin.J.Pain 2009;25:48-57 30. Nielson WR, Jensen MP, Kerns RD. Initial development and validation of a multidimensional pain readiness to change questionnaire. J.Pain 2003;4:148-158 31. Finan PH, Burns JW, Jensen MP, Nielson WR, Kerns RD. Pain coping but not readiness to change is associated with pretreatment pain-related functioning. Clin.J.Pain 2012;28:687-692 32. Vlaeyen JWLinton SJ. Fear-avoidance and its consequences in chronic musculoskeletal pain: a state of the art. Pain 2000;85:317-332 33. Wertli MM, Eugster R, Held U, Steurer J, Kofmehl R, Weiser S. Catastrophizing-a prognostic factor for outcome in patients with low back pain: a systematic review. Spine J. 2014;14:2639-2657 34. Wertli MM, Rasmussen-Barr E, Weiser S, Bachmann LM, Brunner F. The role of fear avoidance beliefs as a prognostic factor for outcome in patients with nonspecific low back pain: a systematic review. Spine J. 2014;14:816-836 35. Smeets RJ, Vlaeyen JW, Kester AD, Knottnerus JA. Reduction of pain catastrophizing mediates the outcome of both physical and cognitive-behavioral treatment in chronic low back pain. J.Pain 2006;7:261-271 36. Wessels T, van Tulder M, Sigl T, Ewert T, Limm H, Stucki G. What predicts outcome in non-operative treatments of chronic low back pain? A systematic review. Eur.Spine J. 2006;15:1633-1644 37. Wertli MM, Burgstaller JM, Weiser S, Steurer J, Kofmehl R, Held U. Influence of catastrophizing on treatment outcome in patients with nonspecific low back pain: a systematic review. Spine (Phila Pa 1976.) 2014;39:263273 38. Wertli MM, Rasmussen-Barr E, Held U, Weiser S, Bachmann LM, Brunner F. Fear-avoidance beliefs-a moderator of treatment efficacy in patients with low back pain: a systematic review. Spine J. 2014;14:26582678 39. Crombez G, Eccleston C, Van Damme S, Vlaeyen JW, Karoly P. Fear-avoidance model of chronic pain: the next generation. Clin.J.Pain 2012;28:475-483 40. Schiphorst Preuper HR, Reneman MF, Boonstra AM, Dijkstra PU, Versteegen GJ, Geertzen JH et al. Relationship between psychological factors and performance-based and self-reported disability in chronic low back pain. Eur.Spine J. 2008;17:1448-1456 12 266 Summary and General discussion 41. Henschke N, Ostelo RW, van Tulder MW, Vlaeyen JW, Morley S, Assendelft WJ et al. Behavioural treatment for chronic low-back pain. Cochrane.Database.Syst.Rev. 2010:CD002014 42. van der Hulst M, Vollenbroek-Hutten MM, Ijzerman MJ. A systematic review of sociodemographic, physical, and psychological predictors of multidisciplinary rehabilitation-or, back school treatment outcome in patients with chronic low back pain. Spine (Phila Pa 1976.) 2005;30:813-825 43. Wideman TH, Adams H, Sullivan MJ. A prospective sequential analysis of the fear-avoidance model of pain. Pain 2009;145:45-51 44. Van Hooff ML, Van Lankveld W, O'Dowd JK, De Kleuver M, Van Limbeek J. F422 Patients with longstanding back pain improve on cognitive behavioral variables after a short, intensive pain magement program. Eur.J.of.Pain 2012;5(S1):158 45. Rawlins M. De testimonio: on the evidence for decisions about the use of therapeutic interventions. Lancet 2008;372:2152-2161 46. Black N. Why we need observational studies to evaluate the effectiveness of health care. BMJ 1996;312:12151218 47. Jacobs WC. Epistemological considerations. On the controversies of spine surgery research [PhD Thesis] 2012:175-179 48. Furlan AD, Tomlinson G, Jadad AA, Bombardier C. Methodological quality and homogeneity influenced agreement between randomized trials and nonrandomized studies of the same intervention for back pain. J.Clin.Epidemiol. 2008;61:209-231 49. Weinstein JN, Lurie JD, Tosteson TD, Zhao W, Blood EA, Tosteson AN et al. Surgical compared with nonoperative treatment for lumbar degenerative spondylolisthesis. four-year results in the Spine Patient Outcomes Research Trial (SPORT) randomized and observational cohorts. J.Bone Joint Surg.Am. 2009;91:1295-1304 50. Tosteson AN, Tosteson TD, Lurie JD, Abdu W, Herkowitz H, Andersson G et al. Comparative effectiveness evidence from the spine patient outcomes research trial: surgical versus nonoperative care for spinal stenosis, degenerative spondylolisthesis, and intervertebral disc herniation. Spine (Phila Pa 1976.) 2011;36:2061-2068 51. Staub LP, Ryser C, Roder C, Mannion AF, Jarvik JG, Aebi M et al. Total disc arthroplasty versus anterior cervical interbody fusion: use of the Spine Tango registry to supplement the evidence from randomized control trials. Spine J. 2016;16:136-145 12 52. Forsth P, Olafsson G, Carlsson T, Frost A, Borgstrom F, Fritzell P et al. A Randomized, Controlled Trial of Fusion Surgery for Lumbar Spinal Stenosis. N.Engl.J.Med. 2016;374:1413-1423 53. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 2007;370:1453-1457 54. Porter ME. What is value in health care? N.Engl.J.Med. 2010;363:2477-2481 55. Shojania KG, Ranji SR, McDonald KM, Grimshaw JM, Sundaram V, Rushakoff RJ et al. Effects of quality improvement strategies for type 2 diabetes on glycemic control: a meta-regression analysis. JAMA 2006;296:427-440 56. Larsson S, Lawyer P, Silverstein MB. From concept to reality. Putting value-based healthcare into practice in Sweden [Internet]. Boston Consultancy Group, 2010 [cited 2016 July 20]. Available from: https://www.bcg. com/documents/file64538.pdf 57. van Leersum NJ, Kolfschoten NE, Klinkenbijl JH, Tollenaar RA, Wouters MW. ['Clinical auditing', a novel tool for quality assessment in surgical oncology]. Ned.Tijdschr.Geneeskd. 2011;155:A4136 58. Larsson S, Lawyer P, Garellick G, Lindahl B, Lundstrom M. Use of 13 disease registries in 5 countries demonstrates the potential to use outcome data to improve health care's value. Health Aff.(Millwood.) 2012;31:220-227 Summary and General discussion 267 59. Tricco AC, Ivers NM, Grimshaw JM, Moher D, Turner L, Galipeau J et al. Effectiveness of quality improvement strategies on the management of diabetes: a systematic review and meta-analysis. Lancet 2012;379:22522261 60. NSQIP. The National Surgery Quality Improvement Program (NSQIP) [Internet]. American College of Surgeons, Chicago 2016 [cited 2016 July 20]; Available from: https://www.facs.org/quality-programs/acsnsqip 61. Khuri SF, Daley J, Henderson W, Hur K, Demakis J, Aust JB et al. The Department of Veterans Affairs' NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of surgical care. National VA Surgical Quality Improvement Program. Ann Surg. 1998;228:491-507 62. Govaert JA, van Bommel AC, van Dijk WA, van Leersum NJ, Tollenaar RA, Wouters MW. Reducing healthcare costs facilitated by surgical auditing: a systematic review. World J.Surg. 2015;39:1672-1680 63. Govaert JA, van Dijk WA, Fiocco M, Scheffer AC, Gietelink L, Wouters MW et al. Nationwide Outcomes Measurement in Colorectal Cancer Surgery: Improving Quality and Reducing Costs. J.Am.Coll.Surg. 2016;222:19-29 64. Deyo RA, Battie M, Beurskens AJ, Bombardier C, Croft P, Koes B et al. Outcome measures for low back pain research. A proposal for standardized use. Spine (Phila Pa 1976.) 1998;23:2003-2013 65. Cieza A, Stucki G, Weigl M, Disler P, Jackel W, van der Linden S et al. ICF Core Sets for low back pain. J.Rehabil. Med. 2004:69-74 66. Pincus T, Santos R, Breen A, Burton AK, Underwood M. A review and proposal for a core set of factors for prospective cohorts in low back pain: a consensus statement. Arthritis Rheum 2008;59:14-24 67. Chiarotto A, Deyo RA, Terwee CB, Boers M, Buchbinder R, Corbin TP et al. Core outcome domains for clinical trials in non-specific low back pain. Eur.Spine J. 2015;24:1127-1142 68. World Health Organization (WHO). International Classification of Functioning, Disability, and Health (ICF) [Internet]. WHO 2001 [updated 2016; cited 2016 July 20]; Available from: http://www.who.int/classifications/ icf/en/ 69. Jaeschke R, Singer J, Guyatt GH. Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin.Trials 1989;10:407-415 70. de Vet HC, Terwee CB, Ostelo RW, Beckerman H, Knol DL, Bouter LM. Minimal changes in health status questionnaires: distinction between minimally detectable change and minimally important change. Health Qual.Life Outcomes. 2006;4:54 71. Gatchel RJ, Lurie JD, Mayer TG. Minimal clinically important difference. Spine (Phila Pa 1976.) 2010;35:17391743 72. de Vet HC, Terwee CB, Ostelo RW, Beckerman H, Knol DL, Bouter LM. Minimal changes in health status questionnaires: distinction between minimally detectable change and minimally important change. Health Qual.Life Outcomes. 2006;4:54 73. Kirwan JR. Minimum clinically important difference: the crock of gold at the end of the rainbow? J.Rheumatol. 2001;28:439-444 74. Gatchel RJ, Lurie JD, Mayer TG. Minimal clinically important difference. Spine (Phila Pa 1976.) 2010;35:17391743 75. Crosby RD, Kolotkin RL, Williams GR. Defining clinically meaningful change in health-related quality of life. J.Clin.Epidemiol. 2003;56:395-407 76. van der Roer N, Ostelo RW, Bekkering GE, van Tulder MW, de Vet HC. Minimal clinically important change for pain intensity, functional status, and general health status in patients with nonspecific low back pain. Spine (Phila Pa 1976.) 2006;31:578-582 12 268 Summary and General discussion 77. de Vet HC, Foumani M, Scholten MA, Jacobs WC, Stiggelbout AM, Knol DL et al. Minimally important change values of a measurement instrument depend more on baseline values than on the type of intervention. J.Clin.Epidemiol. 2015;68:518-524 78. Kamper SJ, Ostelo RW, Knol DL, Maher CG, de Vet HC, Hancock MJ. Global Perceived Effect scales provided reliable assessments of health transition in people with musculoskeletal disorders, but ratings are strongly influenced by current status. J.Clin.Epidemiol. 2010;63:760-766 79. Dougados M. It's good to feel better but it's better to feel good. J.Rheumatol. 2005;32:1-2 80. Tubach F, Dougados M, Falissard B, Baron G, Logeart I, Ravaud P. Feeling good rather than feeling better matters more to patients. Arthritis Rheum 2006;55:526-530 81. Ostelo RW, Deyo RA, Stratford P, Waddell G, Croft P, Von KM et al. Interpreting change scores for pain and functional status in low back pain: towards international consensus regarding minimal important change. Spine (Phila Pa 1976.) 2008;33:90-94 82. Dworkin RH, Turk DC, Farrar JT, Haythornthwaite JA, Jensen MP, Katz NP et al. Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain 2005;113:9-19 83. Tubach F, Ravaud P, Baron G, Falissard B, Logeart I, Bellamy N et al. Evaluation of clinically relevant states in patient reported outcomes in knee and hip osteoarthritis: the patient acceptable symptom state. Ann Rheum Dis 2005;64:34-37 84. Kvien TK, Heiberg T, Hagen KB. Minimal clinically important improvement/difference (MCII/MCID) and patient acceptable symptom state (PASS): what do these concepts mean? Ann.Rheum.Dis. 2007;66 Suppl 3:iii40-iii41 85. Escobar A, Gonzalez M, Quintana JM, Vrotsou K, Bilbao A, Herrera-Espineira C et al. Patient acceptable symptom state and OMERACT-OARSI set of responder criteria in joint replacement. Identification of cut-off values. Osteoarthritis.Cartilage. 2012;20:87-92 86. Mannion AF, Elfering A, Staerkle R, Junge A, Grob D, Semmer NK et al. Outcome assessment in low back pain: how low can you go? Eur.Spine J. 2005;14:1014-1026 87. Mannion AF, Porchet F, Kleinstuck FS, Lattig F, Jeszenszky D, Bartanusz V et al. The quality of spine surgery from the patient's perspective. Part 1: the Core Outcome Measures Index in clinical practice. Eur.Spine J. 2009;18 Suppl 3:367-373 88. Wood AM, White IR, Thompson SG. Are missing outcome data adequately handled? A review of published 12 randomized controlled trials in major medical journals. Clin.Trials 2004;1:368-376 89. Iezzoni LI. Risk adjustment for medical effectiveness research: an overview of conceptual and methodological considerations. J.Investig.Med. 1995;43:136-150 90. Wouters MW, Wijnhoven BP, Karim-Kos HE, Blaauwgeers HG, Stassen LP, Steup WH et al. High-volume versus low-volume for esophageal resections for cancer: the essential role of case-mix adjustments based on clinical data. Ann.Surg.Oncol. 2008;15:80-87 91. Dimick JB, Staiger DO, Birkmeyer JD. Ranking hospitals on surgical mortality: the importance of reliability adjustment. Health Serv.Res. 2010;45:1614-1629 92. Desai A, Bekelis K, Ball PA, Lurie J, Mirza SK, Tosteson TD et al. Variation in outcomes across centers after surgery for lumbar stenosis and degenerative spondylolisthesis in the spine patient outcomes research trial. Spine (Phila Pa 1976.) 2013;38:678-691 93. Lingsma HF, Steyerberg EW, Eijkemans MJ, Dippel DW, Scholte op Reimer WJ, Van Houwelingen HC. Comparing and ranking hospitals based on outcome: results from The Netherlands Stroke Survey. QJM. 2010;103:99-108 94. Fischer C, Lingsma HF, van LN, Tollenaar RA, Wouters MW, Steyerberg EW. Comparing colon cancer outcomes: The impact of low hospital case volume and case-mix adjustment. Eur.J.Surg.Oncol. 2015;41:1045-1053 Summary and General discussion 269 95. Parks KA, Crichton KS, Goldford RJ, McGill SM. A comparison of lumbar range of motion and functional ability scores in patients with low back pain: assessment for range of motion validity. Spine (Phila Pa 1976.) 2003;28:380-384 96. Wittink H. Functional capacity testing in patients with chronic pain. Clin.J.Pain 2005;21:197-199 97. Nolan M, Mitchell JR, Doyle-Baker PK. Validity of the Apple iPhone(R) /iPod Touch(R) as an accelerometerbased physical activity monitor: a proof-of-concept study. J.Phys.Act.Health 2014;11:759-769 98. Attal F, Mohammed S, Dedabrishvili M, Chamroukhi F, Oukhellou L, Amirat Y. Physical Human Activity Recognition Using Wearable Sensors. Sensors.(Basel) 2015;15:31314-31338 99. Ponce H, de Lourdes Martinez-Villasenor M, Miralles-Pechuan L. A novel wearable sensor-based human activity recognition approach using artificial hydrocarbon networks. Sensors.(Basel) 2016;16:1-28 100. Barrett B, Brown D, Mundt M, Brown R. Sufficiently important difference: expanding the framework of clinical significance. Med.Decis.Making 2005;25:250-261 101. Ferreira ML, Herbert RD, Ferreira PH, Latimer J, Ostelo RW, Nascimento DP et al. A critical review of methods used to determine the smallest worthwhile effect of interventions for low back pain. J.Clin.Epidemiol. 2012;65:253-261 102. Ferreira ML, Herbert RD, Ferreira PH, Latimer J, Ostelo RW, Grotle M et al. The smallest worthwhile effect of nonsteroidal anti-inflammatory drugs and physiotherapy for chronic low back pain: a benefit-harm tradeoff study. J.Clin.Epidemiol. 2013;66:1397-1404 103. Grotle M, Brox JI, Vollestad NK. Functional status and disability questionnaires: what do they assess? A systematic review of back-specific outcome questionnaires. Spine (Phila Pa 1976.) 2005;30:130-140 104. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J.Clin.Epidemiol. 2010;63:1179-1194 105. PROMIS. Patient Outcomes Measurement Information System [Internet] 2016 Northwestern University [cited 2016 July 20]; Available from: http://www.healthmeasures.net/explore-measurement-systems/ promis 106. Fairbank J, Gwilym SE, France JC, Daffner SD, Dettori J, Hermsmeyer J et al. The role of classification of chronic low back pain. Spine (Phila Pa 1976.) 2011;36:S19-S42 107. Haskins R, Osmotherly PG, Rivett DA. Validation and impact analysis of prognostic clinical prediction rules for low back pain is needed: a systematic review. J.Clin.Epidemiol. 2015;68:821-832 108. Main CJ, Williams AC. Musculoskeletal pain. BMJ 2002;325:534-537 109. Koes BW, van TuldercM, Lin CW, Macedo LG, McAuley J, Maher C. An updated overview of clinical guidelines for the management of non-specific low back pain in primary care. Eur.Spine J. 2010;19:2075-2094 110. Centeno CJ, Elkins WL, Freeman M. Waddell's signs revisited? Spine (Phila Pa 1976.) 2004;29:1392 111. Cohen SP, Argoff CE, Carragee EJ. Management of low back pain. BMJ 2008;337:a2718 112. Henschke N, Maher CG, Ostelo RW, de Vet HC, Macaskill P, Irwig L. Red flags to screen for malignancy in patients with low-back pain. Cochrane.Database.Syst.Rev. 2013:CD008686 113. Williams CM, Henschke N, Maher CG, van Tulder MW, Koes BW, Macaskill P et al. Red flags to screen for vertebral fracture in patients presenting with low-back pain. Cochrane.Database.Syst.Rev. 2013:CD008643 114. Underwood M, Buchbinder R. Red flags for back pain. BMJ 2013;347:f7432 115. Downie A, Williams CM, Henschke N, Hancock MJ, Ostelo RW, de Vet HC et al. Red flags to screen for malignancy and fracture in patients with low back pain: systematic review. BMJ 2013;347:f7095 116. Steyerberg EW, Moons KG, van der Windt DA, Hayden JA, Perel P, Schroter S et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS.Med. 2013;10:e1001381 117. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern.Med. 2015;162:W1-73 12 270 Summary and General discussion 118. Royston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: Developing a prognostic model. BMJ 2009;338:b604 119. Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ 2009;338:b606 120. Adams ST, Leveson SH. Clinical prediction rules. BMJ 2012;344:d8312 121. Harrell FE Jr. Regression Modeling Strategies. With applications to linear models, logistic and ordinal regression and survival analysis 2nd ed. New York, NY: Springer International Publishing Switzerland; 2015 122. Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD. Validity of prognostic models: when is a model clinically useful? Semin Urol.Oncol. 2002;20:96-107 123. Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how? BMJ 2009;338:b375 124. Aalto TJ, Malmivaara A, Kovacs F, Herno A, Alen M, Salmi L et al. Preoperative predictors for postoperative clinical outcome in lumbar spinal stenosis: systematic review. Spine (Phila Pa 1976.) 2006;31:E648-E663 125. Yee A, Adjei N, Do J, Ford M, Finkelstein J. Do patient expectations of spinal surgery relate to functional outcome? Clin.Orthop.Relat Res. 2008;466:1154-1161 126. Celestin J, Edwards RR, Jamison RN. Pretreatment psychosocial variables as predictors of outcomes following lumbar surgery and spinal cord stimulation: a systematic review and literature synthesis. Pain Med. 2009;10:639-653 127. Freemantle N, Marston L, Walters K, Wood J, Reynolds MR, Petersen I. Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research. BMJ 2013;347:f6409 128. Schulz KF, Grimes DA. Allocation concealment in randomised trials: defending against deciphering. Lancet 2002;359:614-618 129. Pham T, van der Heijde D, Altman RD, Anderson JJ, Bellamy N, Hochberg M et al. OMERACT-OARSI initiative: Osteoarthritis Research Society International set of responder criteria for osteoarthritis clinical trials revisited. Osteoarthritis.Cartilage. 2004;12:389-399 130. Battie MC, Lazary A, Fairbank J, Eisenstein S, Heywood C, Brayda-Bruno M et al. Disc degeneration-related clinical phenotypes. Eur.Spine J. 2014;23 Suppl 3:S305-S314 131. Samartzis D, Borthakur A, Belfer I, Bow C, Lotz JC, Wang HQ et al. Novel diagnostic and prognostic methods 12 for disc degeneration and low back pain. Spine J. 2015;15:1919-1932 132. CBO, Nederlandse Orthopaedische Vereniging. Concept Richtlijn Geinstrumenteerde spinaalchirurgie bij degeneratieve aandoeningen van de thoracolumbale wervelkolom [Guideline in Dutch]; 2015 Summary and General discussion Key points thesis 271 272 Key points thesis What is already known on this topic Theme A. Introduction of a combined physical and psychological (CPP) programme - Worldwide, chronic low back pain (CLBP) is responsible for the greatest disease burden for society in terms of years lived in disability. - CLBP is not a diagnosis, but a symptom referring to the location of the problem and the duration of the complaints. - Although the exact aetiology is unknown it is recommended to identify subgroups benefitting different interventions, as it is unlikely that one treatment benefits all. - Multidisciplinary conservative treatment programmes provide moderate, short-lived effects. Theme B. Outcomes assessment - A patient outcome registry is an organized system that uses observational study methods. - A patient outcome registry can be used to describe care patterns, including appropriateness of care and disparities in the delivery of care. - Worldwide several spine outcome registries have been initiated on a regional or national level, tracking tens of thousands of patients. - It is suggested that outcome registries leads to demonstrable improvement in clinical outcomes and smaller variation between providers - Participation in nationwide quality improvement initiatives including continuous quality monitoring and benchmarked feedback reveals opportunities for targeted improvements. - Different core outcome sets for CLBP exist to standardize outcomes in research. - An outcome set for use in every day clinical practice and to use for continuous improvement of the quality of spine care is still lacking. - Functional ability is one of the recognised outcome domains of treatments for degenerative lumbar spine disorders. - The Oswestry Disability Index (ODI) is a widely used patient-reported outcome measure (PROM) and internationally recommended to evaluate functional ability in these patients. - The interpretation of pre- and post-treatment scores of PROMs has been a topic of research for more than two decades and is still subject to discussion. - To evaluate the course and effect of interventions in patients with degenerative lumbar spine disorders different measures of improvement in functional ability are used. Key points thesis What this thesis adds Theme A. Introduction of a combined physical and psychological (CPP) programme - The intensive multidisciplinary CPP programme is beneficial in improving and maintaining patient relevant outcomes and reducing healthcare consumption for a carefully selected group of patients with CLBP (Chapter 2-3). - The study results are comparable with previously published results of spinal surgery and seem to be even better than results from less intensive rehabilitation programmes (Chapter 2). - Patients who are employed and who are mild to moderately disabled at the start of a CPP programme are most likely to benefit from this programme (Chapter 4). Theme B. Outcomes assessment - The current impact and value of spine registries on quality of care is not yet established, regardless as to whether the intervention was non-surgical or surgical (Chapter 5). - Methodological recommendations are presented to make future evaluations and comparisons possible (Chapter 5). The recommendations comprise the organisation of outcome registries, the methods and patient-related outcomes and influencing (risk) factors used, and the analysis and reporting of results. - Application of these recommendations could lead to registries showing trends in effectiveness of interventions, monitoring the quality of spine care given, and ultimately improving the value of the care given to patients with degenerative spinal disorders (Chapter 5). - An international multidisciplinary consensus-based standard set of well-validated outcomes for CLBP was defined and is recommended for use in both clinical practice and research (Chapter 6). - These outcome measures are structured around the span of a patient’s entire cycle of care, and allow for risk adjustment (Chapter 6). - When implemented, this set facilitates meaningful comparisons of interventions and between providers, ultimately providing a continuous feedback loop, enabling on-going improvements in quality of care (Chapter 6). - The Dutch ODI version 2.1a is a valid, reliable and useful tool with for the assessment of functional ability and disability among Dutch patients with CLBP (Chapter 7). - The use of the Dutch ODI is recommended for future research in patients with degenerative lumbar spine disorders and for evaluating outcomes of secondary and tertiary spine interventions in the Netherlands (Chapter 7). - Absolute measures as a criterion of treatment success is recommended, because it is independent of the pre-treatment value and it represents the patients’ perspective: ‘It’s good to feel better but it’s better to feel good.’ (Chapter 8). - An ODI score ≤22 indicates the achievement of a patient acceptable symptom state (PASS), which also reflects a ‘normal’ healthy condition (Chapter 4 and 8). 273 274 Key points thesis What is already known on this topic Continued from page 272 - To define treatment success two concepts are current: (1) relative change values and (2) the achievement of an acceptable symptom state by reaching an absolute value. - International consensus exists to use a relative change in ODI score to indicate relevant improvement in functional ability in CLBP: (1) 30% reduction from baseline ODI score, or (2) a 10 or 15 points reduction from baseline ODI score. - Several drawbacks of change measures are acknowledged. For instance, the definition of clinical/relevant importance or meaningful change is still arbitrary and change scores are baseline dependent. Theme C. Prediction of outcomes - CLBP is a heterogeneous condition with a lack of diagnostic clarity and is one of the most common complaints for which patients seek consultation in primary care - It is estimated that 60-80% of the patients experience persistence of CLBP complaints after a year and most of them consult secondary care spine specialists for their problems. - In secondary care a large practice variation exists, as currently many of these healthcare providers cannot reliably identify which CLBP patients will benefit most from a (non-) surgical intervention. - Lumbar spine surgeries represent a sizeable proportion of the total cost of back pain, but spine surgery rates vary extensively across, and even within, countries. - A lack of professional consensus among Dutch spine surgeons exists in decision-making for spinal fusion and it is indicated that patient-related factors were not consistently incorporated in the surgeons’ treatment strategy. - To improve treatment outcomes in degenerative lumbar spine disorders it is recommended to develop a classification system to direct both surgical and non-surgical interventions, based on biomedical and psychosocial indicators and on treatment outcomes. - Different non-surgical decision tools exist; none of them are aimed for patient triage. Key points thesis What this thesis adds Continued from page 273 - The ODI-PASS is recommended for use alongside the commonly used change-score measures in patients with degenerative lumbar spine disorders to evaluate the recovery and the course of interventions, irrespective of the invasiveness (Chapter 8). - On an individual level the threshold could be used to indicate whether or not a patient with a degenerative lumbar spine disorder is a ‘responder’ after elective surgery (Chapter 8). C. Prediction of outcomes - The Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP) was developed using scientific evidence and formal multidisciplinary consensus (Chapter 9) and is internally validated to support patient triage to surgical and non-surgical spine specialists (Chapter 11). - The online NDT-CLBP consists of: (1) a pre-treatment patient-based screening questionnaire, based on biomedical and psychosocial patient-reported indicators; (2) systematic outcome monitoring; (3) prognostic patient-reported profiles predicting treatment outcomes of spine surgery and a multidisciplinary treatment programme (Chapter 9 and 11). - By means of pre-treatment patient-reported screening data derived from screening questionnaire of the NDT-CLBP, several patient-reported indicators were identified that are partly predictive of spinal surgery referral (e.g. previous surgery, leg pain intensity, positive treatment expectations, comorbidities, reduced walking distance) (Chapter 10). - Location of consultation was significantly associated with spinal surgery referral, even after correcting for case-mix indicators, which suggests a practice variation and a lack of consensus among spine surgeons (Chapter 10). - The NDT-CLBP is a tool for (shared) decision-making and includes two phases: (1) prognostic patient profiles, based on patient-reported characteristics predicting either ‘response’ or ‘non-response’ to treatment, to enhance timely patient triage to a spine surgeon or a non-surgical specialist for consultation, and (2) based on further diagnostics (e.g. imaging) and future research the profiles will be refined by including both the indicators from the first phase as well as from the diagnostic phase, to reliably refer the right patient to the right treatment (Chapter 11). A ‘proof-of-principle’ tool for phase 1 is developed; phase 2 is planned, but is beyond the scope of this thesis. - Although factors derived from the diagnostic phase (e.g. imaging) were not included, treatment outcome was predicted to an acceptable and satisfactory degree, especially for non-response to spinal surgery (Chapter 11). - To our knowledge we are the first to identify prognostic patient profiles predicting whether CLBP patients are a ‘responder’ or ‘non-responder’ to elective lumbar spine surgery or multidisciplinary pain management programme that can be used for triaging CLBP patients to the right secondary care specialist (Chapter 11). - The next step is planned and consists of external validation of the prognostic patientreported profiles with new patients and using patient samples of other secondary spine practices. This will be followed by an impact analysis of these patient profiles on decisionmaking, treatment outcomes, and costs. 275 276 Key points thesis Key points thesis Dutch summary | Nederlandse samenvatting 277 278 Dutch summary | Nederlandse samenvatting Nederlandse samenvatting Achtergrond Wereldwijd vormt lage rugpijn het grootste sociaal maatschappelijke gezondheidsprobleem en wordt uitgedrukt in aantal jaren geleefd met functionele beperkingen. Dit gaat gepaard met hoge kosten voor de maatschappij, onder meer als gevolg van werkverzuim, arbeidsongeschiktheid en de hoge mate van zorggebruik. Chronische lage rugpijn (CLRP), lage rugpijn die drie maanden of langer bestaat, is eigenlijk geen diagnose maar een symptoom verwijzend naar de lokalisatie van het probleem en de duur van de klachten. Het blijkt één van de meest voorkomende problemen te zijn waarvoor patiënten naar de huisarts of fysiotherapeut gaan. De schatting is dat bij 60-80% van deze patiënten de klachten na een jaar nog bestaan en de meesten consulteren dan medisch specialisten in de tweede lijn. De exacte etiologie van CLRP is voor de grootste groep patiënten onbekend en daardoor is de diagnose vaak ‘degeneratieve lage rug aandoening’ of ‘aspecifiek’. Doordat de etiologie onbekend is, bestaat er een heel scala aan invasieve (bijvoorbeeld chirurgie, injecties) en conservatieve behandelvormen (bijvoorbeeld fysiotherapie, rugschool, mono- of multidisciplinaire cognitieve gedragstherapie, multidisciplinaire bio-psychosociaal pijnmanagement programma’s). Ondanks decennia van wetenschappelijk onderzoek zijn de resultaten nog altijd inconsistent en controversieel en laten chirurgische behandelopties en multidisciplinaire pijnmanagement programma’s maar matige uitkomsten zien. Mede hierdoor bestaat grote praktijkvariatie tussen zorgaanbieders en bestaat er onduidelijkheid over welke behandelingen voor wie (kosten-)effectief zijn. Bovendien blijkt dat onder de Nederlandse wervelkolomchirurgen geen consensus bestaat ten aanzien van de besluitvorming voor welke patiënten met CLRP chirurgie geschikt is en worden patiënt-gerelateerde uitkomsten niet consistent betrokken in de zorgevaluatie en de behandelstrategie van de chirurg. De opbouw van het proefschrift In hoofdstuk 1 is de huidige stand van zaken van de wetenschap beschreven met betrekking tot het onderwerp CLRP in relatie tot dit proefschrift. Dit proefschrift kent drie aparte maar onderling gerelateerde thema’s. De studies (hoofdstuk 2-11) binnen deze thema’s volgen min of meer chronologisch het onderzoeksprogramma. In het eerste thema (Thema A) wordt een conservatief multidisciplinair en intensief fysiek en psychologisch pijn management programma (Combined Physical and Psychological programme; CPP programma) geëvalueerd en wordt duidelijk wie baat zou kunnen hebben bij dit behandelprogramma. Dit twee weken durend intensieve programma is eind 2006 geïntroduceerd binnen de Sint Maartenskliniek en wordt exclusief aangeboden aan patiënten met langdurig bestaande CLRP, waarbij eerdere conservatieve behandelingen hebben gefaald en voor wie wervelkolomchirurgie geen optie is. Het programma wordt onder de verantwoordelijkheid van de wervelkolomchirurgen aangeboden in een hotelsetting buiten de kliniek en bestaat uit: fysieke training, cognitief-gedragsmatige training met ‘graded activity’ en ‘graded exposure’ (het stapsgewijs opvoeren van respectievelijk activiteiten en blootstelling aan activiteiten die beperkt zijn) en educatie. Om het individueel beloop te monitoren en om het programma op groepsniveau te kunnen evalueren, worden alle deelnemers gestandaardiseerd en systematisch tot een jaar na het programma gevolgd op relevante uitkomstindicatoren (routine uitkomsten monitoring). Dit wordt online in een zogenaamde patiënten uitkomstenregister bijgehouden. 279 280 Dutch summary | Nederlandse samenvatting Het tweede thema (Thema B) is gericht op de impact van wervelkolom-gerelateerde uitkomstenregisters (zoals beschreven in thema A) op de kwaliteit van de geleverde zorg. Daarnaast is de methodologie van uitkomstenmeting, die wordt gehanteerd bij de evaluatie van de klinische praktijk en in het wetenschappelijk onderzoek bij patiënten met CLRP, nader bestudeerd. Om de zorg te kunnen evalueren en zorgaanbieders onderling te vergelijken dienen dezelfde uitkomstdomeinen middels dezelfde uitkomstmaten, die relevant zijn voor de patiënt, te worden gemeten. Echter, zo’n standaard set ontbreekt. Een alom (h)erkend uitkomsten domein is de mate van functioneren. Dit domein wordt vaak geoperationaliseerd middels de Oswestry Disability Index (ODI [0-100]; 0= geen beperking in functioneren, 100= bedlegerig), een conditie-specifiek patiënt-gerapporteerde uitkomstmaat (patient-reported outcome measure, PROM), zo ook in dit proefschrift. De eerste twee thema’s zijn een opmaat voor het derde thema (Thema C) dat gaat over de ontwikkeling en onderbouwing van een klinische beslistool voor CLRP op basis van de verzamelde gegevens in een uitkomstenregister. Het doel van deze tool is het verrichten van een poliklinische triage van patiënten naar een tweede of derdelijns wervelkolomchirurg óf een niet-chirurgisch medisch specialist. Het vormt een eerste aanzet om te voldoen aan een lang bestaande internationale aanbeveling om een classificatiesysteem te ontwikkelen dat beslissingsondersteunend is voor verwijzing naar chirurgische en niet-chirurgische behandelvormen, dat is gebaseerd op biomedische en psychosociale indicatoren en op de uitkomsten van de behandeling. Er bestaan wel verschillende beslissingsondersteunende tools in de klinische praktijk, de meeste zijn echter bedoeld voor gebruik in de eerste lijn gezondheidszorg en geen van deze tools is geschikt voor triage van patiënten naar de tweede lijn. Tot slot worden in hoofdstuk 12 de bevindingen bediscussieerd en is de betekenis ervan voor de klinische praktijk en toekomstig research uiteengezet. Per thema hebben we overkoepelende onderzoeksvraagstellingen geformuleerd die middels de resultaten van de verschillende studies zijn beantwoord. De bevindingen per thema Thema A: Introductie van een gecombineerd fysiek en psychologisch behandelprogramma voor CLRP Onderzoeksvraagstellingen: 1.Kunnen bij patiënten met CLRP die het geïntroduceerde CPP behandelprogramma hebben gevolgd, de behandeluitkomsten verbeteren en het zorggebruik verminderen? 2.Is het mogelijk om bij patiënten met CLRP een subgroep te identificeren die het meest baat hebben van dit nieuw geïntroduceerde CPP behandelprogramma (prognose van succes), zodat de toelatingscriteria geoptimaliseerd kunnen worden? In de één en twee-jaar follow-up cohort studies, gepresenteerd in de hoofdstukken 2 en 3, namen geselecteerde en gemotiveerde patiënten met lang bestaande CLRP deel aan het CPP programma. Patiënten waren matig tot ernstig beperkt in functioneren en vergelijkbaar met andere patiënten met lage rugpijn die behandeld worden in de tweede lijn. Eén jaar na het volgen van het programma konden de patiënten beter omgaan met hun rugklachten, hun dagelijks functioneren en kwaliteit van leven was relevant verbeterd en de meesten waren weer aan het werk (hoofdstuk 2). Dezelfde groep patiënten werd twee jaar na deelname nogmaals bevraagd. Het bleek dat de significante en relevante verbeteringen waren Dutch summary | Nederlandse samenvatting behouden, de meeste patiënten waren weer aan het werk en het bleek dat men substantieel minder zorg en minder (zware) pijnmedicatie gebruikte (hoofdstuk 3). In hoofdstuk 4 zijn indicatoren geïdentificeerd die een succesvolle behandeluitkomst kunnen voorspellen (prognose voor succesvolle behandeling). Succesvolle behandeluitkomst was gedefinieerd als een functionele score vergelijkbaar met scores gezien in de ‘normale’, gezonde populatie (ODI ≤22). Patiënten die voor het programma aan het werk waren en die relatief weinig of matig beperkt in functioneren zijn bij aanvang hebben het meeste baat bij het programma. Er zijn hierbij geen interactie effecten gevonden tussen patiëntkarakteristieken en ondanks bevindingen in de literatuur bleek tot onze verrassing dat psychische ‘distress’, de beleving van negatieve emoties als somberheid en gespannenheid, niet voorspellend was voor een succesvolle behandeluitkomst. Ook patiënten met een hoge mate van ervaren ‘distress’ bij aanvang van het programma, hebben baat bij dit programma. Het beperkte aantal geïdentificeerde indicatoren is bruikbaar voor de klinische praktijk, omdat ze makkelijk te herkennen zijn en ze kunnen eenvoudig bijdragen aan een snelle triage en toewijzing van patiënten aan dit programma. Omdat continue uitkomstenmeting een vast onderdeel in het CPP programma is, waren we in de gelegenheid om de één jaar ‘pilot’ resultaten, gepresenteerd in hoofdstuk 2, verder te onderbouwen met die van een groot cohort (848 patiënten). Deze recente studie liet zien dat de patiënten vergelijkbaar goede resultaten behaalden als de initiële pilotgroep. De helft verbeterde dusdanig dat hun functionele status vergelijkbaar was met die van de ‘normale’ gezonde populatie. Het korte, intensieve pijn management programma kan dus het leven aanzienlijk veranderen van een selecte groep patiënten met lang bestaande CLRP (gemiddeld 12 jaar). Thema B: Uitkomstenmeting Onderzoeksvraagstellingen: 1.Wat is de huidige waarde van wervelkolom-gerelateerde uitkomstenregisters voor de dagelijkse klinische praktijk en welke methodologie is gebruikt in deze registers? 2.Welke patiënt-gerelateerde uitkomstmaten dienen te worden gebruikt voor evaluatie van de behandeling van patiënten met degeneratieve lage rug aandoeningen? 3.Welk criterium kan worden gebruikt om een succesvolle behandeluitkomst bij patiënten met degeneratieve lage rug aandoeningen te definiëren? We hebben een systematische literatuurstudie uitgevoerd naar wervelkolom-gerelateerde uitkomstenregisters en een vragenlijst onder vertegenwoordigers van deze registers uitgezet. Wereldwijd hebben we 25 wervelkolomregisters geïdentificeerd. Er konden geen conclusies worden getrokken over de waarde dan wel de impact van deze registers op de kwaliteit van geleverde zorg, ongeacht of de zorg chirurgisch of non-chirurgisch van aard was. De 25 registers waren zeer divers en heterogeen van aard in bijvoorbeeld de opzet, de geïncludeerde doelgroepen en behandelvormen, de methodologie en analysetechnieken en de rapportage. Om in de toekomst de kwaliteit van het wetenschappelijk bewijs gepubliceerd met registerdata te verbeteren hebben we 14 aanbevelingen geformuleerd. Deze aanbevelingen kunnen leiden tot wervelkolom-gerelateerde uitkomstenregisters waarmee met de data trends, patronen, uitkomsten en daarmee de kwaliteit van de geleverde zorg kunnen worden bestudeerd. In de toekomst kunnen deze uitkomstenregisters tevens een bijdrage leveren in het oplossen van controverses die bestaan ten aanzien van de behandeling van degeneratieve wervelkolomaandoeningen. 281 282 Dutch summary | Nederlandse samenvatting De beperkingen in de registers die we hebben bestudeerd waren onder andere gerelateerd aan het feit dat verschillende uitkomstmaten zijn gebruikt, maar ook dat verschillende standaard uitkomstensets blijken te bestaan voor de evaluatie van behandeling van CLRP. Om in de toekomst behandeluitkomsten onderling te kunnen vergelijken tussen artsen, instellingen of zelfs tussen landen, is het noodzakelijk (inter-)nationaal eenduidige definities te hanteren en dient consensus te bestaan ten aanzien van een gestandaardiseerde, systematische werkwijze. In hoofdstuk 6 is een studie beschreven waarbij, in een werkgroep van professionals en patiënten, wereldwijd formele consensus is verkregen over een standaard uitkomstenset die is gebaseerd op uitkomstdomeinen die relevant zijn voor de patiënt, inclusief de beïnvloedende (risico)factoren, de meetinstrumenten en de meetmomenten. Deze set is relatief eenvoudig te implementeren in de klinische praktijk om de geleverde zorg te evalueren en is geschikt voor gebruik in wetenschappelijk onderzoek. Het advies is deze set op te nemen in landelijke uitkomstenregisters. Eén van de standaard uitkomstdomeinen waarover consensus is bereikt is de ‘mate van functioneren’. De ODI versie 2.1a is wereldwijd een veel gebruikte PROM om functioneren te objectiveren. Omdat in Nederland verschillende vertaalde versies in omloop waren hebben we deze versie conform internationale richtlijnen vertaald en hebben we de methodologische kwaliteit ervan bestudeerd. In hoofdstuk 7 staat het vertaalproces beschreven en is de conclusie dat deze Nederlandse versie van de ODI een valide en bruikbare maat is met goede meeteigenschappen. Geadviseerd wordt deze versie van de conditie-specifieke PROM te gebruiken in toekomstig wetenschappelijk onderzoek en in de evaluatie van zorg bij patiënten met lage rugpijn. Deze officiële versie is tevens opgenomen en geïmplementeerd in de onlangs gestarte nationale wervelkolomregister voor registratie van chirurgisch behandelde patiënten (Dutch Spine Surgery Registry). Internationaal bestaat consensus om een verandering in ODI-score te gebruiken om klinisch relevante verbetering te meten. Opties hiervoor zijn: (1) 30% vermindering van de baseline ODI-score, of (2) 10 of 15 punten vermindering ten opzichte van de baseline ODI-score. Echter, er zijn verschillende beperkingen in het gebruik van veranderingsscores bekend, bijvoorbeeld de definitie van wat een klinische relevante of betekenisvolle verandering is en het feit dat veranderingsscores afhankelijk zijn van de baseline score. Een ander concept om een succesvolle behandeling weer te geven is het behalen van een absolute drempelwaarde. In een studie beschreven in hoofdstuk 8, hebben we de ODI-score bepaald, waarbij patiënten aangaven dat ze tevreden zijn met de rug zoals die op dat moment is (zogenaamde ‘patient acceptable symptom state’; PASS), als maat voor succes na chirurgie bij degeneratieve lage rug aandoeningen. Voor deze studie hebben we baseline en follow-up data (één en twee jaar na de operatie) gebruikt van chirurgisch behandelde patiënten die zijn geregistreerd in Spine Tango, de Eurospine spine surgery registry. De drempelwaarde voor de PASS werd geschat op ODI-score van 22, onafhankelijk van het follow-up meetmoment. Deze waarde blijkt overeen te komen met de waarde gezien in de ‘normale’ gezonde populatie. Omdat de concepten van PASS en veranderingsscores complementair aan elkaar zijn, is de aanbeveling deze PASS als absolute drempelwaarde (ODI ≤22) te gebruiken naast de veel gebruikte veranderingsscores. Beide maten dienen bij follow-up metingen gerapporteerd te worden en uitgedrukt in proporties van patiënten die de waarde hebben behaald. Dutch summary | Nederlandse samenvatting Thema C: Voorspellen van uitkomsten Onderzoeksvraagstellingen: 1.Is het mogelijk een triage (beslissingsondersteunende) tool te ontwikkelen, gebaseerd op valide en betrouwbare patiënten profielen, dat de triage van patiënten met chronische lage rugklachten naar een wervelkolomchirurg of een niet-chirurgisch medisch specialist ondersteunt? Om de behandeluitkomsten en daarmee de kwaliteit van geleverde zorg te verbeteren hebben we een triage ondersteunende tool ontwikkeld; de Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP). Het doel is dat middels deze tool patiënten op basis van hun profiel vroegtijdig naar de juiste zorgprofessional (chirurg of niet-chirurgisch medisch specialist) kunnen worden verwezen. De ontwikkeling hiervan staat in hoofdstuk 9 beschreven. Op basis van een literatuuronderzoek is een formele Delphi consensus procedure uitgevoerd. Uiteindelijk zijn 47 indicatoren geselecteerd die mogelijk de behandeluitkomst en/of het persisteren van lage rugpijn voorspellen. Een eerste ‘proof-of-concept’ versie van de NDT-CLBP is opgezet, bestaand uit een web-based screeningvragenlijst en systematische uitkomstenmeting (PROMs en klinische uitkomsten) die zijn ingebouwd in een online uitkomstenregister en een eerste beslissingsalgoritme is opgesteld. Om te verifiëren of we met dit systeem op de goede weg zaten, hebben we eerste analyses uitgevoerd met data van de screeningvragenlijst dat is ingevuld door 4,987 patiënten met CLRP die naar de chirurg zijn verwezen en vervolgens al dan niet een wervelkolomoperatie hebben ondergaan. Die studie staat beschreven in hoofdstuk 10. Het bleek dat tien patiënt-gerapporteerde factoren voorspellend waren voor indicatie operatie, namelijk vrouwelijk geslacht, eerdere rugoperatie gehad, pijn in de boven-rug, verminderde loopafstand, positieve verwachtingen ten aanzien van de behandeluitkomst, obesitas, comorbiditeiten, en de locatie van het consult. Echter, de mate dat het model de uitkomst, verwijzing naar operatie, voorspelde was relatief laag en daarmee lijken andere niet gemeten factoren meer bepalend. Ondanks deze matige prestatie lijkt het mogelijk met patiënt-gerapporteerde pre-consult gegevens het behandelbeloop te voorspellen. In een vervolgstudie, gepresenteerd in hoofdstuk 11, hebben we bestudeerd of behandeluitkomsten, ‘respons’ (succes) en ‘non-respons’ (falen) een jaar na chirurgie en CPP programma, kunnen worden voorspeld. Hierbij is gekeken welke (combinatie van) patiëntgerapporteerde indicatoren, bepaald in hoofdstuk 9 en uitgevraagd middels de web-based screeningvragenlijst van de NDT-CLBP, voorspellend zijn. Vier verschillende prognostische patiënt-gerapporteerde profielen zijn geïdentificeerd, twee voor elke behandeling. Belangrijke indicatoren die de behandeluitkomst bepalen zijn de mate van functioneren (ODI-score) voor aanvang van de behandeling, eerdere lage rugoperaties hebben gehad, psychosociaal disfunctioneren, verwachtingen ten aanzien van behandeluitkomst en een zogenaamde rode vlag voor leeftijd (klachten gestart <20 of >50 jaar). De vier modellen zijn stabiel voor de CLRP-populatie in Sint Maartenskliniek. De mate waarin de modellen de uitkomst voorspellen is matig en zullen diagnostische indicatoren, bijvoorbeeld röntgenfoto’s en via lichamelijk onderzoek, mogelijk mede bepalend zijn. Het non-respons model voor chirurgie presteerde opmerkelijk goed. Tot slot zijn in hoofdstuk 12 de belangrijkste bevindingen van dit proefschrift samengevat en bediscussieerd. Methodologische overwegingen zijn per thema besproken evenals de mogelijke betekenis van de bevindingen voor de klinische praktijk en zijn suggesties gedaan voor verder onderzoek. 283 284 Dutch summary | Nederlandse samenvatting Concluderend Gebaseerd op de studies gepresenteerd in dit proefschrift blijkt dat het geïntroduceerde CPP pijnmanagement programma werkzaam en succesvol is voor een zorgvuldig geselecteerde subgroep van patiënten met CLRP. Continue uitkomstenmonitoring middels een uitkomstenregister, een heldere definitie van behandeluitkomst en het hanteren van strikte selectiecriteria voor behandeling zijn elementen die bijdragen aan het verbeteren van de kwaliteit van geleverde zorg aan patiënten met CLRP. Voor de definitie van succes als behandeluitkomst is onze aanbeveling om de absolute ODI drempelwaarde van 22 (uit 100) te gebruiken naast de alom bekende veranderingsmaten. Uit dit proefschrift blijkt dat verschillende subgroepen van patiënten met CLRP te onderscheiden te zijn met verschillende prognostische profielen die gebaseerd zijn patiënt-gerapporteerde vragenlijsten. Deze profielen kunnen behulpzaam zijn bij de besluitvorming tussen de patiënt en de clinicus ten aanzien van het vervolgtraject (verdere diagnostiek en eventuele behandeling). Het blijkt dat voor de populatie CLRP patiënten in de Sint Maartenskliniek het relatief goed is te voorspellen welke patiënten zullen falen na een operatie. Potentieel kunnen dan in de toekomst onnodige verwijzingen en rugoperaties worden voorkomen en kunnen deze patiënten vroegtijdig worden verwezen naar een niet-chirurgisch medisch specialist. Het onderzoeken van de externe validiteit (generaliseerbaarheid) en de maatschappelijke impact van de ontwikkelde patiëntprofielen is een vereiste voordat harde conclusies kunnen worden getrokken ten aanzien van de NDT-CLBP. Wanneer daadwerkelijk middels patiëntprofielen verschillende subgroepen zijn te onderscheiden, zonder dat de etiologie volledig bekend is, dan leidt dit in de toekomst mogelijk tot een meer parallelle benadering in besluitvorming voor behandeling. Echter, dit staat haaks op de in (inter-)nationale richtlijnen aanbevolen seriële ‘stepped care’ benadering. Hierbij wordt wervelkolomchirurgie als een ‘last resort’ beschouwd en wordt dus pas geadviseerd als niets anders werkt. Wanneer de patiëntprofielen robuust blijken te zijn dan zouden de aanbevelingen hieromtrent in internationale en in de recent verschenen nationale conceptrichtlijn voor geïnstrumenteerde spinaalchirurgie bij degeneratieve aandoeningen van de thoracolumbale wervelkolom, in heroverweging genomen moeten worden. Dit zou een belangrijke paradigma verschuiving in het klinische redeneren en in de besluitvorming voor medische specialisten betekenen. Het prognostisch classificeren van patiënten met CLRP op basis van behandeluitkomsten verdient de hoogste prioriteit op de research agenda en studies van hoge methodologische kwaliteit zijn noodzakelijk voor juiste screening en diagnostiek bij deze doelgroep. Dit draagt substantieel bij aan toekomstige verbetering van behandeluitkomsten bij deze patiënten en kan het leiden tot een meer efficiënte gezondheidszorg, waarbij de enorme sociaal maatschappelijke last beduidend is verminderd. Dutch summary / Nederlandse samenvatting Acknowledgements | Dankwoord 285 286 Acknowledgements | Dankwoord Dankwoord Yes, het eindstation is in zicht . Alhoewel, in zekere zin is mijn promotie slechts een halteonderweg, omdat de projecten, die de basis vormen voor dit proefschrift, ook gewoon mijn werk is. In ieder geval, tot dusver betekent de reis die tot dit proefschrift leidde, zoveel meer voor mij dan de afzonderlijke artikelen die het bevat. In letterlijke zin betekent het treinreizen, vooral naar Nijmegen en later ook onder andere naar Amsterdam en figuurlijk geeft het mijn ontwikkeling aan in een tweede carrière; die van de research en de klinische epidemiologie. Met de verdediging van dit proefschrift rond ik een absoluut enerverende en inspirerende tocht af, waarbij het laatste jaar vooral in het teken stond van het afronden van ‘het boekje’. Het is nu echt ‘AF’, de spreekwoordelijke habijt kan definitief aan de kapstok of liever gezegd naar zolder. Eindelijk kan ik iedereen bedanken die daar direct of indirect aan heeft bijgedragen, want zoiets doe je niet alleen. Dus: ‘Iedereen bedankt’ . Tsja, dit is wat summier, een aantal personen wil ik toch in het bijzonder danken en even in het zonnetje zetten. Allereerst prof. dr. de Kleuver, beste Marinus, vanaf het begin ben je als inhoudelijk eindverantwoordelijke betrokken bij de verschillende studies en de ideeën die we hadden rondom triage. Je stap naar VUmc – Orthopedie betekende dat de deuren naar de academische wereld wagenwijd open konden en vooral dat je promotor kon zijn. Wat een eer dat ik je eerste promovenda ben. En ja, ik ben trots op mijn boekje maar nog meer op onze samenwerking en wat we, samen met de spine unit, bereiken! Je visie, snelle in-/overzicht, deskundigheid, passie voor de orthopedie/wervelkolomchirurgie en de wetenschap, werken bijzonder inspirerend. Een betere supervisor die altijd bereikbaar is voor feedback, ‘waar ook ter wereld’ en tacticus die denkt in mogelijkheden kan ik me niet voorstellen. Mede dankzij jouw gedrevenheid en het in mij gestelde vertrouwen is het proefschrift afgerond. Je enthousiasme, betrokkenheid en steun is van onschatbare waarde. Heel veel dank daarvoor. Met je aanstelling als afdelingshoofd orthopedie in Radboudumc bestaat nu de mogelijkheid om de samenwerking in orthopedie, klinische epidemiologie en outcome research een verdere boost te geven. Prof. dr. Ostelo, beste Raymond, wat bijzonder dat je als oud-studiegenootje van KBW in Maastricht nu mijn tweede promotor bent. In een later stadium ben je ‘ingevlogen’. Sinds de komst van Marinus de Kleuver in VUmc ben je volop betrokken bij de verder ontwikkeling en wetenschappelijke onderbouwing van de Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP). Tijdens overleggen vullen jullie elkaar heel goed aan en je scherpe en kritische methodologische blik en grenzeloze optimisme komen daarbij goed van pas. Daarbij hebben we constructieve discussies, waar ik altijd vol nieuwe energie en inspiratie vandaan kom. Door ook Hanneke van Dongen als post-doc HTA aan te stellen op het NDT-CLBP project hebben we een top(kern)team en kan het niet anders dan dat de NDT-CLBP in welke vorm dan ook gaat slagen. Veel dank daarvoor. Beste Hanneke, fijn dat we soms even de tijd nemen om heerlijk te sparren. Dr. Spruit (co-promotor), beste Maarten, initieel was je betrokken als inhoudsdeskundige en coauteur en later ook als co-promotor. Dat is niet voor niets: je scherpe analytische vermogen is onevenaarbaar. In combinatie met Marinus de Kleuver en Raymond Ostelo prijs ik me gelukkig met zo’n team, hoewel ik jullie nog niet één keer projectmatig samen aan tafel heb gehad . Je rake opmerkingen en je scherpe kritische blik hebben me vaak verder geholpen en het heeft dit proefschrift zeker naar een hoger plan getild. 287 288 Acknowledgements | Dankwoord Een speciaal woord van dank is gericht aan dr. van Limbeek. Beste Jacques, als directeur van het toenmalige Research Development & Education heb je me indertijd aangenomen als kwaliteitsadviseur revalidatie. Als visionair en strateeg heb je me geïnspireerd en een snelle scherpe denker zoals jij zijn er weinig. Evaluatie van behandelprogramma’s was onderdeel van mijn werk en je hebt me de ruimte en mogelijkheden gegeven me als klinisch epidemioloog te ontwikkelen en te profileren. Tot op de dag van vandaag ben ik daar erg blij om. Je deur stond letterlijk altijd open voor methodologische en statistische vragen, waarop altijd een individueel inspirerend college volgde. Daarbij heb je me ook het Miettiniaans denken bijgebracht – dank daarvoor! Ik ben blij dat we samen nog een gastcollege van Ollie Miettinen hebben kunnen bijwonen. Bovendien zal ik dankzij jou ook nimmer John Snow vergeten (één van de grondleggers van de epidemiologie). Dank voor dit alles, ik zie uit naar je vragen aangaande predictiemodellen tijdens de verdediging en ik hoop je van constructief repliek te kunnen dienen. Leden van de manuscriptcommissie, prof. dr. (Barend) van Royen (voorzitter beoordelingscommissie en afdelingshoofd VUmc orthopedie), prof. dr. (Maurits) van Tulder, prof. dr. (Ronald) Bartels, prof. dr. (Jo) Nijs en dr. (Jaqcues) van Limbeek, dank voor de tijd en energie die jullie hebben gestoken in het lezen en beoordelen van mijn proefschrift. Ik ben benieuwd naar jullie vragen tijdens mijn verdediging. Beste Barend, ik voel me altijd erg welkom op jullie afdeling en wat fijn dat ik altijd bij jullie terecht kon en van jullie diensten gebruik kon maken. Dank voor je betrokkenheid. En Tsjitske Haanstra, beste Tjitske, wat een fijne samenwerking hebben we en we vullen elkaar mooi aan. Ook al vertrek je naar de Brandwondenstichting, de methodologie is wat ons verbindt en samen kunnen we de patiënt-gerelateerde uitkomsten hoogtij laten vieren. Veel dank gaat uit naar mijn beide paranimfen: Joke Vriezekolk en Nelleke Sicco Smit. Joke, collega-vriendin ‘van-de-reuma’: je grenzeloze enthousiasme en betrokkenheid inspireert mij. Je bent geweldig, je hebt het zelfs gered om bij één van mijn artikelen je eerste ‘acknowledgement’ te behalen! Wat fijn dat je aan mijn zijde wil staan tijdens mijn verdediging. Het geeft me nu al rust dat ik de lastige vragen kan doorspelen Nelleke, lieve zorgzame vriendin ‘van-vele-jaren-terug’ en van het wildwater-kanoën. Nadat ik je geholpen had je scriptie voor je echo-opleiding af te ronden, zou jij in ruil mijn ‘tasje vasthouden’ tijdens mijn promotie. Weliswaar is het mijn boekje en excuses dat het wat zwaar is geworden. Het is heerlijk om even op verhaal te komen bij je op de finca of om heerlijk samen een eind te wandelen. Ik verheug me op je gezelschap voor, tijdens en uiteraard na de verdediging. Natuurlijk dank ik ook al die patiënten die geparticipeerd hebben in de verschillende studies en nog participeren in het onderzoek. Ook de Nederlandse Vereniging voor Rugpatiënten ‘De Wervelkolom’ voor hun adviserende rol in het NDT-CLBP project. Een deel van het onderzoek is uitgevoerd in samenwerking met de collega’s van RealHealthNL. Veel dank voor jullie gedrevenheid, inzet en betrokkenheid. John O’Dowd, dear John, many thanks for your trust in me, our collaboration and your introduction to prof. (Jeremy) Fairbank, to further substantiate the findings of our RealHealthNL studies. Dear Jeremy, thanks for your excellent and inspiring clinical and scientific insights, critical review, and collaboration. I am looking forward to future projects. Acknowledgements | Dankwoord De stafleden orthopedie van Sint Maartenskliniek en in het bijzonder de leden van de spine unit wil ik graag danken voor de interesse en de waardering voor het onderwerp van ‘outcome assessment & decision-making’. Het vertrouwen dat jullie hebben en de betrokkenheid en enthousiasme die jullie allen tonen is enorm. Het is fijn af en toe aan te mogen sluiten bij het spine overleg en super-inspirerend om te horen hoe de (individuele) uitkomsten en de NDTCLBP nu al een bijdrage leveren in jullie drukke dagelijkse praktijk. Lieve collega’s en oud-collega’s van OrthoResearch heel veel dank voor jullie vriendschap, steun en begrip tijdens m’n promotietraject en vooral tijdens de welbekende ‘laatste loodjes’. De ‘rest’ van de afdeling Research, dank voor jullie gezelligheid, interesse, collegialiteit en natuurlijk niet vergeten de cake-van-de-week. Dank ook aan de leden van de werkgroep MaartensFacts. De overeenkomst tussen ‘lage rugpijn’ en ‘oude kaaskroketten’ is onmiskenbaar! Bart Nienhuis, dank voor je interesse in mijn onderzoek, eerst als manager naast Jacques van Limbeek en later als kamergenoot. Je kritische ‘down-to-earth’ houding ten aanzien van onderzoek met vragenlijsten houd me scherp. Het doet me deugd dat je toch ook inziet dat het ergens toe kan leiden. Het is fijn af en toe te filosoferen over de wetenschap en ‘het leven’ tijdens overheerlijke bak eigenhandig gezette koffie . Er is gelukkig meer dan alleen werk: beste familie en vrienden, ik heb jullie gemist. Eindelijk heb ik meer tijd voor jullie en voor de geneugten van het leven. Daarbij hoort ook de muziek, mijn cello voelde zich al bijna in de steek gelaten en ik ben blij dat er alweer mooie concerten in het verschiet liggen. Enkele persoonlijke woorden wil ik graag richten tot die mensen die me heel erg dierbaar zijn. Helaas zijn Emile en Marjolein, pa en ma, niet bij de promotie, mijn trouwe fans van het eerste uur. Zij hebben de basis gelegd voor wie ik nu ben en van jongs af aan heb ik van hen alle gelegenheid gekregen om me verder ontwikkelen. Dankzij hen heb ik geleerd dat als je gemotiveerd en gedisciplineerd bent, met hard werken van alles mogelijk is. Pa had de verdediging graag mee willen maken, helaas laat zijn gezondheid vandaag de dag dit niet meer toe en hopen we op een later en rustiger moment het feestje opnieuw over te doen bij hem thuis. Eens te meer ben ik gelukkig dat ook Hans en Riet er zijn en zoveel interesse tonen. Lieve Astrid, lief zussie en uiteraard ook Gerard, wat ben ik zielsgelukkig met jullie. Man̆ana is toch nog gekomen. Volgende zomer samen genieten bij Man̆ana Man̆ana? “Quand on travaille pour plaire aux autres on ne peut pas réussir, mais les choses qu’on a faites pour se contenter soi-même on toujours une chance d’intéresser quelqu’un.” (Marcel Proust, 1871-1922) 289 290 291 Curriculum Vitae 292 Curriculum Vitae Curriculum Vitae Miranda van Hooff was born on May 27, 1969 in Diepenveen, The Netherlands. After graduating secondary school (HAVO, Alexander Hegius SG, Deventer) in 1987 she studied physiotherapy at the Saxion University of Applied Sciences in Deventer (then IJselland). She followed her last internship in a rehabilitation centre for spinal cord injury in Grenoble (France) where her interest in spinerelated disorders was stimulated. After receiving her bachelors’ degree in 1991 she followed a master programme health sciences for physiotherapists with a specialisation in clinical epidemiology at the Maastricht University. After graduation in 1994 she worked from 1994 to 2006 as a physiotherapist specialised in neurological rehabilitation and as a health care advisor at the rehabilitation center Klimmendaal (then Kastanjehof) in Apeldoorn. Next to her work as a rehabilitation physiotherapist, in 2004 she worked as a parttime junior epidemiology advisor for health outcome indicators at the Knowledge Institute for Medical Specialists (then CBO) in Utrecht. Appreciating the importance of continuous health outcomes assessment and evaluation of clinical performance by healthcare providers themselves, Miranda realized that working as a health scientist, would indirectly affect more patients than she could ever treat as a therapist herself. She decided to leave direct patient care behind. In 2006, she started working as a senior quality advisor at the department of Rehabilitation in the Sint Maartenskliniek. In this position she was responsible for the implementation and evaluation of treatment programmes and the implementation of an organisation wide quality system. Since 2010, Miranda is a full-time researcher to support research performed at the orthopaedic department of the Sint Maartenkliniek. She is involved in several spine, hip arthroplasty, and prosthetic joint infections related clinical (outcomes) studies and systematic reviews. In 2012, she started the development of the international acclaimed Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP) that culminated in this thesis. Late 2012 this thesis was conceptualised. In that same year she was registered as an epidemiologist A. Next to her work at Sint Maartenskliniek, from 2013 to April 2016 she worked as a part-time clinical epidemiologist at the Dutch Institute for Clinical Auditing (DICA) to set up and implement for example the Dutch Spine Surgery Registry (DSSR) for the Dutch Spine Society. She is still involved as a member of the scientific committee of the DSSR and works in close collaboration with the Scandinavian spine registries (SweSpine, NorSpine, DaneSpine). Currently, she is working as a researcher & clinical epidemiologist at the Sint Maartenskliniek and is engaged in several orthopaedic and especially in spine-related (outcomes) research projects. She works in close collaboration with VU university medical centre, VU University EMGO in Amsterdam, and the Radboud university medical centre in Nijmegen. 293 294 295 Publications 296 Publications List of publications This thesis Hooff ML, van Dongen JM, Coupé VMH, Spruit M, , Ostelo RWJG, De Kleuver M Prognostic patient-reported profiles for referral to secondary or tertiary spine specialists – The Nijmegen Decision Tool for Chronic Low Back Pain. [Submitted] Van Dongen JM, Van Hooff ML, Spruit M, De Kleuver M, Ostelo RWJG Patient-reported factors partly predicted referral to spinal surgery in a consecutive cohort of 4,987 chronic low back pain patients. [Under review] Van Hooff ML, Mannion AF, Staub LP, Ostelo RWJG, Fairbank JCT (2016) Determination of the Oswestry Disability Index score equivalent to a ‘satisfactory symptom state’ in patients undergoing surgery for degenerative disorders of the lumbar spine – A Spine Tango registrybased study. Spine J. Oct;16(10):1221-1230. doi: 10.1016/j.spinee.2016.06.010. Epub 2016 Jun 22. Van Hooff ML, Jacobs WC, Willems PC, Wouters MW, De Kleuver M, Peul WC, Ostelo RW, Fritzell P (2015) Evidence and practice in spine registries. A systematic review, and recommendations for future design of registries. Acta Orthop Oct;86(5):534-44. doi: 10.3109/17453674.2015.1043174. Clement RC, Welander A, Stowell C, Cha TD, Chen JL, Davies M, Fairbank JC, Foley KT, Gehrchen M, Hagg O, Jacobs WC, Kahler R, Khan SN, Lieberman IH, Morisson B, Ohnmeiss DD, Peul WC, Shonnard NH, Smuck MW, Solberg TK, Stromqvist BH, Van Hooff ML, Wasan AD, Willems PC, Yeo W, Fritzell P (2015) A proposed set of metrics for standardized outcome reporting in the management of low back pain. Acta Orthop. Oct;86(5):523-33. doi: 10.3109/17453674.2015.1036696. Van Hooff ML, Spruit M, Fairbank JCT, Van Limbeek J, Jacobs WCH (2015) The Oswestry Disability Index (version 2.1a): Validation of a Dutch language version. Spine (Phila Pa 1976) Jan 15;40(2):E83-90. doi: 10.1097/BRS.0000000000000683. Van Hooff ML, Van Loon J, Van Limbeek J, De Kleuver M (2014) The Nijmegen Decision Tool for Chronic Low Back Pain. Development of a clinical decision tool for secondary or tertiary spine care specialists. PLoS One. Aug 18;9(8):e104226. doi: 10.1371/journal.pone.0104226. eCollection 2014. Van Hooff ML, Spruit M, O’Dowd JK, Van Lankveld W, Fairbank JC, Van Limbeek J (2014) Predictive factors for successful clinical outcome 1 year after an intensive combined physical abd psychological programme for chronic low back pain. Eur Spine J 23(1):102-12. Van Hooff ML, Ter Avest W, Horsting PP, O’Dowd J, De Kleuver M, Van Lankveld W, Van Limbeek J (2012) A short, intensive cognitive behavioral program for pain management reduces healtcare-use in patients with Chronic Low Back Pain. Two-year follow up results of a prospective cohort. Eur Spine J 21(7):1257-64. 297 298 Publications Van Hooff ML, Van der Merwe JD, O’Dowd J, Pavlov PW, Spruit M, De Kleuver M, Van Limbeek J (2010) Daily functioning and self-management in patients with chronic low back pain after anintensive cognitive behavioral programme for pain management. Eur Spine J 19(9):1517-26. Other publications peer-reviewed journals Van Diek FM, Albers CGM, Van Hooff ML, Meis JF, Goosen JHM [Accepted for publication] The use of implant sonication when screening for infection in revision surgery. Acta Orthop. Bier JD, Ostelo RWJG, Van Hooff ML, Wildervanck N, Koes BW, Verhagen AP [Accepted for publication] Validity and reproducibility of the translated STarT Back Tool in low back pain patients in Dutch primary care. Physical Therapy. Baauw M, Van Hooff ML, Spruit M (2016) Current construct options for revision of large acetabular defects. A systematic review. JBJS Reviews Nov;4 (11): e2. http://dx.doi.org/10.2106/ JBJS.RVW.15.00119. Faraj SSA, Holewijn RM, Van Hooff ML, De Kleuver M, Pellisé F, Haanstra TM (2016) De novo degenerative lumbar scoliosis: a systematic review of prognostic factors for curve progression. Eur Spine J (2016) 25: 2347. Jacobs AM, Van Hooff ML, Meis JF, Vos F, Goosen JH (2016) Treatment of prosthetic joint infections due to Propioni bacterium. Acta Orthop Feb;87(1):60-6. Baauw M, Van Hellemondt GG, Van Hooff ML, Spruit M (2015) The accuracy of positioning of a custom-made implant within a large acetabular defect at revision arthroplasty of the hip. Bone Joint J. Jun;97-B(6):780-5. doi: 10.1302/0301-620X.97B6.35129. Altmann VC, Hart AL, Vanlandewijck YC, Van Limbeek J, Van Hooff ML (2015).The impact of trunk impairment on performance of wheelchair activities with a focus on wheelchair court sports: a systematic review. Sports Med Open. 2015;1(1):6. Epub May 7. Hofstede SN, Nouta KA, Jacobs W, Van Hooff ML, Wymenga AB, Pijls BG, Nelissen RG, Marangvan de Mheen PJ (2015) Mobile bearing vs fixed bearing prostheses for posterior cruciate retaining total knee arthroplasty for postoperative functional status in patients with osteoarthritis and rheumatoid arthritis. Cochrane Database Syst Rev Feb 4;2:CD003130. doi: 10.1002/14651858.CD003130.pub3. Althuizen MNR, Van Hooff ML, Van den Berg-van Erp SHM, Van Limbeek J, Nijhof MW (2012) Early failures in large head metal-on-metal total hip arthroplasty. Hip Int 22(6):641-7 Conference proceedings thesis-related – oral presentations Van Hooff ML, Van Dongen JM, Spruit M, Coupé V, Ostelo RWJG, De Kleuver M. Prediction of treatment outcomes for patients with Chronic Low Back Pain: the development of a clinical decision guideline for spine surgeons. XIV International Back & Neck Pain Forum Buxton (31 May – 3 June 2016) Publications Van Hooff ML, Van Dongen JM, Spruit M, Ostelo RWJG, De Kleuver M. Prediction of treatment outcomes for patients with Chronic Low Back Pain: the development of a clinical decision guideline for spine surgeons. Global Spine Congress Dubai (13-16 April 2016) Van Hooff ML, O'Dowd JK, Van Loon J, Spruit M. Long-term follow-up results of a Combined Physical and Psychological program for patients with Chronic Low Back Pain. Global Spine Congress Dubai (13-16 April 2016) Van Hooff ML, O’Dowd JK, Van Loon J, Spruit M. Long-term follow up of a combined Physical and psychological programme for patients with longstanding chronic low back pain. SPBR Bournemouth (5-6 November 2015) Van Hooff ML (Invited speaker, in Dutch) RealHealthNL een innovatief programma: Uitkomsten en voor wie geschikt? NOV Veldhoven (15-16 Oktober 2015) Van Hooff ML, Jacobs WCH, Stoefs J, Fritzell P. During Pre-conference Focus group meeting: Global collaboration on spine registries. ISSLS Seoul (2 June 2014) Van Hooff ML, Spruit M, Mannion AF, Fairbank JCT. What is the level of disability on the Oswestry Disability Index that most spine surgery patients are satisfied with? NOV Rotterdam (6-7 February 2014) Van Hooff ML, O’Dowd J, De Kleuver M, Fairbank J, Van Limbeek J. Clinical outcomes of a combined physical and psychological program in a large cohort of longstanding chronic low back pain. SPBR London (14-15 November 2013) Van Hooff ML, O’Dowd J, Spruit M, Fairbank J, Van Limbeek J. Factors predicting clinical outcome one year after an intensive pain management programme for chronic low back pain. ISSLS Scottsdale (13-17 May 2013) Van Hooff ML, O’Dowd J, Spruit M, Fairbank J, De Kleuver M, Van Limbeek J. Factors predicting clinical outcome one year after an intensive pain management programme for chronic low back pain. Nominated best paper award. Global Spine Congress Hong Kong (4-6 April 2013) Van Hooff ML, De Kleuver M, Van Loon J, Van Limbeek J. Subgrouping patients with Chronic Low Back Pain: Development of a clinical decision guideline for spine surgeons. Global Spine Congress Hong Kong (April 4-6, 2013) Van Hooff ML, O’Dowd J, Spruit M, Van Limbeek J. Factors predicting clinical outcome one year after an intensive pain management programme for chronic low back pain. SBPR Isle of Man (8-9 November, 2012) Van Hooff ML, De Kleuver M, Van Lankveld W, Van Limbeek J. Subgrouping patients with chronic low back pain. SpineWeeek ISSLS Amsterdam (28 May – 1 June 2012) 299 300 Publications Van Hooff ML, Horsting P, O’Dowd J, De Kleuver M, Van Lankveld W, Van Limbeek J. Considerable long term reduction in pain and healthcare use in patients with chronic low back pain after a short intensive pain management program. SpineWeek ISSLS Amsterdam (28 May – 1 June 2012) Van Hooff ML, Van der Merwe JD, O’Dowd J, Pavlov PW, Spruit M, De Kleuver M, Van Limbeek J. Daily functioning and self-management in patients with chronic low back pain after an intensive cognitive behavioral pain management program: 1 year follow up results. WEON ‘Research with Impact’ Nijmegen (10-11 June 2010) Van Hooff ML, Van der Merwe JD, O’Dowd J, Pavlov PW, Spruit M, De Kleuver M, Van Limbeek J. Results of a short intensive cognitive behavioral pain management program for patients with chronic low back pain: a cohort study. SBPR Chepstow (November 5-6 2009) Van Hooff ML, De Kleuver M, Van der Merwe JD, O’Dowd J, Pavlov PW, Spruit M, Van Limbeek J. Results of a short intensive cognitive behavioral pain management program for patients with chronic low back pain: a cohort study. EuroSpine Warsaw (21-24 November 2009) Van Hooff ML, Van der Merwe JD, O’Dowd J, Pavlov PW, Spruit M, De Kleuver M, Van Limbeek J. Resultaten van een kort, intensief cognitief gedragsmatig programma voor patiënten met chronische lage rugklachten: een prospectieve cohort studie. Bijeenkomst Zorgverzekeraars. Sint Maartenskliniek Nijmegen (4 September 2009) Van Hooff ML, Van der Merwe JD, O’Dowd J, Pavlov PW, Spruit M, De Kleuver M, Van Limbeek J. Results of a short intensive cognitive behavioral pain management program for patients with chronic low back pain: a cohort study. Global Spine Congress San Fransisco (23-26 June 2009) Van Hooff ML, Horsting P, De Kleuver M (in Dutch). Goed resultaat van een cognitief gedragsmatig programma voor patiënten met chronische lage rugklachten: een prospectieve cohort studie. NOV Spring meeting Utrecht (15 May 2009) Conference proceedings thesis-related – poster presentations Van Hooff ML, Van Dongen JM, Groot D, Spruit M, Coupé VMH, Ostelo RWJG, De Kleuver M. Prediction of treatment outcomes for patients with chronic low back pain: The development of a clinical decision guideline for spine surgeons. SpineWeek ISSLS Singapore (16-20 May 2016) Van Hooff ML, Van Loon J, De Kleuver M. Feasibility of the Nijmegen Decision Tool to support spine surgeons in the triage of Chronic Low Back Pain patients. ISSLS Seoul (3-7 June 2014) Van Hooff ML, O’Dowd J, De Kleuver M, Fairbank J, Van Limbeek J. Clinical outcomes of a combined physical and psychological program in a large cohort of longstanding chronic low back pain. EuroSpine e-poster Liverpool (2-4 Oktober 2013) Van Hooff ML, De Kleuver M, Van Loon J, Van Limbeek J. Development of a clinical decision tool to support spine surgeons in the triage of chronic low back pain patients. EuroSpine e-poster Liverpool (2-4 Oktober 2013) Publications Van Hooff ML, Horsting P, O’Dowd J, De Kleuver M, Van Lankveld W, Van Limbeek J. Positive Long-term results of a short, intensive pain management program for functioning, quality of life and healthcare use in patients with chronic low back pain. EFIC Hamburg (21-24 September 2011) Van Hooff ML, Van Lankveld W, O’Dowd J, De Kleuver M, Van Limbeek J. Patients with longstanding back pain improve on cognitive behavioral variables after a short, intensive pain management program. EFIC Hamburg (21-24 September 2011) Media thesis-related Van Hooff ML, De Frêtes A, De Kleuver M (in Dutch). Nijmegen Decision Tool voor patiënten met chronische lage rugklachten. Nederlands Tijdschrift voor Revalidatiegeneeskunde (NTR 2015). De Kleuver M, Den Broeder A, Swierstra B, Van Hooff ML (in Dutch). Maartenskliniek draait warm met uitkomstmaten. Medisch Contact (16 March 2015). Van Hooff ML, De Kleuver M (spine unit SMK; in Dutch). Vragenlijst helpt bij lage rugpijn. De Gelderlander (24 September 2014). Van Hooff ML namens De Kleuver M, Ostelo RWJG, Voogt L. The Nijmegen Decision Tool for Chronic Low Back Pain. Nominated for Porter Prize Value Based Healthcare Europe (January 2014). Working group Low Back Pain of International Consortium for Health Outcomes Measurment (ICHOM). Low Back Pain Data collection User Manual and Flyer Standard Set LBP (November 2013). Available from: http://www.ichom.org/project/low-back-pain/ Van Hooff ML namens RealHealthNL, Spine unit Sint Maartenskliniek (in Dutch). RealHealth: Gezondheidswinst inzichtelijk. Bewezen effectief. Nominated for Achmea Quality Award 2013 (12 September 2013). Middelhuis T, Van Hooff M, Spruit M. RealHealth & Sint Maartenskliniek (in Dutch). Beter functioneren met chronisch lage rugpijn. Wervelwind (July 2013); p 11-3. Van Hooff ML, O’Dowd J, Spruit M, Fairbank J, Van Limbeek J. Identified indicators that predict clinical outcomes after intensive pain management program (Based on abstract ISSLS Scottsdale (13-17 May 2013). Published online by HealthDay in Japanese as Educational Material June 2013. Available from: http://www.ds-pharma.co.jp/ Trainers RealHealthNL, Van Hooff ML, Spruit M (in Dutch) en onder redactie van Kolkman M. Pijn hebben mag. Reportage: Leren leven met chronische klachten. Psychologie Magazine (May 2013); p 90-3. Apeldoorn A, Van Hooff M, Ostelo R (in Dutch). De STarT Back Screening Tool. Screeningsinstrument lage rugklachten. FysioPraxis (April 2013); p 32-3. 301 302 Publications Van Hooff ML, De Kleuver M, O’Dowd J, Van Limbeek J. Considerable long-term reduction of pain and healthcare use in Chronic Low back Pain patients after a short pain management program. Clinical evidence for the RealHealthNL program. Spinal News International; Issue 24 (Juli 2012); p14. 303 Theses Sint Maartenskliniek 304 Theses Sint Maartenskliniek Theses Sint Maartenskliniek De Rooij, D. (1988). Clinical and serological studies in the connective tissue diseases. University of Nijmegen, Nijmegen, The Netherlands. Geurts, A. (1992). Central adaptation of postural organization to peripheral sensorimotor impairments. University of Nijmegen, Nijmegen, The Netherlands. Van Lankveld, W. (1993). Coping with chronic stressors of rheumatoid arthritis. University of Nijmegen, Nijmegen, The Netherlands. Tromp, E. (1995). Neglect in action: a neuropsychological exploration of some behavioural aspects of neglect. University of Nijmegen, Nijmegen, The Netherlands. Van Balen, H. (1997). A disability-oriented approach to long-term sequelae following traumatic brain injury. Neuropsychological assessment for post-acute rehabilitation. University of Nijmegen, Nijmegen, The Netherlands. De Kleuver, M. (1998). Triple osteotomy of the pelvis.An anatomical, biomechanical and clinical study. University of Nijmegen, Nijmegen, The Netherlands. Hochstenbach, J. (1999). The cognitive, emotional, and behavioural consequenses of stroke. University of Nijmegen, The Netherlands. Donker, S. (2002). Flexibility of human walking: a study on interlimb coordination. Groningen University, Groningen, The Netherlands. Hendricks, H. (2003). Motor evoked potentials in predicting motor and functional outcome after stroke. University of Nijmegen, Nijmegen, The Netherlands. Hosman, A. J. F. (2003). Idiopathic thoracic spinal deformities and compensatory mechanisms. University of Nijmegen, Nijmegen, The Netherlands. Jongerius, P. (2004). Botulinum toxin type-A to treat drooling. A study in children with cerebral palsy. Radboud University, Nijmegen, The Netherlands. Van de Crommert, H. (2004). Sensory control of gait and its relation to locomotion after a spinal cord injury. Radboud University, Nijmegen, The Netherlands. Van der Linde, H. (2004). Prosthetic prescription in lower limb amputation. Development of a clinical guideline in the Netherlands. Groningen University, Groningen, The Netherlands. De Haart, M. (2005). Recovery of standing balance in patients with a supratentorial stroke. Radboud University, Nijmegen, The Netherlands. Den Otter, R. (2005). The control of gait after stroke: an electromyographic approach to functional recovery. Groningen University, Groningen, The Netherlands. 305 306 Theses Sint Maartenskliniek Weerdesteyn, V. (2005). From the mechanisms of obstacle avoidance towards the prevention of falls. Radboud University, Nijmegen, The Netherlands. Baken, B. (2007). Reflexion on reflexes. Modulation during gait. Radboud University, Nijmegen, The Netherlands. Gaasbeek, R. (2007). High tibial osteotomy. Treatment of varus osteoarthritis of the knee. Radboud University, Nijmegen, The Netherlands. Koëter, S. (2007). Patellar instability. Diagnosis and treatment. Radboud University, Nijmegen, The Netherlands. Langeloo, D. (2007). Monitoring the spinal cord during corrective spinal surgery: a clinical study of TES-MEP. Radboud University, Nijmegen, The Netherlands. Ruiter, M. (2008). Speaking in ellipses. The effect of a compensatory style of speech on functional communication in chronic agrammatism. Radboud University, Nijmegen, The Netherlands. Van den Bemt, B. (2009). Optimizing pharmacotherapy in patients with rheumatoid arthritis: an individualized approach. Radboud University, Nijmegen, The Netherlands. Van Nes, I. (2009). Balance recovery after supratentorial stroke. Influence of hemineglect and the effects of somatosensory stimulation. Radboud University, Nijmegen, The Netherlands. Aarts, P. (2010). Modified constraint-induced movement therapy for children with unilateral spastic cerebral palsy: the Pirate group intervention. Radboud University, Nijmegen, The Netherlands. Groen, B. (2010). Martial arts techniques to reduce fall severity. Radboud University, Nijmegen, The Netherlands. Van Koulil, S. (2010). Tailored cognitive behavioral therapy in fibromyalgia. Radboud University, Nijmegen, The Netherlands. Boelen, D. (2011). Order out of chaos? Assessment and treatment of executive disorders in braininjured patients. Radboud University, Nijmegen, The Netherlands. Heesterbeek, P. (2011). Mind the gaps! Clinical and technical aspects of PCL-retaining total knee replacement with the balanced gap technique. Radboud University, Nijmegen, The Netherlands. Hegeman, J. (2011). Fall risk and medication. New methods for the assessment of risk factors in commonly used medicines. Radboud University, Nijmegen, The Netherlands. Smulders, E. (2011). Falls in rheumatic diseases. Risk factors and preventive strategies in osteoporosis and rheumatoid arthritis. Radboud University, Nijmegen, The Netherlands. Theses Sint Maartenskliniek Snijders, G. (2011). Improving conservative treatment of knee and hip osteoarthritis. Radboud University, Nijmegen, The Netherlands. Vriezekolk, J. (2011). Targeting distress in rheumatic diseases. Utrecht University, Utrecht, The Netherlands. Willems, P. (2011). Decision making in surgical treatment of chronic low back pain. The performance of prognostic tests to select patients for lumbar spinal fusion. Maastricht University, Maastricht, The Netherlands. Beijer, L. (2012). E-learning based speech therapy (EST). Exploring the potentials of E-health for dysarthric speakers. Radboud University, Nijmegen, The Netherlands. Hoogeboom, T. (2012). Tailoring conservative care in osteoarthritis. Maastricht University, Maastricht, The Netherlands. Brinkman, M. (2013). Fixation stability and new surgical concepts of osteotomies around the knee. Utrecht University, Utrecht, The Netherlands. Kwakkenbos, L. (2013). Psychological well-being in systemic sclerosis: Moving forward in assessment and treatment. Radboud University, Nijmegen, The Netherlands. Severens, M. (2013). Towards clinical BCI applications: assistive technology and gait rehabilitation. Radboud University, Nijmegen, The Netherlands. Stukstette, M. (2013). Understanding and treating hand osteoarthritis: a challenge. Utrecht University, Utrecht, The Netherlands. Van der Maas, A. (2013). Dose reduction of TNF blockers in Rheumatoid Arthritis: clinical and pharmacological aspects. Radboud University, Nijmegen, The Netherlands. Zedlitz, A. (2013). Brittle brain power. Post-stroke fatigue, explorations into assessment and treatment. Radboud University, Nijmegen, The Netherlands. Koenraadt, K. (2014). Shedding light on cortical control of movement. Radboud University, Nijmegen, The Netherlands. Smink, A. (2014). Beating Osteoarthritis. Implementation of a stepped care strategy to manage hip or knee osteoarthritis in clinical practice. VU University Amsterdam, Amsterdam, The Netherlands. Stolwijk, N. (2014). Feet 4 feet. Plantar pressure and kinematics of the healthy and painful foot. Radboud University, Nijmegen, The Netherlands. Van Kessel, M. (2014). Nothing left? How to keep on the right track. Spatial and non-spatial attention processes in neglect after stroke. Radboud University, Nijmegen, The Netherlands. 307 308 Theses Sint Maartenskliniek Altmann, V. (2015). Impact of trunk impairment on activity limitation with a focus on wheelchair rugby. Leuven University, Leuven, Belgium. Bevers, K. (2015). Pathophysiologic and prognostic value of ultrasonography in knee osteoartrhitis. Utrecht University, Utrecht, The Netherlands. Cuperus, N. (2015). Strategies to improve non-pharmacological care in generalized osteoarthritis. Radboud University, Nijmegen, The Netherlands. Kilkens, A. (2015). De ontwikkeling en evaluatie van het Communicatie Assessment & Interventie Systeem (CAIS) voor het aanleren van (proto-)imperatief gedrag aan kinderen met complexe ontwikkelingsproblemen. Radboud University, Nijmegen, The Netherlands. Penning, L. (2015). The effectiveness of injections in cuffdisorders and improvement of diagnostics. Maastricht University, Maastricht, The Netherlands. Stegeman, M. (2015). Fusion of the tarsal joints: outcome, diagnostics and management of patient expectations. Utrecht University, Utrecht, The Netherlands. Van Herwaarden, N. (2015). Individualised biological treatment in rheumatoid arthritis. Utrecht University, Utrecht, The Netherlands. Wiegant, K. (2015). Uitstel kunstknie door kniedistractie. Utrecht University, Utrecht, The Netherlands. Willems, L. (2015). Non-pharmacological care for patients with systemic sclerosis. Radboud University, Nijmegen, The Netherlands. Witteveen, A. (2015). The conservative treatment of ankle osteoarthritis. University of Amsterdam, Amsterdam, The Netherlands. Zwikker, H. (2015). All about beliefs. Exploring and intervening on beliefs about medication to improve adherence in patients with rheumatoid arthritis. Radboud University, Nijmegen, The Netherlands. Luites, J. (2016). Innovations in femoral tunnel positioning for anatomical ACL reconstruction. Radboud University, Nijmegen, The Netherlands. Pakvis, D. (2016). Survival, primary stability and bone remodeling assessment of cementless sockets. An appraisal of Wolff’s law in the acetabulum. Radboud University, Nijmegen, The Netherlands. Lesuis, N. (2016). Quality of care in rheumatology. Translating evidence into practice. Radboud University, Nijmegen, The Netherlands. Schoenmakers, K. (2016). Prolongation of regional anesthesia. Determinants of peripheral nerve block duration. Radboud University, Nijmegen, The Netherlands. 309 310 Scan the QR code to view this thesis online. For more information about this topic, go to: www.maartenskliniek.nl/spinesymposium Stellingen 1. Het triëren en juist toewijzen van chronische lage rugpijn patiënten voor de juiste behandeling leidt tot betere uitkomsten. (dit proefschrift) 2. De drempelwaarde voor een succesvolle behandeluitkomst voor chronische lage rugpijn komt overeen met de mate van functioneren van een gezonde populatie. (dit proefschrift) 3. De Nijmegen Decision Tool for Chronic Low Back Pain is volledig gebaseerd op het biopsychosociaal model, een unicum binnen de orthopedie alwaar het biomedisch model dominant is. (dit proefschrift) 4. Een uitkomstenregister, opgezet conform observationele onderzoeksmethoden, is de basis voor evaluatie van zorg, het vergelijken van interventies en value-based health care. (dit proefschrift) 5. Het concept van op behandeluitkomsten gebaseerde prognostische patiënt profielen bij chronische lage rugpijn, leidt tot een paradigmaverschuiving in de moderne geneeskunde. (dit proefschrift) 6. De onderzoeksvraag en daarmee het onderwerp van studie, is belangrijker dan de onderzoeksmethode zelf. (opinie, gebaseerd op werk van Olli S. Miettinen, 1936- ) 7. Afhankelijk van de vraagstelling heeft observationeel onderzoek een duidelijke meerwaarde boven experimenteel onderzoek. (opinie) 8. Om interventies voor een specifieke doelgroep met elkaar te kunnen vergelijken zijn eenduidige en alom geaccepteerde definities van ‘succes’ en ‘falen’ onontbeerlijk. (opinie) 9. Absolute maten voor behandelsucces zijn te prefereren boven relatieve maten, aangezien: ‘It’s good to feel better but it’s better to feel good. (Dougados, J.Rheumatol. 2005)’. (opinie) 10. A-specifieke lage rugpijn bestaat niet. (opinie) 11. It’s more important to know what sort of a person has a disease than to know what sort of a disease a person has (Hippocrates, 460-377 BC). 12. The difficulty lies not so much in developing new ideas as in escaping from the old ones (John Maynard Keynes,1883-1946). 13. De maatschappelijke impact van research heeft meer impact dan de hoogte van de impact factor. (eigen ervaring) 14. Music is part of being human (Oliver Sacks, 1933-2015).