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SYMPTOM BURDEN AND ITS RELATIONSHIP TO FUNCTIONAL STATUS IN THE CHRONICALLY CRITICALLY ILL by CLAREEN WIENCEK Submitted in partial fulfillment of the requirements For the degree of Doctor of Philosophy Dissertation Adviser: Dr. Barbara Daly Frances Payne Bolton School of Nursing CASE WESTERN RESERVE UNIVERSITY May, 2008 ii CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Clareen Wiencek___________________________ Candidate for the PhD____________degree*. (signed) Dr. Barbara Daly____________________ Dr. Patricia Higgins__________________ Dr. Elizabeth Madigan________________ Dr. Rana Hejal______________________ (date) March 10, 2008_______ *We also certify that written approval has been obtained for any proprietary material contained therein. iii Dedication I dedicate my dissertation and doctoral journey to my parents, Vincent A. and Josephine D. Aufderheide. Forged by the Great Depression and World War II, this physician and nurse instilled in me and all their children the value of hard work and perseverance. Their love and influence shaped my professional calling and nurtured a life-long love of learning and education. This is my gift to them. iv Table of Contents Page Title Page………………………………………………………………… i Committee Signature Sheet……………………………………………… ii Dedication Page………………………………………………………….. iii Table of Contents………………………………………………………... iv List of Tables………………………………………………………........... vii List of Figures……………………………………………………………. viii Acknowledgements……………………………………………………… ix Abstract………………………………………………………………….. x Chapter I: Background and Significance………………………………… 1 Significance………………………………………………………. 3 Conceptual framework and conceptual definitions………………. 6 Research questions……………………………………………….. 15 Chapter II: Literature Review……………………………………………. 19 Problem and significance………………………………………… 19 Chronic critical illness……………………………………………. 21 Concept of symptom burden……………………………………... 23 Symptom burden in the chronically critically ill (CCI) …………. 30 Critical attributes of symptom burden…………………………… 32 Symptom burden and functional status…………………………... 49 Symptom burden and functional status in the CCI………………. 54 Chapter III: Methods……………………………………………………. 57 v Secondary analysis………………………………………………. 57 Sample and setting………………………………………………. 58 Instruments……………………………………………………… 62 Procedure………………………………………………………... 68 Analysis………………………………………………………….. 69 Chapter IV: Results……………………………………………………… 71 Description of the sample……………………………………….. 72 Symptom prevalence…………………………………………….. 80 Symptom burden………………………………………………… 83 Relationship between symptom burden and functional status….. 88 Criterion-related validity of the single-item question…………… 98 Additional exploratory analysis…………………………………. 101 Chapter V: Discussion…………………………………………………… 107 Major findings…………………………………………………… 107 Sample description………………………………………………. 108 Symptom prevalence…………………………………………….. 114 Symptom burden………………………………………………… 117 Relationship of symptom burden to functional status…………… 123 Validity of the single-item scale…………………………………. 127 Limitations……………………………………………………….. 130 Implications for nursing practice………………………………… 135 Recommendations for future research…………………………… 136 Summary………………………………………………………… 138 vi Appendix I: Open-ended question……………………………………….. 141 Appendix II: SF-8………………………………………………………... 142 Appendix III: OASIS…………………………………………………….. 144 Appendix IV: Enrollment form…………………………………………… 148 Bibliography………………………………………………………………. 155 vii List of Tables Page Table 1. Parent Study Variables and Time Points 60 Table 2. Comparison of Demographics and Key Variables among 77 the Total Sample, the Patient Self-Responder Group and the Proxy Responder Group Table 3. Disposition of Patient Self-responder and Proxy Responder Group 80 Table 4. Symptom Categories and Prevalence 84 Table 5. Symptom Burden of the Total Sample, Patient Self-Responders 86 and Proxy Responder Group Table 6. Prevalence of Symptoms in Patient and Proxy Responder Group 87 Table 7. Correlation between Independent Variables and Functional Status 92 at 2 Months Table 8. Coefficients for Final Regression Model with 2 month 95 Functional Status as the Dependent Variable Table 9. Regression Model Summary 95 Table 10. Logistic Regression Results 98 Table 11. Agreement between Three Symptom Categories and Three 100 Corresponding Items of the SF-8 (Kappa) Table 12. Correlation between Symptom Burden and the SF-8 101 Table 13. Differences in Key Variables between Subjects with Low 104 and High Symptom Burden viii List of Figures Page Figure 1. Conceptual Framework 9 Figure 2. Study Model 17 Figure 3. Sample Distribution 75 Figure 4. 2 month Functional Status of Patient Self-responders 90 Figure 5. 2 Month Functional Status in the Proxy Responder Group 90 ix Acknowledgements I believe that a journey of this magnitude is never made alone. I was so fortunate to have the support of family and friends and the guidance of gifted faculty members. In the middle of my doctoral process, I successfully climbed to the 19, 340 foot summit of Mt. Kilimanjaro on the African continent. I learned invaluable lessons from both journeys - that once on the path there was no turning back and that I could only reach the summit because of the outstretched hands helping me every step of the way. Below, I acknowledge each unique contribution of those outstretched hands, in no particular order of importance, as all were essential to my success. Dr. Barb Daly: My committee chair, adviser, and mentor. I am honored to work with a woman of her brilliance, balance and accomplishments. She, more than anyone in my nursing career, has shaped my vision and my practice. I am forever indebted to her. Dr. Patricia Higgins: Committee member and teacher who taught me early and taught me well the importance of concepts and theories. Dr. Liz Madigan: Committee member and teacher who taught me everything I know about statistics and then some. Dr. Rana Hejal: Committee member and exemplary clinical role model for the comprehensive care of this difficult population Ron Hickman: My fellow doctoral student and friend who walked with me in the final steps as we both dealt with the details and deadlines. His support and smile made the end of the journey manageable. Maryjo Prince-Paul: My fellow student and friend who started the journey with me and whose outstretched hand pulled me along when most needed. She will always be intricately woven into the fabric of my PhD. Maryjo and Ron: I will so enjoy seeing your stars shine and your research careers soar. Helen Foley: My colleague and dear friend. She was there for the late afternoon tears, the deadlines, and the self-absorption and kept me balanced when all was out of balance. She was the rock I came to depend on. To my husband and children, Jim, Jimmy and Julie: Their love and tolerance of yet another family opportunity missed or delayed was essential. They gave me the space when I needed it and the love when I needed it most. x Symptom Burden and Its Relationship to Functional Status in the Chronically Critically Ill Abstract by CLAREEN WIENCEK The underlying premise of this study was that the prolonged nature of chronic critical illness is associated with multiple, influencing factors that cause a diffuse, nonspecific symptom experience that is difficult to assess and treat. Thus, the purpose of this study was to gain a better understanding of symptom burden and its relationship to functional status in the post-discharge experience of the chronically critically ill (CCI) with the ultimate goal of improving palliative care for a population with exceptionally poor outcomes. A descriptive, predictive design was used in this secondary analysis of a preexisting data base in the CCI. Responses to the question, “what symptoms are most bothersome at this time?” were analyzed to determine symptom burden. This question allowed patients and their proxies to use their “own words” in reporting bothersome symptoms. From the patients in the parent study who received a 2 month disease management intervention, 56% (129) were able to self-report symptoms, 28% (65) required proxy reporting of symptoms, and 16% (37) had no symptom reports available. Overall, symptom prevalence was high as 95% reported one or more symptom. Eleven categories of symptoms emerged from analysis of the raw data. Pain was the most xi prevalent in both the patient self-responder and the proxy responder group. Generally, patients self reported burden related to physical symptoms such as pain, fatigue, and respiratory discomfort as compared to proxies who perceived burden in the CCI patient due to cognitive impairment, communication distress, and loss of independence. The model of symptom burden, when controlling for age, gender, ethnicity, number of preexisting conditions, preadmission health status and length of stay, predicted 18.3% of the variance in functional status at 2 months (adj R2 = ,18l F = 4.26; p = < .001). Symptom burden did not make a significant contribution to the model. Study limitations included selection bias and effects of proxy reporting on reliability and validity of study findings. More study is needed to identify the degree and meaning of symptom burden in the CCI and its relationship to functional status and other outcomes in order to facilitate effective management and comfort. 1 Chapter I Background and Significance The poor outcomes and high resource use in the chronically critically ill (CCI) are well known (Carson & Bach, 2002; Daly, Douglas, Genet Kelley, O’Toole, & Montenegro, 2005; Nelson, 2002) but the symptom burden of these patients and its impact on functional status, quality of life or other important outcomes has been largely unexplored. Chronic critical illness as distinguished from critical illness is a complex syndrome of physiologic abnormalities, neuroendocrine and immunologic dysfunction, prolonged medical and nursing dependence, and uncertain trajectory associated with a high risk of disability, distress and death. Multiple factors contribute to the symptoms associated with the syndrome (Nelson, 2002; Nelson, Meier, Litke, Natale, Siegel, & Morrison, 2004) but how these symptoms are perceived, reported, assessed, or managed or whether the effects are cumulative, distressing or burdensome is poorly understood. If most of the outcomes of chronic critical illness cannot be altered, as suggested by Daly and colleagues (2005), then it is essential that a better understanding of the symptom burden of chronic critical illness be elucidated with the ultimate goals of optimizing palliative care, minimizing suffering for the patient, and facilitating goal setting with the patient and family. Symptom experience has been examined in other populations and major symptom models exist, yet, the field of symptom research is still developing (Barsevick, Whitmer, Nail, Beck, & Dudley, 2006; Dodd et al 2001). Consequently, there are no universal definitions or criteria that guide the clinical assessment and measurement of multiple, 2 multidimensional symptoms or the perceived distress or burden to patients. Though two recent studies of the symptom burden in the CCI have been reported (Nelson et al., 2004; Nelson, Tandon, Mercado, Cambi, Ely, & Morrison, 2006) more study is needed to fully explain the symptom burden and its impact on outcomes. There are multiple challenges inherent in studying symptoms in the CCI. First, many CCI patients are cognitively impaired as a result of the primary illness or injury, prolonged illness, and/or medication or sedation limiting the ability to self-report. Cognitive dysfunction has been reported as high as 34% (Douglas, Daly, Brennan, Gordon, & Montenegro, 2005) and 50 % (Nelson, et al., 2006). Second, even if these patients are cognitively intact, they experience considerable communication barriers posed by endotracheal and tracheostomy tubes, mechanical ventilators, and impaired writing ability secondary to edema or profound weakness. Third, the prolonged nature of the syndrome and typically long intensive care unit (ICU) stays, reported to range from 15.9 days (Daly et al., 2005) to 24.5 days (Heyland, Konopad, Noseworthy, Johnston, & Gafni, 1998) predispose these patients to a symptom experience that is protracted. Lastly, chronic critical illness is not simply an extension of critical illness but a syndrome of major metabolic abnormalities (Nelson et al., 2004; van den Berghe, 2002) compounded by environmental and technology related factors specific to the ICU setting. The accumulation of symptoms resulting from such technology and the myriad of physiologic, psychologic, and/or situational influencing factors sets the foundation for a symptom burden that could be among the highest of any patient population in the acute or post-acute setting. These patients are recognized to be at high risk for substandard symptom control (Nelson et al., 2004) but evidence remains limited. Therefore, the 3 purpose of this study was to gain a better understanding of symptom burden in the CCI and its relationship to functional status in the post-hospital discharge phase. Significance Despite 20 years of research since Girard and Raffin (1985) first mentioned the CCI, there remains no universally accepted definition of this patient population (Carson, 2006; Carson & Bach, 2002; Douglas and Daly, 2003; Nierman, 2002). This poses limitations to early detection and effective management in the clinical setting and to the interpretation and generalizability of research findings. However, it is widely recognized that the CCI are those patients who survive the initial life-threatening event but continue to need prolonged medical and nursing care in the ICU for weeks to months (Carson & Bach, 2002; Daly et al., 2005; Nelson et al., 2004). These patients usually require prolonged mechanical ventilation (PMV), a hallmark of the syndrome (Nierman, 2002) but there is no generally accepted definition of PMV. The mean number of ventilator days for all patients in the ICU is 4.7 days (Carson & Bach, 2002) but many different criteria are used to designate PMV. Major investigators use mechanical ventilation greater than 72 hours, Diagnostic Related Group (DRG) 483, tracheostomy for conditions other than face, mouth, and neck diagnoses, or the Health Care Financing Administration (HCFA) criterion of PMV greater than 21 days (Carson, 2006). A recent consensus conference recommended the use of 21 days as the accepted marker of PMV (MacIntyre, Epstein, Carson, Scheinhorn, Christopher, & Muldoon, 2005). Though length of ventilatory support varies in the CCI, the need for a ventilator, at some point, is the only universal feature of the syndrome of chronic critical illness. Except for this shared feature, there is no common disease or single event that predicts who will become CCI 4 and there is no common path of recovery or progression (Carson, 2006; Carson & Bach, 2002). Outcomes are poor in the CCI. Multiple studies have documented high mortality rates, poor functional status, high readmission rates, reduced quality of life, and high caregiver burden and depression (Carson, Bach, Brzozowski, & Leff, 1999; Chelluri et al., 2004; Daly et al., 2005; Douglas et al., 2001; Douglas & Daly, 2003). Despite resource intensive care and major intervention studies to understand or effect the course of chronic critical illness (Daly et al., 2005; Daly, Phelps, & Rudy, 1991; Nasraway, Button, Rand, Hudson-Jinks, & Gustafson, 2000) outcomes remain generally unchanged. Evidence is needed to guide more effective management of the clinical course especially palliative care for symptom relief. This study was important to nursing for several reasons. First, ample evidence exists that critically ill patients experience a multitude of distressing symptoms and that treatment effectiveness varies (Desbiens et al., 1999; Desbiens et al., 1996; Nelson, 2002; Nelson et al., 2004; Puntillo et al., 2001). Second, the CCI are a complex, heterogeneous, highly prevalent, and resource-intensive population requiring prolonged care in the ICU whose incidence is projected to increase (Carson, 2006; Carson & Bach, 2002). Third, while the poor outcomes of this population have been well documented, there is a dearth of empirical data about the symptom experience of the CCI. Finally, the results of this study would inform the nursing discipline of the prevalence and nature of the symptoms experienced by the CCI enabling nurses to more effectively manage symptoms. Since symptom assessment and management are primarily a nursing responsibility, the knowledge gained by this inquiry would enable critical care nurses to contribute to the 5 quality of care of the CCI, including both measures to promote functional recovery of some and support of a peaceful and comfortable death for others. In addition to clinical relevance, this study had significant potential for future research. Evidence about overall symptom burden and the most bothersome symptoms could be used to design intervention studies testing the efficacy of assessment and screening tools and treatment algorithms. Additionally, the National Institute of Nursing Research has identified symptom management as a research priority (http://www.ninr.nih.gov.). Study of the symptom burden of the CCI was critical to the health care system as well. The economic implications of caring for these complex patients cannot be overstated. Patients who are chronically critically ill comprise 5-10% of the ICU population and consume a disproportionate 30-50% of ICU resources. The annual cost of caring for the CCI is estimated up to $24 billion in ICU costs per year (Carson & Bach, 2002) while the incidence is increasing (Carson, 2006). The diagnostic group of prolonged mechanical ventilation (PMV), a frequently used marker of chronic critical illness, ranks third in total charges ($5 billion in 2005) and first in charges per patient (Carson, 2006). It has been projected that services provided in the ICU account for 20% of inpatient costs in American hospitals or 0.9% of the annual gross domestic product (Jacobs & Noseworthy, 1990). Outcome prediction remains rudimentary for CCI patients (Carson & Bach, 2002) but multiple authors have identified advanced age and poor premorbid functional status as strong predictors of mortality in this group. Hospital mortality rates of 47.4% (Douglas et al., 2001) and cumulative 1-year mortality rates ranging from 44-68% (Combes, Costa, 6 Trouvillet et al., 2003; Douglas et al., 2002; Carson et al., 1999; Chelluri et al., 2004) have been reported. Mean length of stay (LOS) for ICU patients is 4.7 days which is a statistic that has held steady even as hospital stays for the critically ill decreased from 14.8 to 11.8 days (Rosenberg, Zimmerman, Alzola, Draper, & Knaus, 2000). The average hospital LOS for the CCI has decreased from 53 days in 1987 (Spicher & White) to 21.9 days, recently reported by Daly and colleagues (2005). Also, economic burden is due to the high risk of readmission and high post-discharge costs of caring for the CCI (Douglas et al, 2002). Readmission rates as high as 40% in the first 6-month post-hospital discharge period have been reported (Douglas et al., 2001; Nasraway et al., 2000). A recently completed disease management intervention for the CCI noted a 2 month readmission rate of 39.5% in 334 randomized patients who survived to discharge (Daly et al., 2005). This economic burden is essentially unchanged though the care setting for the CCI now also includes long-term acute care hospitals (LTACs) or regional weaning centers. LTACs are a common care setting for the CCI and have proliferated in number since 1992 when HCFA allowed care provision for PMV patients outside the prospective payment system (PPS). Now under a PPS, LTACs continue to be a primary post-acute care setting for these patients prolonging exposure to an intensive care like environment. Early transfer of the CCI from the ICU to LTAC can save costs for hospitals and third party payers but higher overall mortality associated with LTAC transfer and 1 year mortality rates of 24-77% have been reported (Carson, 2006). This finding would suggest that the particular care setting does not impact overall outcomes in the CCI. Conceptual Framework and Conceptual Definitions 7 Framework Two existing symptom models inform this investigation into the symptom burden of the CCI. The Revised Symptom Management Conceptual Model proposed by Dodd and colleagues (2001) is comprised of the 3 interrelated dimensions of the symptom experience, symptom management strategies and outcomes. According to the model developers (Dodd et al., 2001), effective management of symptoms requires attention to all three dimensions. The symptom experience encompasses a feedback loop of the individual’s perception of a symptom, evaluation of what that symptom means, and the response to the symptom. A potential limitation is its application to cognitively impaired patients whose perception of the symptom and attached meaning is often unknown. Dodd’s model assumes that nonverbal patients may experience symptoms and the interpretation by the caregiver is a valid basis for intervention, an assumption shared by this investigator. The second model, the middle- range theory of unpleasant symptoms (Lenz, Pugh, Milligan, Gift, & Suppe, 1997) depicts influencing factors, the unpleasant symptom(s) and the performance or outcome of the symptom experience. The theory proposes that some of the same factors may influence different symptoms and interventions should be aimed at not only alleviating the symptoms but also altering factors that influence the symptoms. The theory of unpleasant symptoms (TUS) describes the presence of multiple symptoms with multidimensional relationships that are multiplicative not additive. The TUS was originally developed from three research programs, fatigue during postpartum and intrapartum and fatigue and dyspnea in persons with chronic obstructive pulmonary disease (COPD) (Lenz, Suppe, Gift, Pugh, & 8 Milligan, 1995). Subsequently, it has been tested in persons with Alzheimer’s Disease, cardiac patients, and COPD (Lenz et al., 1997). Common features are shared among the CCI and persons with these disease processes. Major premises underlying the updated TUS include multidimensionality in both concepts and measurement, overlapping conceptualizations of unpleasant symptoms, overlap in the influencing factors, and bidirectional feedback loops between the influencing factors, the unpleasant symptoms and the outcome of the experience. Though both symptom models include the components of symptom(s) and outcomes, the Dodd model stresses management of symptoms and the Lenz model explains the interplay among the influencing factors, symptom expression and outcomes. Since the TUS focuses attention on multiple, interacting symptoms and influencing factors and since the underlying assumptions in the Lenz’ model of multidimensionality and multiplicative relationships are believed to contribute to symptom burden in the CCI, this study’s conceptual framework is derived from the TUS. Due to the complexity and prolonged nature of the syndrome of chronic critical illness and much that is still unknown, it is valid to assume that symptoms overlap and that multiple factors influence the symptom experience. This prolonged and diffuse symptom experience is believed to contribute to the symptom burden in the CCI. The conceptual framework of symptom burden, the influencing factors, and relationship to functional status is presented in Figure 1. In addition to the above assumptions based on existing symptom research, this model has a strong empirical base. Accordingly, assumptions underlying this model are that some degree of predictability 9 exists with physical and psychological phenomena and that human responses to health and illness can be identified, measured and understood. One of the purposes of Influencing Factors Physiologic Psychologic Situational Symptom Burden Functional status Figure 1. Conceptual Framework. undertaking this investigation was to develop a basis for effective symptom management in the CCI through a better understanding of symptom burden. Though preliminary, evidence does exist to support the concept of symptom burden in the CCI. In the one study of symptoms in the CCI, Nelson and colleagues (2004) found that symptom distress is common and severe. Ninety percent of these patients were symptomatic, reporting an average of 8.6 symptoms per subject; 44% reported pain at the highest levels of distress, 60% admitted to dyspnea, and 80% reported unsatisfied thirst. Multiple investigators of the general ICU population have reported significant levels of pain, dyspnea, psychological distress, depression, thirst, fatigue, and delirium (Desbiens et al., 1999; Desbiens et al., 1996; Nelson et al., 2001; Puntillo, 1990; Puntillo et al., 2001). A recent study (Nelson et al, 2006) reported that 30% and 50% of chronically critically ill patients were comatose or delirious, respectively, adding to symptom burden. Anecdotal evidence suggests that the incidence of cognitive impairment is increasing in this population (J. Nelson, personal 10 communication, February 23, 2007). Preliminary findings from an intensive communication intervention study of the CCI show that 82.5% of patients ventilated greater than 72 hours do not possess decision making capacity (Daly, 2005). More evidence is needed to explain the symptom burden in the chronically critically ill. Research findings support the premise that there are multiple physiologic, psychologic and situational influencing factors contributing to symptoms in the critically ill and the CCI. These patients suffer considerable physiologic stress that promotes protein catabolism and the release of proinflammatory cytokines causing nitrogen wasting, muscle wasting and weakness, edema, and a persistent systemic inflammatory response (Epstein and Breslow, 1999). The symptom burden created by this particular aspect of chronic critical illness is unknown. However, evidence does exist to support the physiological influences from the underlying disease pathology, pre-morbid conditions, or procedure-related pain on symptoms (Nelson et al., 2004; Puntillo et al., 2001). Patients with underlying co-morbidities who experience acute, life threatening illness are at the greatest risk of becoming chronically critically ill (Carson & Bach, 2002). There are also psychological factors that influence symptoms, such as the person’s mood, reaction to the illness state, or knowledge about the symptoms and their possible meaning. Depression is highly prevalent during chronic critical illness (Douglas et al., 2002; Nelson et al., 2004). Empirical data supports the relationship between depression and pain in similar patient populations, such as heart failure (Joynt, Whellan & O’Connor, 2004; Block, 2000; Gallagher, 1999). The Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment (SUPPORT) provided evidence of 11 the relationship between pain, anxiety and depression in seriously ill adults (Desbiens et al., 1996). Situational factors that influence symptoms include the social and physical environment, such as noise, light, care routines and social support. Some evidence exists to support the positive influence of social support on the experience of pain in the heart failure population. Similarities shared by the CCI and heart failure patients include complex physiologic derangements and associated unpleasant symptoms, high mortality and morbidity, poor functional status, depression and poor quality of life (Stewart et al., 1989). Environmental factors, such as noise, light and care routines cause sleep disturbances, another burdensome symptom for patients exposed to lengthy stays in intensive care like environments (Cooper, Gabor, & Hanly, 2001; Cooper, Thornley, Young, Slutsky, Stewart, & Hanly, 2000). As conceptualized by Lenz and colleagues (1997), outcomes of the symptom experience are functional and cognitive abilities. Functional abilities may include physical activity, activities of daily living, social interaction, liberation from mechanical ventilation and role performance such as return to work. Cognitive abilities may include problem-solving, thinking, concentration, and interaction with people and things in the environment. In persons with lung cancer, chronic back pain, angina and stroke, symptom expression has been found to negatively influence functional status (Cutler, Fishbain, Steele-Rosomoff, & Rosomoff, 2003; Kurtz, Kurtz, Stommel, Given, & Given, 2000; Widar, Ahlstrom, & Elk, 2004). The impact of functional impairment on overall quality of life in CCI patients has been investigated. In a comparison of short-term and long-term ventilator patients 12 (n=538), long-term patients requiring greater than 96 hours of ventilation were more likely to be discharged to a nursing home and experienced lower quality of life (QOL) up to one year post-discharge (Carson & Bach, 2001). Similarly, more than 74.8 % of CCI patients needed caregiver support at 2 months (Spicher & White, 1987) and half needed assistance at 1 year (Carson et al., 1999). Only 4-33% of CCI patients had the functional ability to allow return to the home setting (Chelluri et al., 2004; Nasraway et al., 2000; Nelson et al., 2004). Thirty-two percent of CCI patients discharged from a regional weaning center were dependent in all activities of daily living at 3 months and only half were liberated from the mechanical ventilator (Nelson et al., 2004). Studies have shown that the majority (86-89%) of CCI patients are living at home prior to the acute event (Daly et al., 2005; Nelson et al., 2006; Scheinhorn et al., 2007) underscoring the major decline in functional status experienced by survivors of chronic critical illness. Conceptual Definitions For the purpose of this inquiry, chronic critical illness, symptom burden, and functional status were defined. Chronic critical illness is a discrete syndrome resulting from abnormalities in organ function, metabolism, and neuroendocrine function (Carson & Bach, 2002; Nelson et al., 2006) associated with persistent organ and system dysfunction or failure necessitating prolonged medical and nursing care. There is no common etiology or primary insult and no consistently predictable disease trajectory, though the need for PMV is a hallmark of the syndrome. While discrete, the syndrome results from the overlap between critical and chronic illness that is an unintended consequence of modern medical technology (Nierman, 2002). The initial goal of critical care, to treat life-threatening illness, has been blurred by the use of such technology. 13 Now, critical illness, chronic illness, and terminal illness exist together on a daily continuum in the ICU setting. The chronically critically ill are those patients who suffer from this syndrome and who survive a life-threatening illness but require prolonged intense medical and nursing care and meet the following additional criteria: episode of mechanical ventilation greater than 72 hours, no history of long-term mechanical ventilation prior to ICU admission, and survive to be discharged from the ICU. Symptom burden is derived from widely recognized definitions of symptom. Symptom has been defined as “subjective evidence of disease or physical disturbance” (Websters 9th, 1991) or the subjective perception of illness or disease (Walke et al., 2004). Most investigators accept a definition of symptom as a subjective experience with a negative connotation requiring perception by the individual and evaluation and response to the meaning of the symptom (Dodd et al., 2001). Symptom burden is frequently used synonymously with symptom distress especially in operational definitions or research findings. Symptom distress is defined as how much the person is bothered by the unpleasant feeling or the degree or amount of physical or mental upset, anguish, or suffering experienced from a specific symptom (Armstrong, 2003; Rhodes & Watson, 1987). It is the affective nature of the symptom and most contributes to quality of life due to the meaning attached (Lenz et al., 1997). Though accepted as one of the four dimensions of the symptom experience, distress does not capture the cumulative and nondiscrete features of the burden resulting from one symptom or multiple symptoms of varying severity that coexist for a longer duration. The prolonged exposure to factors that can influence symptoms in the CCI and the multiple factors that might precipitate symptom expression are hypothesized as important aspects of symptom burden. Thus, 14 symptom burden, as the focus of this investigation, was defined as the distress or bother resulting from one or more symptoms, in isolation or with a cumulative effect, as perceived by the patient and reported by the patient or significant other. Functional status is an important determinant of health related quality of life (Kurtz et al., 2000). Also, independence in functional status is related, to a large extent, to cognitive ability. In one study of acceptable medical outcomes, patients reported that cognitive impairment was even less desirable than death (Fried, Bradley, Towle, & Allore, 2002). As mentioned previously, cognitive impairment is a common consequence of chronic critical illness. Thus, functional status, a result of both physical and cognitive abilities, was defined as the level of independence in activities of daily living (ADL). In summary, symptom burden in the CCI is expected to be high and to be influenced by multiple physiologic, psychologic, and situational factors. While isolating etiologies of separate symptoms may facilitate management, focusing on one influencing factor, etiology or isolated symptom in the face of the diffuse and complex nature of chronic critical illness may not be useful at this time. After 20 years of study, the precise cause or moment when critical illness becomes chronic remains unknown (Carson & Bach, 2002; Nelson et al., 2004). Additionally, most CCI patients plateau at a level of persistent organ dysfunction, such as ventilator dependence or cognitive impairment, that leads to a totally dependent state combined with multiple and repeated complications. This prolonged state of chronic critical illness appears to be associated with a significant symptom burden (Nelson et al., 2004; Nelson et al., 2006). Thus, more investigation is needed to more fully explain the burden in order to improve management, reduce suffering, and improve outcomes even if the outcome is a peaceful and pain-free death. 15 Research Questions While the symptom burden in the CCI is understudied, preliminary work suggests that symptom distress is common and severe (Nelson et al., 2004). Particularly concerning, the impact of the symptom burden on outcomes is unknown. It is known, however, that functional status declines substantially over the course of chronic critical illness. Thus, identifying predictors of functional status, an essential component of quality of life (Kurtz et al., 2000), and addressing in-hospital factors, particularly symptom burden, that may impact functional status is essential. The specific aims of this study were (1) to examine the symptom burden of CCI patients enrolled in a disease management program, and (2) to analyze the relationship between symptom burden and functional status through a secondary analysis of an existing data base. The study model found in Figure 2 explains the specific variables of symptom burden and functional status, covariates, and relationships that were examined. The relationship between symptom burden and functional status, while controlling for age, gender, ethnicity, length of stay, number of preexisting conditions, and preadmission health status, was analyzed using multiple regression. The literature suggests that these variables have physiological, psychological and situational influences on symptom expression and could influence the number, duration, and distress associated with symptoms in the CCI. Thus, the research questions were: 1. What is the symptom burden in the CCI as measured by the most prevalent symptoms and number of symptoms in response to the open-ended question, “what symptoms are most bothersome at this time?” 16 2. To what extent does symptom burden upon discharge from the index hospitalization predict functional status at 2 months after discharge, when controlling for age, gender, ethnicity, length of stay, number of preexisting conditions, and preadmission health status? 3. What is the criterion-related validity of the open-ended question measuring symptom burden as compared to the Medical Outcomes Study Short Form (SF-8): a. What is the degree of agreement between responses to the open-ended question and the SF-8 question, “how much bodily pain have you had during the past 24 hours?” b. What is the degree of agreement between responses to the open-ended question and the SF-8 question, “during the past 24 hours, how much energy did you have?” c. What is the degree of agreement between responses to the open-ended question and the SF-8 question, “during the past 24 hours, how much have you been bothered by emotional problems (such as feeling anxious, depressed or irritable?” Summary An unintended consequence of modern technology is the syndrome of chronic critical illness. The patients who suffer from this syndrome experience prolonged organ and system dysfunction, multiple physiologic and neuroendocrine abnormalities, prolonged medical and nursing dependence, and poor outcomes. Given the prolonged 17 Influencing Factors: Covariates Age Gender Ethnicity # of preexisting conditions Preadmission health status Length of hospital stay SYMPTOM BURDEN Open‐ended question FUNCTIONAL STATUS OASIS Figure 2. Study Model. nature and resource-intensiveness of the syndrome of chronic critical illness, it is important to understand the cumulative effects on symptom burden and the effect of this burden on functional status. Since chronic critical illness is not acknowledged as a terminal illness that might benefit from fully integrated palliative care and since these patients are usually too debilitated, ill, or cognitively impaired to participate fully in symptom management, clinical efforts to alleviate symptoms should take precedence (Danis, 2004). End-of-life research has focused on the terminally ill or those with life-limiting disease. The CCI have not benefited from this research and like other chronic diseases are underrepresented (Klinkenberg, 2004). Also, the prolonged nature of chronic critical illness may cause a diffuse, nonspecific symptom experience in which there is little specificity or clarity to direct effective management. It may be difficult for the patient or caregiver to 18 differentiate which symptoms are most bothersome and which are most prevalent. Therefore, the patient’s response to a simple and single question may show benefit to focus management and palliative care. Thus, it is essential that research be conducted to explain the symptom burden in the CCI and to explain the relationship between symptom burden and functional status. It was the goal of this investigation to provide evidence on which to base effective interventions and alleviation of suffering for these patients, a majority of whom will die within 6 months to 1 year of the initial life-threatening event. 19 Chapter II Literature Review In this chapter, data based articles with theoretical, quantitative or qualitative approaches will be reviewed to support conceptual definitions and the proposed relationship between symptom burden and functional status. The problem and significance will be restated and then review articles or empirical data explaining the syndrome of chronic critical illness discussed. Empirical support for the concept of symptom burden will be synthesized. Next, the physiologic, psychologic and situational factors that influence symptom burden in the chronically critically ill (CCI) and similar populations such as the critically ill will be reviewed. Qualitative or quantitative approaches to examining the relationship between symptom burden and functional status, the most hypothetical aspect of the conceptual model, will be examined in similar patient populations and the two studies in the CCI. Problem and Significance Chronically critically ill patients plateau at a level of persistent organ dysfunction, such as ventilator dependence, renal failure or cognitive impairment, that leads to a prolonged dependent state fraught with multiple and repeated complications. The syndrome of chronic critical illness appears, clinically, to be associated with a considerable symptom burden. Evidence is growing that symptom burden is prevalent in this population (Nelson et al., 2004; Nelson et al., 2006) but more research is needed. The syndrome of chronic critical illness is an unintended consequence of modern technology and recent evidence shows that the incidence is increasing (Carson & Bach, 2002; Carson, 2006; Daly et al., 2005; Nelson et al., 2004; Nelson et al., 2006). The 20 performance of tracheostomy for failure to wean, a commonly used marker of chronic critical illness, has increased from 8.3 per 100,000 persons in 1993 to 24.2 per 100,000 in 2002 (Carson, 2006). This is a substantial increase of 190%. While approximately 40% of adult ICU patients require mechanical ventilation (Hopkins & Jackson, 2006), the proportion of mechanically ventilated patients requiring support for more than 4 days has increased from 33 to 35%. These increases appear to be associated with a larger number of pre-existing conditions rather than advanced age, as the most significant increase has been noted in those persons under the age of 55 (Carson, 2006). The economic impact of the syndrome of chronic critical illness is huge. While these patients comprise 5-10% of the ICU population, they consume up to 30-50% of ICU resources (Carson, 2006; Carson & Bach, 2002). The annual cost of caring for the CCI is estimated to be $24 billion in ICU costs per year (Carson & Bach, 2002). Prolonged mechanical ventilation, though not universally defined, has been noted to rank third in total Medicare charges ($5 billion in 2005) and first in charges per patient (www.cms.hhs.gov, 2005). Outcomes remain poor in the chronically critically ill (Daly et al., 2005; Douglas & Daly, 2003; Nelson et al., 2006), with hospital and 6 month mortality rates as high as 37.7% and 50%, respectively (Daly et al., 2005; Douglas, Daly, Brennan, Harris, Nochomovitz, & Dyer, 1997). A recent consensus conference on prolonged mechanical ventilation (MacIntrye et al., 2005) recommended using 1-year mortality as the most relevant outcome for the chronically critically ill. Studies have reported 1-year mortality rates as high as 58-72% (Engoren, Arslanian-Engoren, & Fenn-Buderer, 2004; Spicher & White, 1987). 21 Finally, this study aims to address the gaps in knowledge in the symptom burden of the chronically critically ill. Except for one study by Nelson and colleagues (2004) who found symptom distress to be common and severe, symptom burden has not been investigated in the chronically critically ill. It is postulated that symptom burden is high due to the mechanisms underlying the syndrome of chronic critical illness and the cumulative effect of symptoms over a prolonged ICU course. Evidence about the burden in the chronically critically ill and the critically ill or seriously ill populations would support this view. Significant levels of pain, dyspnea, psychological distress, depression, thirst, fatigue, and delirium have been found in acutely ill populations (Desbiens et al., 1999; Desbiens et al., 1996; Nelson et al., 2001; Puntillo et al., 2001; Puntillo, 1990). Chronic Critical Illness Characteristics of the Syndrome of Chronic Critical Illness Chronic critical illness is a discrete syndrome resulting from abnormalities in organ function, metabolism, and neuroendocrine function (Carson & Bach, 2002; Nelson, et al. 2006) necessitating prolonged medical and nursing care. There is no common etiology or primary insult and no consistently predictable disease trajectory, though the need for prolonged mechanical ventilation is a hallmark of the syndrome (Nierman, 2002). While discrete, the syndrome consists of components of both critical and chronic illness. At some critical juncture, yet to be clearly defined, persistent inflammation, neuroendocrine, and pathophysiologic mechanisms produce the prolonged state of chronic critical illness (van den Berghe, 2002). Pathophysiologic Basis of Chronic Critical Illness 22 There is a general lack of understanding of the complex pathophysiologic mechanisms underlying the failure to recover from critical illness and progression into a chronic state (van den Berghe, 2002). Several complex physiologic processes may be relevant. For example, it is known that in prolonged critical illness, fatty acids are no longer efficiently used as metabolic substrates leading to fatty infiltration of vital organs such as the liver and pancreas. Large amounts of protein are wasted from skeletal muscle and visceral sites leading to further impairment of vital organ systems (van den Berghe, 2002). Left ventricular dysfunction may also contribute to the syndrome. Significant differences were found by Sereika and Clochesy (1996) in weaning times from prolonged mechanical ventilation in patients with left ventricular dysfunction. Immobility and inflammation contribute to the syndrome of chronic critical illness. Immobility is common during the early phases of critical illness and inflammation is a normal consequence of infection or injury. Prolonged immobility can lead to acquired muscle dysfunction which has been identified as a cause of prolonged ventilator dependency and lengthy recovery. Chronic stress and chronic illness have been associated with elevated serum levels of various interleukins (Winkelman, 2004). The proinflammatory cytokines of Interleukin-1 (Il-1), Il-6 and tumor necrosis factor alpha (TNF-a) have been implicated in muscle degradation. Neuroendocrine mechanisms that account for the chronic phase of critical illness are speculative. Growth hormone deficiency leading to the wasting syndrome of the chronic state has been noted (van den Berghe, 2002) and it is hypothesized that the pituitary is part of the multiple organ system dysfunction common in this population. The neuroendocrine stress response in chronic critical illness involves multiple hormonal 23 abnormalities different from the acute phase of critical illness. During acute illness, anterior pituitary hormones are actively released, whereas in prolonged critical illness pulsatile secretions of the anterior pituitary hormones decrease due to low hypothalamus output. Obviously, impaired activity of target issues would result. Nierman and Mechanick (1999, 1998) found that the CCI have a high prevalence of bone hyperresorption with high parathyroid hormone levels consistent with immobilization and a high prevalence of hypotestosteronemia. Winkelman, Higgins, Chen, and Levine (2007) proposed that the imbalance between pro- and anti-inflammatory cytokines may contribute to chronic critical illness. More research is needed to understand the multiple derangements in hormonal, endocrine and inflammatory physiology that demarcates chronic critical illness from critical illness. In summary, theoretical support exists to explain the syndrome of chronic critical illness. Some empirical data to support the role of proinflammatory cytokines and endocrine abnormalities does exist. However, the specific factor explaining not just when, but why critical illness becomes chronic remains unknown and most likely is multi-factorial (Carson, 2006; Nierman, 2002). The Concept of Symptom Burden Definition Symptom burden has been defined as the distress or bother resulting from one or more symptoms, in isolation or with a cumulative effect, as perceived by the patient and reported by the patient or significant other. Historical meanings of symptom will be reviewed and then symptom burden will be differentiated from the concept of symptom 24 clusters. Data based articles supporting symptom burden in the CCI and similar populations will be reviewed to delineate the critical attributes. Historical Development of the Concept, Symptom ‘Symptom’ originates from the Latin word, ‘synthoma’, and was first used in its current meaning in the 1600’s (Rhodes and Watson, 1987). During the 1800’s, signs were differentiated from symptoms as evidence of disease or illness that could be observed by the person or others. Though different models and definitions of symptoms exist, there is consensus that symptoms are subjective, perceived by the person experiencing them, multidimensional, and that multiple symptoms can occur simultaneously. Also, symptoms can be caused by multiple influencing factors and symptoms affect patient outcomes (Dodd et al., 2001; Lenz et al, 1997; Armstrong, 2003). The multiple internal dimensions of intensity, quality, duration and distress are also generally accepted as common across symptoms. ‘Burden’ is derived from the Old English word, byrthen or beran, meaning to carry. The current use of burden means something oppressive or worrisome that is carried as a load. ‘Burdensome’ was first used in 1578 and means imposing or constituting a burden (Merriam-Webster’s Collegiate Dictionary, 1991, pg. 188). Duty or responsibility is often associated with use of burden. Like distress, burden has a negative connotation when attached to symptoms. Both words are used in other disciplines, such as physics, economics, and sociology. Symptom clusters have been studied recently, predominantly in the oncology population, and require conceptual distinction from symptom burden. Symptom clusters are defined as three or more concurrent symptoms that are related to each other and that 25 may or may not have a common etiology (Dodd et al., 2001). This associative rather than causal relationship is central to the concept, reflecting the assumption that the relationship among symptoms in a cluster is stronger than relationships across clusters. Symptom clusters can coexist without having the same etiology, burden, or outcome. Central to this definition is the characteristic that symptoms occur together, but the extent to which patients experience multiple symptoms in a cluster simultaneously is not yet supported empirically or clinically (Kim, McGuire, Tulman, & Barsevick, 2005). Indeed, symptom clusters may be grouped more intuitively rather than empirically (Cleeland & Reyes-Gibby, 2002). For example, in a study of patients undergoing chemotherapy, investigators found intercorrelations among pain, fatigue and sleep disturbances to be too small to support clustering (Dodd, Miaskowski, & Paul, 2001). Factor analysis is the exploratory and descriptive technique used to test the underlying structure, if present, for a group of symptoms (Gift, Jablonski, Stommel, & Given, 2004). Ongoing research is needed to support or refute the usefulness of the construct of symptom clusters and the critical aspects (Barsevick, Whitmer, Nail, Beck, & Dudley, 2006) and indeed symptom burden may be part of a cluster effect. Research by Nelson and colleagues (2004) on the symptom burden in the CCI and this study may add to the understanding of symptoms in the chronically critically ill but at present there is insufficient evidence to support the existence of three or more symptoms occurring together in the CCI. Therefore, the concept of symptom cluster was not used as the basis for this study. Symptom Burden in Cancer, Heart Failure, Terminal Illness, and the Critically Ill Symptom burden in cancer 26 The analysis of symptoms in other populations sharing common characteristics with the chronically critically ill will help to clarify the critical attributes of the concept and the strength of empirical support. The effects of pain and fatigue on the cooccurrence of other cancer or treatment related symptoms was studied in 841 patients aged 65 or older and newly diagnosed with breast, colon, lung or prostate cancer (Given, Given, Azzouz, Kozachik, & Stommel, 2001). The investigators used a single-item question to assess pain and fatigue at multiple time points over one year. Patients who reported 3 or more comorbid conditions had additional symptoms at a significant level (x=4.7). Additionally, the presence of pain and fatigue together were substantial independent predictors of the number of other symptoms reported. Patients with pain and fatigue averaged 6.3 other symptoms, fatigue alone 4.4, and pain alone 3.8. This study provides support for the cumulative effect of multiple symptoms over a prolonged time period as these differences were reported to hold stable over the 1 year study period. Limits to the applicability to the CCI is the fact that these cancer patients were predominantly community dwelling and 65 and older. As discussed previously, the majority of CCI patients require care in a facility and the average age is 63-65 (Chelluri et al, 2004; Daly et al., 2005). Symptom burden in the seriously ill The Study to Understand Prognosis and Preferences for Outcomes and Risks of Treatment (SUPPORT) is a landmark, multi-center study in the acutely and critically ill providing strong evidence that symptom burden is common and undertreated. Desbiens and colleagues (1999) defined symptom burden as the presence of one or more symptoms at least moderately severe and occurring at least half the time or extremely severe at any 27 frequency. They reported on the frequency and severity of pain, dyspnea, depression, nausea and anxiety in 1,582 patients with COPD, heart failure, liver failure, nontraumatic coma, colon cancer, lung cancer, acute respiratory failure and multiple organ system failure (MOSF). Nearly half of the sample had one or more symptoms of a moderate severity at least half the time. Patients with inoperable lung cancer, acute respiratory failure and MOSF had the highest symptom burden. This finding provides the strongest support for symptom burden in the CCI due to the similarities between chronic critical illness and these disease states. In other SUPPORT studies, investigators reported that the final days for half of the sample entailed more than 8 days in an undesirable state and, for 50% of the conscious patients who died in the hospital, family members reported moderate to severe pain half of the time (SUPPORT Study Investigators, 1995). Fortynine percent of the patients (n=5,176) in a SUPPORT cohort reported pain and nearly 15% reported extremely severe or moderately severe pain at least half of the time (Desbiens et al., 1996). Symptom burden in heart failure and chronically ill populations Three studies examined symptoms in heart failure patients and chronically ill patients. Patients with New York Heart Association (NYHA) Class III/IV heart failure (n=53) experienced a mean of 15.1 (SD 8.0) symptoms with shortness of breath and fatigue most prevalent, difficulty sleeping most burdensome, and fatigue, sleep disturbances, and respiratory symptoms most distressing (Zambroski, Moser, Bhat, Ziegler, 2005). Total symptom burden, total symptom prevalence, lower age, and functional status predicted 67% of the variance in health related quality of life. This study was limited by a small sample size and convenience sampling limiting generalizability. 28 Breathlessness and fatigue were highly prevalent and burdensome in 542 community dwelling patients with NYHA Class III/IV heart failure. Female gender, depression, and two or more comorbidities were predictive of symptom burden (Barnes et al., 2006). Finally, Walke and associates (2004) studied symptom burden in 226 community-dwelling persons with COPD, cancer or heart failure 2 months after hospital discharge. Eighty percent of the persons experienced 2 or more symptoms. Subjects with COPD had significantly higher moderate or severe symptoms than persons with cancer or heart failure. These results support the presence of a high symptom burden in chronically ill populations that share common characteristics with the CCI, supporting the proposed model. Fatigue is often the most prevalent and bothersome symptom. The hospital mortality and 6-month and 1-year morality rates are high for the CCI. Thus, for many of the CCI, a peaceful and comfortable death will become the goal of care. Some evidence supports the premise that symptom burden may be associated with this end-of-life stage. Family members were asked to recall symptoms of loved ones who died of chronic disease several months after their death (Klinkenberg et al., 2004). Fatigue was most commonly reported (83%), followed by pain (48%) and shortness of breath (50%). The mean number of symptoms recalled was 2.7 with 75% of the sample describing 2 or more symptoms. Linear regression showed that COPD, cardiac disease and cancer contributed significantly to the total symptom burden and all chronic diseases together explained 17% of the variance. Thus, no single chronic disease explained a large part of the symptom burden, lending support to the nondiscrete attribute of individual symptoms that contribute to burden in the model. 29 In another study, staff perceptions of symptom presence, frequency and severity were obtained on 348 hospice patients (Kutner, Kassner, & Nowels, 2001). Lack of energy was the most common, frequent and severe symptom. Cancer patients were less likely than noncancer patients to have frequent (42% vs. 59%, p = 0.03) and severe (17% vs. 37%, p = 0.004) shortness of breath and frequent difficulty swallowing (41% vs. 60%, p = 0.048). Though the risk of a Type I error was high due to multiple comparisons, this study lends moderate support to the presence of multiple symptoms as a component of burden. A limitation of both of the above studies was the use of proxy data to obtain symptom data. Patients at the end-of-life, including the CCI, are often unable to respond to symptom assessments but it is an underlying assumption of the definition of symptom burden that patients still perceive symptom(s) and the patient or significant other can report the symptom(s). These two studies lend support to this critical attribute of symptom burden as defined in this study. Symptom burden in the critically ill Critically ill and mechanically ventilated patients share many of the same risks for symptom burden as the CCI. Mechanical ventilation is a hallmark of the syndrome of chronic critical illness and a source of symptom burden. Existing studies support that ventilated patients commonly experience dyspnea, anxiety, pain, sleep disturbances, depression, fatigue and distress due to communication barriers (Bergbom-Engberg & Haljamae, 1989; Connelly , Gunzerath, & Knebel, 2000; Higgins, 1998; Holland, Cason, & Prater, 1997; Knebel, Janson-Bjerklie, Malley, Wilson, & Marini, 1994; Nelson, 2002; Nelson et al., 2004; Rotondi et al., 2002). Of 100 patients who were ventilated for at least 48 hours, thirty-three (44%) recalled a pain level of 10/10 associated with the 30 endotracheal tube (Rotondi et al., 2002). In the study by Nelson and colleagues (2004), chronically critically ill patients (n=50) transferred to a Respiratory Care Unit experienced substantial symptom distress, including dyspnea (60%), pain at the highest level (44%), severe distress due to communication barriers (90%), and psychological symptoms frequently or almost constantly (60%). Patients ventilated for at least 3 days experienced significant levels of dyspnea and anxiety throughout the weaning process and independent of weaning mode (Knebel et al., 1994). Fatigue was found in 100% of patients (n=20) on mechanical ventilation for ≥ 7 days and moderate depressive symptoms were reported in 60% (Higgins, 1998). A recent pilot study on coexisting symptoms in mechanically ventilated patients (n=15) reported that patients experienced dyspnea (100%), severe thirst (40%), moderate to severe pain (40%), and tiredness, hunger, discomfort, and depression (30%). More than 60% of this sample had some degree of symptom burden (Li & Puntillo, 2006). Thus, there is strong empirical evidence to support the contribution of mechanical ventilation to symptom burden in the chronically critically ill. Symptom Burden in the Chronically Critically Ill There are multiple sources of unpleasant symptoms in the CCI, including factors related to the underlying acute illness, chronic conditions, complications, and/or diagnostic and therapeutic procedures. The accumulation of these insults and the prolonged recovery associated with chronic critical illness suggest a high symptom burden (Nelson et al., 2004). To date, there have only been two studies of the symptom experience of the CCI. Nelson and colleagues (2004) investigated the self-reported symptom burden, in real-time, of 50 patients who were transferred to a respiratory care 31 unit (RCU). In this sample, 72% (36/50) were able to complete a self-report and 90% reported symptoms. Forty-four percent of the patients reported pain at the highest level, 60% experienced dyspnea, 60% reported severe psychological symptoms, and 90% reported severe distress related to difficulty communicating. The CCI patients in this study were able to self-report symptom burden and that burden caused a very high level of distress. Symptoms were assessed twice weekly for the entire RCU stay. The average number the study patients were able to respond to symptom assessments was 3.6 times (95% confidence interval, 2.9-4.2) over an average RCU length of stay of 3-4 weeks. This was the first study to report a prolonged and bothersome pattern of symptoms experienced by the CCI using repeated time-points. In a follow-up study by Nelson and associates (2006), the incidence of brain dysfunction was examined in 203 CCI patients admitted to their RCU. Though 82% of these patients were cognitively intact prior to the acute event, 30% were comatose and 50% delirious throughout the RCU admission. The average number of days in coma and delirium was 17.9 and 25.6 days, respectively. At 6 months, 70.6% of the sample was still profoundly cognitively impaired and two-thirds dependent in all activities of daily living. These findings support two additional attributes of symptom burden, duration and inability to report symptoms in a conventional way, as conceptualized in this study. In summary, there is substantial evidence to support that symptom burden exists in seriously ill populations similar to the CCI, and there is beginning evidence to indicate substantial symptom burden in the CCI. The SUPPORT studies are the most rigorous but the Nelson studies are the most specific to the population of interest. Some studies are limited by the use of proxy reports or small sample sizes; this underscores the inherent 32 difficulty in studying symptom burden in patients with such a high degree of disease burden and communication barriers. Critical Attributes of Symptom Burden Critical attributes of a concept are characteristics that appear repeatedly in the literature (Walker and Avant, 1995). ‘Symptom burden’ is not used as frequently in the symptom literature as ‘symptom distress’ or ‘symptom clusters’. Until recently, there were no studies describing symptom burden in the chronically critically ill. Cleeland and Reyes-Gibby (2002) define symptom burden as a summary measure of disease-and treatment-outcome status and propose that symptoms contribute to the burden of the disease. Critical attributes of symptom burden include: 1. one or more symptom as perceived by the patient, independent of the abililty to verbally report the symptom 2. if multiple symptoms coexist, vague, indistinct lines exist between symptoms with a cumulative effect 3. duration of the symptom(s) is a component of the burden 4. relief of the burden requires reliance on others 5. the person’s ability to evaluate and respond to the perceived symptom may be unclear, unknown or inconsistent Influencing Factors of Symptom Burden: Physiologic, Psychologic, and Situational Physiologic Factors There are multiple physiologic or pathophysiologic factors that influence the symptom burden in the chronically critically ill. Theoretical support and empirical support will be reviewed for the effects of prolonged inactivity, almost universal in the 33 CCI, the oxidative stress response and inflammatory response mediated by cytokines, critical illness myopathy, neuroendocrine abnormalities, nutritional deficits, and treatment related burden. Inactivity and prolonged bedrest Chronically critically ill patients endure prolonged bedrest. As we know now, bedrest is not a benign phenomenon (Winkelman, 2007). Prolonged inactivity quickly leads to deconditioning associated with changes in multiple organ systems. These include the cardiovascular effects of decreased plasma volume and orthostatic instability, a decrease in respiratory volumes and ventilation/perfusion mismatching predisposing patients to hypoxia, atelectasis, and pneumonia, integumentary changes increasing the risk of decubitus ulcers, hyperglycemia due to insulin resistance, and, musculoskeletal effects of bone loss and increased calcium excretion. The influence of prolonged inactivity and bedrest on symptom burden in the CCI has not been studied so the relationship is hypothetical. However, one study of the effects of several weeks of inactivity in healthy male volunteers found multiple symptom expression including anxiety, hostility, depression and sleep deprivation (Mallis & DeRoshia, 2005). Clinically we know that the main activity for the CCI is bedrest due to the prolonged hemodynamic instability and dependence on life-sustaining therapy. Length of stay (LOS) for the CCI in the ICU has decreased from 53 days as reported in 1987 (Spicher & White) to 15.9 days recently reported by Daly and colleagues (2005) but is still substantially higher than the LOS of 4.64 days for the average ICU patient (Rosenberg et al, 2000). Studies indicate that exposure to an intensive care-like 34 environment or post-acute settings, associated with extensive inactivity, is prolonged. Nelson and colleagues (2006) reported the mean combined stay in the ICU and ventilator weaning unit was 45 days and the median LOS in extended care facilities for a group of CCI patients studied by Nasraway and colleagues (2000) was 70 days. Evidence from a descriptive pilot study (n=20) of the relationship between activity and cytokine levels in the CCI supports the assertion that inactivity is predominant in the CCI. The average amount of activity recorded over a 24 hour period, on the 10th day of the ICU stay, by actigraphy and direct observation was approximately 3 hours (Winkelman et al, 2007). The investigators reported that turning, range of motion, transfer to or sitting in a chair accounted for 61% of the recorded activity. The remaining 39% of activity was considered baseline activity or all movement initiated by the subject. Thus, theoretical support, empirical support of long LOS, and very preliminary data, regarding the average activity level, support the preponderance of inactivity most CCI patients endure and the potential for its influence on symptom burden. Oxidative stress and the inflammatory response Systemic deconditioning is mediated by the two pathways of the oxidative stress response and inflammatory cytokines. It is well known that traumatic injury causes diffuse changes in the function of the autonomic nervous system and the endocrine system. These changes lead to widespread physiologic changes mediated by cytokines and other mechanisms. Cytokines are small biologically active proteins found on white blood cells, myocytes, endovascular cells, and epithelial cells. Activated macrophages are known to synthesize large quantities of the cytokines, interleukin-1 (IL-1), interleukin- 35 6(IL-6), interleukin-10 (IL-10), and tissue necrosis factor-alpha (TNF-α). Stress promotes protein catabolism and release of proinflammatory cytokines causing nitrogen wasting, muscle weakness, and persistent inflammatory response (Epstein and Breslow, 1999). Proinflammatory cytokines interleukin-1 (IL-1), IL-6 and tumor necrosis factor –alpha (TNF-a) are associated with many inflammatory pathologies and lead to prostaglandin synthesis that promotes peripheral inflammation and increased sensitivity to painful stimuli (Cleeland et al., 2003; Winkelman, 2004). Evidence does exist to support the contribution of cytokines to the symptom burden in the CCI. Interleukins (IL-1) are known to induce fever and IL-1B is a major inducer of cyclooxygenase-2 synthesis that leads to production of prostaglandins. Prostaglandins, in turn, produce peripheral inflammation and increased sensitivity to painful stimuli (Curfs, Meis, & Hoogkami, 1997). IL-6 also stimulates the release of prostaglandins and persistent elevations have been linked to increased mortality risk in sepsis and trauma (Gogos, Drosou, Bassaris, & Skoutelis, 2000; Muller Kobold et al., 2000). Studies have linked both excessive and low levels of the protypical antiinflammatory cytokine, IL-10, with systemic inflammatory response (SIRS) and increased mortality from infection (Gogos et al., 2000; Opal & DePalo, 2000). TNF-α, an early responder to injury and infection, has been found to be the physiologic basis for symptoms of fever, fatigue, anorexia, rigor and headache (Balkwill, 2000; Dantzer, 2001; Dinarello, 2000). Some investigators suggest that it is not the absolute values of individual cytokines but the balance between pro- and anti-inflammatory mediators that determines progression of disease and disuse. The range of the ratio of Il-6:IL-10 is 1.6 for healthy 36 adults to as high as 18.7 in the critically ill (Gordon et al., 2001;Taniguchi, Koido, Aiboshi, Yamashita, Suzaki, & Kurokawa, 1999a, 1999b). In the only study of cytokine levels in the chronically critically ill (n=10), the average IL-6:IL-10 ratio was 13.2 suggesting a high degree of cytokine expression though this ratio was not associated with weaning success or length of stay (Winkelman et al., 2007). Oxidative stress causes damage to DNA, lipid structure and proteins. These oxidants, reactive oxygen species (ROS) and nitric oxide (NO), mediate inflammatory damage to muscles contributing to generalized deconditioning in patients suffering from prolonged illness (Winkelman, 2007). There is a strong link between oxidative stress and contractile dysfunction in skeletal muscle (Winkelman, 2007). Studies do indicate that antioxidants, which mitigate this oxidative stress, are reduced during lung injury (Langen, Korn & Wouters, 2003) and acute renal failure (Simmons et al., 2004). Both lung injury and renal failure commonly coexist with chronic critical illness though no studies have been conducted to examine the connection between oxidative stress and symptoms in the CCI. The growing recognition that cancer symptoms share common biologic mechanisms led to the development of a cytokine-immunologic model of symptom expression by a working group of basic and clinical scientists active in symptom-related research. Cleeland and colleagues (2003) acknowledge that many of the components of the model lack empirical support as it was derived from animal studies and clinical evidence. However, preliminary clinical studies of patients with acute leukemia and myelodysplastic syndrome showed correlations between fatigue and cognitive impairment and elevations in IL-1, IL-6, IL-8, and TNF-α (Kurzrock, 2001; Meyers, 37 Seabrooke, Albitar, & Estey, 2003). Still, this model provides a framework for future research and lends theoretical strength to the supposed contribution of the stress response and cytokine mediated inflammatory response to symptom burden in the CCI. Critical illness myopathy and symptom burden The syndrome of chronic critical illness predisposes these patients to myopathies. There are well established links between prolonged bedrest, inactivity, and muscle atrophy. Muscle atrophy due to oxidative stress and proteolysis in septic patients also has been reported (Rabuel et al., 2004). Identified mediators of sepsis induced muscle changes include nitric oxide, TNF-α, IL-1, and IL-6, known to stimulate prostaglandins and the pain response. Sepsis and multiple organ system failure, common in the CCI, are major risk factors for the development of critical illness neuromyopathy (CINM), the most common acquired neuromuscular disorder in the ICU setting (DeJonghe, Lacherade, Durand, & Sharshar, 2007). Multiple studies of the effect of CINM on the duration of mechanical ventilation in critically ill patients have reported a statistically significant relationship (Amaya-Villar et al., 2005; De Jonghe, et al., 2002; Druschky et al., 2001; Garnacho-Montero, Amaya-Villar, Garcia-Garmendia, Madrazo-Osuna, & Ortiz-Leyba, 2005; Garnacho-Montero et al., 2001; Leijten et al., 1996). Though the incidence of CINM in the CCI has not been reported, it can be postulated that CINM and muscle atrophy prolong dependency on mechanical ventilation, thereby prolonging length of stay and exposure to other sources of symptom burden producing a cumulative effect. The contribution of CINM to symptom burden in the CCI is speculative and warrants further study. 38 High serum cortisol levels, low ACTH levels, and low thyroid hormone levels are associated with chronic critical illness (van den Berghe, 2002). Some of the clinical expressions of hypothyroidism include fatigue, somnolence, myalgias, and poor memory (Carey, Lee, & Woeltje, 1998). Though the exact mechanism underlying the hypothyroidism in chronic critical illness is unexplained (van den Berghe, 2000) it is possible that hypothyroidism and its clinical effects contribute to symptoms in this population. This area needs to be studied to add empirical support to the influence of endocrine changes on symptom burden. Nutritional devices and deficiencies Nutritional factors may explain some of the symptom burden in the CCI. These factors are device related and due to specific deficiencies. Placement of a percutaneous endoscopic gastrostomy (PEG) tube has become the method of choice for long-term tube feeding (McMahon, Hurley, Kamath, & Mueller, 2005) and is common in the chronically critically ill. In an ongoing investigation of the effects of an intensive communication intervention on resource use in the CCI, 30% (87/235) of the patients enrolled as of February 2007 had PEG tubes placed while in the ICU (Daly, 2005). Higgins and group (2006) reported that 85% of CCI patients (N=360) received enteral feedings as their only nutritional source. Estenssoro and colleagues (2006) reported that chronically critically ill patients, with a mean ICU LOS of 39 days, averaged 30 days (±21) of enteral feedings with interruptions due most commonly to diarrhea, ileus and shock. PEG tubes are associated with minor and major complications. Minor complications which occur in about 10% of patients are pain, erythema at the insertion site, and leakage; major complications of bowel perforation, hemorrhage and aspiration 39 occur in 3% of patients (DiSario et al., 2002; Finocchiaro et al., 1997). These complications, coupled with the prevalence of PEG tube and enteral feeding tubes in the CCI, and other GI complications in patients receiving mechanical ventilation (Mutlu, Mutlu, & Factor, 2001) lend support to nutritional devices as a treatment related source of symptom burden in the CCI. Malnutrition has a negative effect on physiologic parameters in the CCI and outcomes in general populations. Serum albumin and body mass index were predictors of mortality in hemodialysis patients (Leavey, Strawderman, Jones, Port, & Held, 1998). The need for hemodialysis or renal replacement therapy is common in the critically ill and is usually associated with multiple organ failure (Korkeila, Ruokonen, & Takala, 2000) and the prevalence of renal disease in the CCI has increased from 17 to 24% (Carson et al., 2006). Higgins and colleagues (2006) studied the relationship between nutritional indicators and mechanical ventilation in the CCI and found that mean albumin and hemoglobin levels were low on admission and remained lower than normal throughout the illness. While nutrition did not predict the major outcome of liberation from mechanical ventilation, they did find that 56% of the 312 patients were underfed. Unsatisfied thirst was the most prevalent symptom at the highest level of intensity in 36 CCI patients studied by Nelson et al (2004) and hunger was prevalent in approximately 52% of this sample. Though more research is needed, evidence does support the assertion that perception of thirst and hunger in addition to the potential discomforts or complications of PEG tubes or other feeding tubes contribute to symptom burden in the CCI. Influence of treatment related activities 40 Common nursing care activities and treatment related activities contribute to symptom burden in the CCI. Strong empirical data supports the relationship between pain and the common procedures of turning, suctioning, central venous catheter insertion, wound drain removal, femoral sheath removal and wound care. In a multi-center, prospective study of procedural pain-related behaviors in 5, 957 acutely or critically ill hospitalized patients Puntillo, Morris, Thompson, Stanik-Hutt, and White (2004) reported a mean procedural distress score of 2.60 (SD 3.05) and a mean pain intensity score across all 6 procedures of 3.7 (SD 3.2). Procedural distress was defined as a negative emotional response to the procedure and was measured using a visual analog scale (VAS) from 0 (no distress) to 10 (worst possible distress). Procedural pain intensity was defined as the unpleasant sensory and emotional experience arising from actual or potential tissue damage associated with diagnostic or therapeutic procedures and was measured using a VAS from 0 (no pain) to 10 (worst possible pain). Behavioral responses included facial responses such as grimacing or wincing, verbal responses such as moaning or whimpering, and body movements such as restlessness and guarding. The investigators reported that patients exhibited significantly more pain-related behaviors with vs. without procedural pain (3.5 vs. 1.8 behaviors; t = 38.3, df = 5072.5; 95% confidence interval, 1.6-1.8). Thirty-three percent of the variance in the amount of pain behaviors exhibited during a procedure was explained by the three variables of degree of pain intensity, degree of procedural distress and the turning procedure. None of the other 5 procedures made a significant unique contribution to explaining the variance in pain-related behaviors nor did age, ethnicity, and amount of benzodiazepines before/during the 41 procedure. Thus, one of the most basic and common nursing procedures, turning, was found to contribute most significantly to burden. The common procedure of endotracheal suctioning is recognized as a painful procedure. Puntillo (1994) reported the mean pain intensity score associated with endotracheal suctioning to be 4.9 cm on a 0-10 numerical rating scale in a group of cardiovascular surgery patients. The use of a behavioral pain scale in thirty mechanically ventilated patients receiving sedation was studied by Payen and colleagues (2001). They found that mobilization (turning side to side) and suctioning resulted in significantly higher values on the pain scale than the application of compression stockings or central venous catheter dressing changes (4.9 vs. 3.5, p < .01). Mechanical ventilation is as an influencing factor on the symptom burden in the critically and chronically critically ill and a hallmark of the syndrome of chronic critical illness. Existing studies support that ventilated patients commonly experience dyspnea, anxiety, pain, sleep disturbances, depression, fatigue, and distress due to communication barriers (Bergbom-Engberg & Haljamae, 1989; Connelly, Gunzerath, & Knebel, 2000; Higgins, 1998; Holland, Cason, & Prater, 1997; Knebel, Janson-Bjerklie, Malley, Wilson, & Marini, 1994; Nelson, 2002; Nelson et al., 2004; Rotondi et al., 2002). Of 100 patients who were ventilated for at least 48 hours, thirty-three (44%) recalled a pain level of 10/10 associated with the endotracheal tube (Rotondi et al., 2002). In the study by Nelson and colleagues (2004), chronically critically ill patients (n=50) transferred to a Respiratory Care Unit experienced substantial symptom distress including dyspnea (60%), pain at the highest level (44%), severe distress due to communication barriers (90%), and psychological symptoms frequently or almost constantly (60%). Patients ventilated for at 42 least 3 days experienced significant levels of dyspnea and anxiety throughout the weaning process and independent of weaning mode (Knebel et al., 1994). Fatigue was found in 100% of patients (n=20) on mechanical ventilation for ≥ 7 days and moderate depressive symptoms were reported in 60% (Higgins, 1998). A recent pilot study on coexisting symptoms in mechanically ventilated patients (n=15) reported that patients experienced dyspnea (100%), severe thirst (40%), moderate to severe pain (40%), and tiredness, hunger, discomfort, and depression (30%). More than 60% of this sample had some degree of symptom burden (Li & Puntillo, 2006). There is strong empirical evidence to support the contribution of mechanical ventilation to symptom burden in the chronically critically ill. There are multiple physiologic factors that could have a prolonged and cumulative effect on symptom burden in the CCI. Though research is scant in the CCI, there is strong theoretical support for the role that prolonged inactivity, the inflammatory response and cytokines play in symptom expression. Aspects of the physical environment, feeding devices and other types of devices, or care routines also have been shown to produce symptoms in the CCI. The extremely common nursing care activities of turning and suctioning have been shown to be strong contributors to symptom burden. There is also strong evidence linking mechanical ventilation, the hallmark of CCI, and symptom burden. Psychologic Factors Influencing Symptom Burden Psychologic factors such as the individual’s mental state or mood and reaction to the illness state can contribute to symptom burden. There have been numerous studies demonstrating a link between depression and anxiety and symptom burden. Joynt, 43 Wheelan and O’Connor (2004) reported that depression may augment catecholamine release, proinflammatory cytokine expression, and platelet activation in heart failure patients. Depression, common in heart failure, is an independent predictor of poor outcomes as demonstrated by Joynt et al. (2004) and by investigators of 113 outpatients in a heart failure clinic (Sullivan, Levy, Russo, & Spertus, 2004). Sullivan and group also found that depression was positively correlated with poorer health status and symptom severity. In a qualitative study by Bosworth et al. (2004), anxiety among 45 heart failure patients was associated with the fear that symptoms would not be controlled. Additionally, these patients expressed high anxiety, depression and fear associated with the unpredictability and uncertainty of the chronic disease. Depression was prevalent in 42% of patients with COPD (Gift & McCrone, 1993) and depression can worsen distressing symptoms of pain and fatigue in the terminally ill (Block, 2000) and other populations (Gallagher & Verma, 1999). The Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT) reported on the experience of 5, 176 seriously and acutely ill patients (1995). The investigators reported that anxiety was strongly associated with higher levels of pain with nearly double the odds of an increased level of pain in those patients reporting anxiety as compared to those with no anxiety. The odds of having greater pain increased by two-fold if co-existing with depression (Desbiens et al., 1996). A study by McWilliams, Cox and Enns (2003) found a significant positive correlation between the chronic pain condition of arthritis and anxiety disorders in a national survey 44 of 5,877 patients. The strongest association was between pain and panic disorder and post-traumatic stress disorder. Depression is common in some reports of the CCI and possibly contributes to symptom burden. Connelly, Gunzareth, and Knebel (2000) assessed mood state in 21 patients who received at least 3 days of mechanical ventilation and weaned successfully. Patients who weaned experienced greater mood disturbance. The total mood disturbance score in these subjects was higher than the comparison groups of college students, postcardiac surgery patients, and the terminally ill and was largely due to higher fatigue and anger scores. There was moderate correlation between total mood disturbance and dyspnea intensity (r = .031). There was a 33% prevalence of agony and panic in a group of patients on mechanical ventilation; this panic was significantly associated with asynchrony between spontaneous and mechanical ventilation (Bergbom-Engberg & Haljamae, 1989). The incidence of delirium in the CCI has been reported in almost 50% of CCI patients (Almanza, Downhill, & Nierman, 2000; Nelson et al., 2006) but the impact on symptom burden has just recently been investigated. Delirium was an independent predictor of hospital LOS after controlling for age and severity of illness and was experienced by 83% of mechanically ventilated patients in medical and coronary ICU’s (Ely et al., 2001). In the only study to examine the burden of brain dysfunction in the chronically critically ill, Nelson and colleagues (2006) reported the findings in 203 patients transferred to their Respiratory Care Unit (RCU). Severe and prolonged coma and delirium were found. Despite normal pre-admission cognitive function in 82% of these patients, 30% (61) were comatose and 50% (66) were delirious throughout the RCU 45 stay. This burden persisted beyond the intensive care environment as 68.2% (58) of survivors were too impaired to respond to cognitive assessments at 6 months. There was a significant association between the duration of delirium or coma and the increased likelihood of being discharged to post-acute care (odds ratio, 1.09; 95% C.I., 1.00-1.20; P=.047) and longer LOS in the hospital (parameter estimate, 0.03; 95% C.I., 0.02-0.03; P<.001). As discussed, delirium (Nelson et al., 2006) and depression are highly prevalent during chronic critical illness (Nelson, 2002; Nelson, 2004; Douglas et al, 2002) and anxiety and fear have been found in mechanically ventilated patients but the specific influence of mood disturbances or other psychologic disorders on symptom burden in the chronically critically ill has only been reported in one study. Evidence does exist in populations similar to the CCI, such as heart failure, that mood state impacts the expression of symptoms. Situational Factors Influencing Symptom Burden Physical environment In addition to physiologic and psychologic influences of symptom burden, situational factors also influence the experience of CCI patients. These factors can be classified according to the impact of the physical environment and social environment on the CCI. Studies that address the impact of light, noise and care routines on sleep patterns and the relationship between social isolation and symptoms will be reviewed. Patients with prolonged stays in the ICU are constantly exposed to environmental stressors such as excessive light and noise and care routines of hourly vital signs, hygiene, dressing changes, line placement, diagnostic procedures, restraints, and others. 46 Due to these multiple disruptions, it is generally appreciated that ICU patients experience sleep disturbances (Cooper, Gabor, & Hanly, 2001; Cooper et al., 2000; Gabor et al., 2003; Higgins, Winkelman, Lipson, Guo, & Rodgers, 2007; Redeker, 2000). Since the CCI are exposed to an average length of 45 days in an intensive-care like environment (Nelson et al., 2006), it is assumed that the CCI experience similar sleep disruption as the average ICU patient. Sleep disorders are associated with the stress of acute illness (Redeker, 2000) and adverse consequences of illness (Cooper et al., 2000). Sleep disruption is characterized by lower nocturnal sleep efficiency, increased wakefulness, and reduced rapid eye movement sleep (Cooper et al., 2000). In a study of ICU survivors interviewed within 3 days of ICU discharge, 61% reported sleep deprivation though insomnia was only rated by 7% of the sample as the worst memory of intensive care (Simini, 1999). In contrast, a study by Novaes, Aronovich, Ferraz, and Knobel (1997) found that 50 ICU patients ranked insomnia as number 2 in a list of 40 stressors. Specific causes of sleep disruption have been studied, including melatonin secretion, light and noise, care routines and medications. In the first study to measure melatonin in septic patients, Mundigler and group (2002) reported the absence of the circadian rhythm of 6-sulftoxymelatonin, the urinary metabolite of melatonin. Stable circadian rhythm of melatonin is known to be closely related to normal cyclic change between day and night sleep. These findings may be relevant to environmental factors influencing symptom burden. Light and sound were recorded continuously for 7 days in a respiratory and medical ICU (Meyer et al., 1994). All areas showed noise levels that exceeded the EPA 47 recommendation of 45 dB(A) for hospitals at daytime and 35 dB(A) at nighttime. In the Medical ICU, the mean sound peak level was 83.6 ± 0.1 dB. ICU patients described vital signs and phlebotomy as the most disruptive nursing interventions to sleep and staff conversation and telemetry alarms as the most disruptive environmental noises in a study by Freedman, Kotzer, and Schwab (1999). There was no perceived difference between patients on the mechanical ventilator (16%) and off though ventilated patients did experience more daytime sleepiness. The findings of this study were limited by the lack of polysomnography (PSG) to verify the subjective response by participants to minimize recall bias. In a more recent study, Freedman and colleagues (2001) directly linked noise to 15% of arousals from sleep using polysomnography in the critically ill. There is strong evidence that the noise level endured by the CCI is high and contributes to symptom burden inn the form of sleep insufficiency. In a study of 20 patients with acute lung injury, no patients had normal sleep as measured by PSG and continuous EEG during a 24 hour period (Cooper et al., 2001). The total duration of sleep was close to normal, though there was a marked reduction in REM sleep and severe nocturnal sleep fragmentation as compared to less critical patients. This fragmentation has been correlated with daytime sleepiness. Finally, patient care activities have been investigated as a source of sleep disturbance. Sleep patterns in patients on mechanical ventilation were compared to healthy volunteers using PSG and self-report (Gabor et al., 2003). Twenty percent of arousals/awakenings were due to patient care activities and noise peaks separately. Interestingly, patient care interruptions occurred an average of 7.8 ± 4.2 times per hour of sleep. Sound and care activities accounted for less than 30% of the observed sleep 48 disruption suggesting that other factors contribute to this problem in the critically ill. It should be noted that these investigators found a lack of correlation between self-report of sleep disruption and PSG data. In summary, strong evidence supports sleep disruption in the critically ill. These disturbances are most likely multifactorial and related to a combination of noise, light, care activities, sleep disruption, medication, and severity of illness. Evidence also exists that this sleep deprivation is perceived by patients as highly bothersome. Social environment Aspects of the social environment that can influence symptom burden include marital and family status, access to health care resources, and social support (Lenz et al., 1997). Due to the prolonged nature and complexity of chronic critical illness that usually requires care in an intensive care or post-acute care setting, the lack of social support and potential for social isolation are most applicable. Though the relationship between social support and symptoms has not been studied in the CCI, Higgins (2001) did examine the breadth and helpfulness of social support networks in this population. On average, these patients reported 5.7 (possible range 1-7) supports. Mean helpfulness scores with higher scores indicating greater satisfaction were: spouse (M=3.1), children (M=3.6), other family members (M=3.5), friends (M=3.1), nurses (M=3.5), and physicians (M=3.4). These findings would suggest that at least for this cohort of 103 CCI still in the acute care setting were very satisfied with the degree of social support. This is the only study examining social support in the CCI but there have been investigations in other population. 49 Two qualitative studies have reported on the influence of social support in the critically ill. The perception of need and adequacy of social support in 30 critically ill patients was studied by Hupcey (2001). Categories that emerged were the need for social support based on the person’s perception and lack of social support. Quality of support was more important than quantity. Critically ill patients (n=10), in another study, reported positive feelings when they perceived social support (Geary, Tringali, & George, 1997). Neither study examined the influence of social support on outcomes or symptom burden. The effect of social support or isolation also has been investigated in heart failure patients. The CCI share many similarities with heart failure patients that include complex physiologic derangements and associated symptom burden, high mortality and morbidity, poor functional status, depression, and poor quality of life (Stewart et al., 1989). Social isolation was a significant predictor of mortality in 119 stable heart failure patients followed for two years by Murberg and Bru (2001). Changes in social support did predict changes in health related quality of life in 227 patients hospitalized with heart failure (Bennett et al., 2001). Again, the relationship between social support and symptom burden was not the focus of these studies but the findings do provide insight into the impact of social support on the related variables of mortality and quality of life. In summary, the direct influence of social support on symptom burden in the CCI has not been examined. This relationship is hypothetical but does seem reasonable that support could mitigate stressful responses or symptom-producing events. There are considerable gaps in the literature to support the relationship between the social environment and symptoms in the CCI. Symptom Burden and Functional Status 50 The relationship between symptom burden and functional status in the CCI is a major component of this study model but the most hypothetical as only two studies have examined this correlation (Nelson et al., 2006; Nelson et al., 2004). These studies and studies of the relationship between symptoms and functional status will be discussed as the foundation for the inclusion of covariates in the study model. Symptom Burden and Functional Status in Similar Populations Cancer The relationship between symptoms and functional status has been studied extensively in the oncology population, heart failure, and COPD. Five oncology studies are pertinent to this review. The effect of pain, fatigue, and sleep insufficiency on functional status was examined in 93 patients receiving chemotherapy in an outpatient setting (Dodd, Miaskowski, & Paul, 2001). Subjects rated their functional status using the Karnofsky Performance Scale (KPS) at baseline and at the end of the third cycle of chemotherapy (timepoint 2). KPS at timepoint 1, age, pain and fatigue were significantly correlated with the KPS at timepoint 2. The overall hierarchical multiple regression model explained 48.4% of the variance in functional status but the intercorrelations among the 3 symptoms were too small to be considered a symptom cluster as defined by the investigators. The contribution of age to functional status in this study provides support for age as a covariate in the study model. Also, the method used by the investigators in this study was pertinent to this study’s design. Items from a well-being or quality of life instrument were used to obtain symptom data in a secondary analysis. Three proxy items from the Quality of LifeCancer (QOL-CA) measure were used: “pain”, “tires easily”, and “sleeps enough to meet 51 needs”. The summed score on the total QOL- CA was not used by these investigators. Though Dodd and colleagues identified the use of items from a QOL measure to assess symptoms in their sample as a study limitation, they did report that this symptom cluster correlated significantly with functional status, though the symptoms did not correlate with each other. The use of this particular approach by experts in the field of symptom research lent support to inclusion of a similar approach to measure symptom burden in this sample of the CCI. In a second study, 129 elderly patients with lung cancer were found to be at high risk for loss in physical function due to symptom severity (Kurtz, Kurtz, Stommel, Given, & Given, 2000). Using ANCOVA, prior physical function (B = 0.52, P = < 0.001) and symptom severity (B = 0.3; P = < 0.001) were found to be significant predictors of the loss of physical function at 2-4 weeks after chemotherapy or 4-6 weeks after surgery. Physical functioning was measured using a subscale of the Medical Outcomes Study (MOS) 36-Item Short Form Health Survey (SF-36). Loss of physical functioning was positively correlated with prior functional status and negatively correlated with age. Fatigue (82.2%) was the most frequently reported symptom. This analysis lends support to age and premorbid functional status as significant variables related to post-discharge functional status. In a third study of 187 oncology patients in the outpatient setting, distress from multiple symptoms was significantly correlated with a decline in functional status (Schuit et al., 1998). Sixty percent of all patients had pain as the most prevalent in addition to experiencing distress from nausea, constipation, dyspnea, and urinary incontinence. No significant difference between pain in the cancer group (mean age 61) and control group 52 (mean age 50) led to the conclusion that pain is common in this age group. Depression explained 44% of the variance in fatigue (p = 0.001) as a component of functional status in 60 patients with uterine cancer. After adjustment for age, marital status and cancer stage, depression was a significant independent variable (p = 0.001, R2 48%) in this study (Ahlberg, Ekman, Wallgren, & Gaston-Johansson, 2004). Finally, a recent study of the symptom cluster of depression, fatigue, sleep disturbance, cognitive impairment, and pain in patients with high-grade gliomas (n=73), significantly correlated with each other and with functional status (Fox, Lyon, & Farace, 2007). The five variables explained 62 % (F = 19.63, p = .000) of the variance in functional status with depression making the largest single contribution (56%). Limits of this study included convenience sampling and under-representation of older, less educated and less affluent subjects. Heart failure and COPD The trajectory of heart failure and COPD are similar to chronic critical illness and confirmation of the relationship between symptoms and functional status in these conditions provides important support for the study model. In a secondary analysis of 2,992 patients from the SOLVD (Studies of Left Ventricular Dysfunction ) trial, nine psychosocial variables, as measured by the POMS (Profile of Mood States), were predictors of severe limitations in activities of daily living (ADLs) at 1 year (Clarke, Frasure-Smith, Lesperance, & Bourassa, 2000). Patients with low ejection fractions, poor social integration, and high levels of anxiety or depressed mood experienced serious limitations in functional status. Baseline functional status and comorbid conditions were controlled for in the analysis and did not affect the associations. 53 Though NYHA class functional status was used as an independent variable in 58 heart failure patients, a study by Carels (2004) explains some of the relationship between symptoms and functional status. Multiple regression analyses showed that diminished physical quality of life (measured with a 2 week diary derived from the Minnesota Living with Heart Failure Questionnaire) was associated significantly with greater depression (t = 2.48, p < 0.02) and diminished functional status (t = 5.32, p < 0.01). Though the small sample size limits generalizability of findings, this study does add support to the relationship between psychological symptoms and functional status. Though the use of functional status as a predictor and physical quality of life as an outcome is conceptually vague, it is not uncommon that many investigators use the concepts of quality of life and functional status interchangeably (Fox et al., 2007). Two studies of the relationship between symptoms and functional status in COPD also support the proposed study model. A secondary analysis of 104 patients with COPD revealed that exercise capacity (B = -.337, p = .0007), dyspnea (B = .324, p = .0009), and depressed mood (B = -.204, p = .011) directly influenced functional status as measured by the 35-item Pulmonary Functional Status Scale (Weaver, Richmond, & Narsavage, 1997). This study used a rigorous path analysis. The low correlation between age and functional status (r = .09) was an unexpected finding. In a second study, the cumulative impact of dyspnea, fatigue and sleep disturbances on functional status on community dwelling COPD patient (n = 100) was examined by Reishtein (2005). Significant moderate negative correlations were found between dyspnea and functional status ( r = .51, p = < .001) and fatigue and functional status ( r = -.27, p < .01). Age, oxygen use, and dyspnea predicted 44% of the variance in functional performance with fatigue and 54 sleep disturbances not making significant contributions to the variance. As these two studies suggest, there is conflicting evidence about the impact of age on functional status. More study is needed to elucidate the contribution of age to functional status, therefore, age will still be controlled for in this study. Symptom Burden and Functional Status in the Chronically Critically Ill Functional status and other outcomes in the CCI have been studied extensively by multiple investigators (Carson et al., 1999; Chelluri et al., 2004; Daly et al., 2005; Douglas et al., 1997; Nasraway et al., 2000) but only one group has analyzed the relationship between symptom burden and functional status (Nelson et al., 2006). Evidence supports a substantial decline in functional status for the CCI from preadmission independence of 56.7% (Nelson et al., 2006) or home dwelling of 89% (Daly et al., 2005) to more than 74.8 % needing caregiver support at 2 months (Spicher & White, 1987) and half needing assistance at 1 year (Carson et al., 1999). Thirty-two percent of CCI patients discharged from a regional weaning center were dependent in all activities of daily living at 3 months (Nelson et al., 2004). In a comparison of short-term and long-term ventilator patients (n=538), long-term patients requiring greater than 96 hours of ventilation were more likely to be discharged to a nursing home and experienced lower QOL up to one year post-discharge (Carson & Bach, 2001). These findings support the functional decline associated with chronic critical illness. Though most CCI patients were in the home setting prior to admission, some had pre-existing functional limitations. Preadmission health status was the only variable to predict readmission risk in a disease management study by Daly and colleagues (2005). There is strong evidence that premorbid functional status is a predictor of poor outcomes 55 in the CCI (Carson et al., 1999; Daly et al., 2005; Garland et al., 2004) and thus will be controlled for in this study. Functional status and readmission risk in the CCI were part of the investigation by Daly and colleagues (2005) in a recently completed trial of a disease management program. They enrolled 1, 041 patients and randomized 334 patients to the intervention or control group, following an in-hospital mortality rate of 37.3%. The differences between these two groups in the costs of care, QOL, satisfaction with care, and caregiver burden and depression were examined. Patients who received the disease management intervention had significantly fewer days of rehospitalization (11.4 vs. 16.7) with a projected cost savings of $481, 811. Functional status at discharge was the only variable to predict frequency of hospital readmission in the study subjects at a statistically significant level. Though symptom data were collected at discharge, symptom prevalence and the impact of symptoms on functional status were not analyzed. The relationship between symptom burden and functional status was studied directly by Nelson and colleagues. They found severe symptom burden in 50 CCI patients with mean Functional Independence Measure (FIM) motor scores decreasing from 75.2 on admission to 46.1 at 3 months and 57.4 at 6 months (Nelson et al., 2004). The modified FIM used in this study ranged from 13, completely dependent, to 91, completely independent. The relationship between symptom burden and functional status was not analyzed. In a more recent study, Nelson et al. (2006) reported that thirty percent of CCI patients (n = 203) were comatose and 50% delirious. At 6 months, 68.2% of survivors were still too profoundly impaired to participate in cognitive assessment. The number of days spent in coma or delirium was significantly associated with lower FIM 56 Motor scores at 3 months (parameter estimate, -0.47: 95% C.I., -0.78 to –0.15; P = .004) and at 6 months (parameter estimate, -0.92; 95% C.I., -1.4 to –0.43; P < .001). This study adds strong support to the relationship between symptom burden and functional status as hypothesized in the study model. Additionally, the investigators’ control for age, sex, ethnicity, Charlson Comorbidity Index, severity of illness, and the number of comorbidities in the multivariate analysis strengthened the inclusion of age, gender, and comorbidities as covariates in this study. In summary, strong evidence exists in populations with characteristics similar to the CCI to support the relationship between symptom burden and functional status. The study by Nelson and colleagues (2006) supports the contribution of cognitive dysfunction to symptom burden and decline in functional status in the CCI. The inclusion of age, gender, ethnicity, preexisting conditions, length of stay and premorbid functional status as covariates in the study model have strong support (Ahlberg et al., 2004; Clark et al., 2000; Dodd et al, 2001; Kurtz et al., 2000; Nelson et al., 2006). Summary This literature review was conducted to support the proposed conceptual definitions of chronic critical illness and symptom burden and to elucidate the relationship between symptom burden and functional status. An indepth analysis of the theoretical background and empirical support for the physiologic, psychologic and situational influences of the symptom burden in the CCI was included. Though evidence does exist in populations with similar characteristics, evidence is lacking to describe symptom burden in the CCI and to support the relationship between symptom burden and functional status. 57 Chapter III Methods The method used to examine symptom burden in the chronically critically ill (CCI) and its relationship to functional status was a descriptive, predictive investigation through a secondary analysis of data from CCI patients enrolled in a disease management study. Responses of CCI patients or their proxies to the open-ended question, “what symptoms are most bothersome at this time” upon discharge from the index hospitalization were analyzed. A sample of 194 was used in this analysis due to 37 missing or blank open-ended questions in the parent sample (n = 231). Secondary Analysis Secondary analysis of an existing data base has several general advantages. First, such analysis contributes to knowledge development by examining a large data base with a new purpose or aim (Burns & Grove, 2001). Second, it is an economical and efficient technique to describe the symptoms in vulnerable populations, such as the chronically critically ill, for whom data collection can be physically taxing or operationally difficult (Clarke, Frasure-Smith, Lesperance, & Bourassa, 2000; Cleeland & Reyes-Gibby, 2002). This secondary analysis of an existing data base of the CCI shared these advantages and afforded the opportunity to examine symptom data, from one of the largest CCI data bases, that were collected but not analyzed. However, some general limitations to secondary analysis were also considered in this study. First, the variables available in an existing data set may not be exact measures of the construct under study (Kiecolt & Nathan, 1985). In this study, symptom burden was measured as the number of symptoms reported by patient self-responders and proxy 58 responders to the open-ended question. It was hypothesized that this open-ended approach would capture the most bothersome symptoms in the person’s own words, thereby providing a measure of the construct, symptom burden. To establish criterionrelated validity, responses to the open-ended question were compared to subjects’ responses on the Medical Outcomes Study Short Form-8 (SF-8), a widely used measure of well-being with established psychometrics. Functional status was measured in the parent study using the Outcomes and Assessment Information Set (OASIS) and this study’s functional status variable was derived from those items. Preliminary evidence has been reported to support sufficient reliability and validity of the functional status items on the OASIS (Madigan & Fortinsky, 2000). Other limitations of secondary analysis of an existing data base include fixed sample size and measurement conditions but the effect was minimal in this study. The sample of 194 derived from the parent study was adequate to support descriptive and multivariate analysis and the conditions under which subjects completed measurement tools in the parent study were consistent with the specific aims of this study. However, the inability to verify patient or proxy responses to the open-ended question did contribute to missing data. Sample and Setting The sample consisted of 194 CCI patient self-responders and proxy responders for whom responses to the open-ended question were available. The primary purpose of the parent study was to evaluate the impact of adding an eight week disease management intervention to the usual system of care of chronically critically ill patients discharged from the acute care setting on readmission rates and other important outcomes (Daly et 59 al., 2005). Patients were recruited from the Surgical, Medical, Neurosurgical, and Cardiac Intensive Care Units of a 950-bed tertiary care hospital in the Midwest. Patients in the parent study were enrolled between March 2001 and December 2004 and met the following criteria: mechanical ventilation greater than 72 hours, 18 years of age or older, English speaking, presence of primary caregiver, lived within 80 miles of the enrollment site, and did not require chronic mechanical ventilation prior to the index admission. Also, patients in a hospice or organ transplant program were excluded since it was assumed that they were already receiving disease management and/or support services. All patients who met these eligibility criteria were enrolled and when hospital discharge was expected, the patient and primary caregiver were approached for consent. Forms were completed by the research team at time of enrollment, hospital discharge and at 2 months post-discharge. Table 1 shows the variables, measures and time points at which data were obtained in the parent study. Power Analysis Power is the probability that a statistical test will detect a significant difference that exists and is calculated to minimize a Type II error. Effect size (e.s.) is the extent or degree to which the phenomenon under study is present in the population (Burns & Grove, 2001). Effect size varies by the population under study so the most desirable source of effect size is evidence from previous studies. In the parent study with 8 covariates, a pre-study level of significance of 0.10, nondirectional hypothesis and desired power of 90%, a medium effect size was determined with a projected sample size 60 Table 1 Parent Study Variables and Time Points Variable Measure Time point Enrollment Symptom burden Responses to open-ended Discharge 2 months X question Symptoms SF-8 questions 4,5, 7 X Functional status OASIS X Age Enrollment form X Gender Enrollment form X Ethnicity Enrollment form X Length of stay Enrollment form # of preexisting Enrollment form X X X conditions Pre-admission health Pre-SF8 X status Note. SF-8 = Medical Outcomes Study Short Form SF-8 with recall period 24 hours prior to discharge; Pre-SF8 = SF-8 with recall period of 4 weeks prior to admission 61 of 256. Nelson and colleagues (2006) used multiple regression to predict the contribution of days in coma or delirium to functional status and other outcome variables. The six covariates of age, sex, ethnicity, Charlson Comorbidity Index, APACHE, and number of comorbid conditions were controlled. Effect size was not pre-calculated but a significance of p = .004 with a sample size of 230 was reported. Large effect sizes in regression analysis of the relationship between symptoms and functional status were reported by Reishtein (2004) in 100 COPD patients (e.s. = .79) and by Dodd and colleagues (2001) in their study of 93 patients with cancer (e.s. = .92). Though these studies found large effect sizes, a more conservative medium effect size was used in this study since this was an exploratory analysis of the relationship between symptom burden and functional status in a new population. In addition, a sub-sample of this study’s data base, patient self-responders only (n = 103), was used to test the relationship between symptom burden and functional status. Proxy responders comprised 28.2% (n = 65) of the sample of 194 and 19% or 45 patients did not complete the 2 month study period, predominantly due to death. Thus, functional status data were missing for these subjects. The use of patient self-responder data, only, minimized the effect of proxy data and increased the chance of finding a statistically significant relationship between symptom burden and functional status. Until recently, the focus in nursing research was predominantly on the level of significance and controlling for Type I errors rather than Type II errors (Burns & Grove, 2001). Type II error was a greater concern in this study and can occur when a small sample size is present, the level of significance is too strict, or a small difference exists in the measured effect. A specific aim of this study was to explain the relationship between 62 symptom burden and functional status. Except for the one study in the effect of brain dysfunction on functional status (Nelson et al., 2006), this relationship had not been tested in the CCI. More evidence is needed regarding the effect of any symptom or cumulative effect of multiple symptoms over a prolonged period on functional status in the CCI. An alpha level of 0.10 was used in this analysis to minimize the error of missing a statistically significant relationship, if present. Thus, in the hierarchical regression model with 8 independent variables and a medium effect size of 0.15, the sub-sample of 103 patient self-responders had 86% power to detect significance at alpha = 0.10 and critical F of 1.74 (Erdfelder, E., Faul, R., & Buchner, A., 1996). Instruments Open-ended Question Data from four instruments were used in this analysis: the open-ended question on the investigator developed preference elicitation tool, the Medical Outcomes Study Short Form-8 (SF-8), the OASIS, and the parent study’s enrollment form. The investigatordeveloped, open-ended question (Appendix A) was used in the parent study to elicit symptoms. Advanced practice nurses conducted interviews within 24 hours of discharge from the hospital. Respondents were instructed to identify and rank, if possible, the most bothersome symptoms. The open-ended question was an untested instrument and therefore no psychometrics were available. However, data derived from the question, “what symptoms are most bothersome at this time?” were used as the measure of symptom burden for several reasons. First, this single-item allowed the CCI patients, who at discharge from the ICU are still quite debilitated and fragile, to easily describe symptoms meaningful to 63 them. The focus and construction of an ideal tool for measuring symptom burden should allow respondents to report the most prevalent and distressing symptom and assess the person’s perception of the collective impact of symptoms, i.e, the burden (Cleeland & Reyes-Gibby, 2002). The tool used in the parent study met these criteria and was consistent with the study assumption that symptom burden is a subjective phenomenon. Second, this single-item open-ended question did not limit the subject’s choices to a predetermined symptom list, as occurs with more traditional instruments. Finally, since the single-item question was developed by investigators with extensive experience in chronic critical illness research and since there was adequate variability in responses, face validity was present. The validity and reliability of single-item indicators as compared to multiple-item scales has been established in studies examining quality of life, fatigue, emotional well being and other concepts (Bernhard, Sullivan, Hurny, Coates, & Rudenstam, 2001; Bowling, 2005; Cole, Kawachi, Maller, & Berkman, 2000; de Boer et al., 2004; Hurny et al., 1996; Kirsh, Passik, Holtsclaw, Donaghy, & Theobald, 2001; Spadoni, Stratford, Soloman, & Wishart, 2004). A recent study of patients undergoing chemotherapy (Bernhard, Maibach, Thurlimann, Sessa & Aapro, 2002) found that a single item indicator for overall treatment burden was responsive to the spectrum of reactions, changes or symptoms such patients may experience. It has been reported that the lower reliability of single as opposed to multiple-item scales affects discriminant validity (Bernhard et al., 2002). Discriminant validity is a higher correlation between the measure and the concept under study than between the measure and those concepts not intended to be measured. There is a loss of precision to 64 discriminate among symptoms with single-item indicators. However, this imprecision was less important to the primary aim of this study and was consistent with conceptualization of the nondiscrete and cumulative effect of one or multiple symptoms in the chronically critically ill. Also, the effect on discriminant validity is minimized by larger sample sizes, so the sample size of 103 was expected to reduce this threat. It is well established that symptoms encompass the multiple dimensions of intensity, quality, duration and distress (Dodd et al., 2001; Lenz, Pugh, Milligan, Gift, & Suppe, 1997). The use of the open-ended question, “which symptoms are most bothersome at this time?” allowed respondents to use their own words to describe those dimensions. In this study, symptom prevalence was defined as the frequency of occurrence of specific symptoms in the sample of CCI patients who responded to the open-ended question. Symptom burden variable A burden variable was created for each subject from the responses to the openended question, “what symptoms are most bothersome at this time?” This variable was the total number of symptoms, in the possible range of 0-4 symptoms, as named by the patient self-responder or proxy responder. This burden variable was entered as the predictor variable in the multiple regression analysis. Medical Outcomes Study Short-form 8 (SF-8) The SF-8 (Appendix B) measures overall health status and was derived from the most powerful items of the parent instrument, the SF-36. The SF-36 has been used to estimate the relative impact of varying conditions on physical and mental well-being. Psychometrics are available for the SF-36 and the SF-8. In the SF-8, one single item 65 measures each of the eight domains: physical functioning, social functioning, physical role limitation, emotional role limitations, mental health, energy/vitality, pain, and general health perceptions. Higher scores indicate better health status. Alternative form reliability for summed scores on the SF-8 has been reported to range from 0.85 to 0.90 for one week recall (Ware, J., Kosinski, M., Dewey, J. & Gandek, B., 2001). The single item pain, energy/vitality, and mental health scales were used in this investigation. The rating scale for the pain item is: 1= no pain, 2 = very mild, 3 = mild, 4 = moderate, 5 = severe, and 6 = very severe. The rating scale for the energy/vitality item is: 1 = very much (energy), 2 = quite a lot, 3 = some, 4 = a little, and 5 = none. The rating scale for emotional problems is: 1=not at all, 2=slightly, 3=moderately, 4=quite a bit, and 5=extremely. The agreement between the symptom categories of pain, psychoemotional distress and fatigue, derived from responses to the open-ended question, were compared to responses to the three SF-8 single-item indicators, pain, energy/vitality and mental health in an exploratory analysis to establish criterion-related validity of the single item openended question. The same approach to using items from a well-being or quality of life instrument to obtain symptom data in a secondary analysis was undertaken by Dodd, Miaskowski and Paul (2001) and was previously reviewed in Chapter 2. The use of this particular approach by experts in the field of symptom research lends support to inclusion of a similar approach to measure symptom burden in this sample of the CCI. Also, the extent to which the lack of well-being, as measured by the SF-8, reflects symptom burden can be explored with this method to add to the conceptualization of symptom burden. 66 The physical (PCS-8) and mental (MCS-8) component summary measures of the SF-8 were used in this analysis. First, the responses to the pre-SF-8, obtained after consent and just prior to hospital discharge, were used as the measure of preadmission health status. The recall time frame for the pre-SF-8 was the 4 weeks prior to hospitalization. The PCS-8 and MCS-8 were entered as the sixth and seventh covariate in the regression analysis of the relationship between symptom burden and functional status (research question two). SF-8 data were also used in the exploratory analysis of the agreement between symptom categories derived from responses to the open-ended question and responses to the three single-item indicators on the SF-8: pain, energy/vitality and mental health. That is, the extent to which the report of pain, fatigue and/or emotional problems such as feeling anxious or depressed on the open-ended question matched the responses on the SF-8 obtained at discharge from the index hospitalization. Functional Status: OASIS Functional status was defined as the level of independence in activities of daily living (ADL) and was measured by the OASIS. The OASIS was first developed by Shaughnessy and colleagues (1994) for the purposes of measuring outcomes for adult home health care patients and for quality improvement activities. Use of the OASIS was mandated by the Centers for Medicare and Medicaid Services in 1999 and is now used extensively for reimbursement, clinical, and research purposes (Madigan & Fortinsky, 2004). OASIS data is to be collected by home health agency staff upon admission to the agency, discharge from the agency, at the 60-day recertification period, and when there is a significant change in condition including hospitalization. 67 The OASIS consists of 107 items that are used to classify patients into home health agency resource groups (HHRG). The three domains of the HHRGs are: clinical severity, functional status, and service utilization. Functional status includes difficulty related to dressing, bathing, toileting, transferring, and mobility. Kappas for interrater reliability ranging from .50 to 1.0 for functional status items were reported by Shaughnessy et al. (1994). Some discrepancies, especially in the behavioral and clinical items, were reported in a video simulation to test the accuracy of OASIS completion by nurses and therapists (Madigan, Tullai-McGuinness, & Fortinsky, 2003). Construct validity is available and the reliability of the functional domain of the OASIS has been reported with coefficient alpha ranging from .86-.91 and inter-rater kappa’s ranging from .67-1.0 (Madigan & Fortinsky, 2000). The parent study used 17 items from the OASIS as an assessment of functional status in the CCI upon discharge from the index hospitalization. All items retained their original format. This shortened OASIS (Appendix C) included an assessment of risk factors, need for supportive assistance, presence of open wounds and pressure ulcers, respiratory treatments, activities of daily living and instrumental activities of daily living (ADL/IADL), number of oral and IV medications, and ability to use the telephone. This tool was completed upon the patient’s discharge from the hospital regardless of discharge disposition as a measure of resource use and functional status. The range of score on the functional status items was 0-66 with higher scores indicating greater functional dependence. The functional status variable for this study was derived from the parent study’s OASIS items. This variable was the total score computed from the raw scores of eight 68 OASIS variables recognized to measure ADL: grooming, dressing upper body, dressing lower body, bathing, toileting, transferring, ambulation and eating. The possible range of this functional status variables was 0-33 with higher scores indicating greater dependency in functional status. Enrollment Form The fourth and final instrument used in this study was the enrollment form from the parent study (Appendix D). This form was used to extract chart data from enrolled patients and included the following: demographic data, APACHE scores, number of preexisting conditions, number of home medications, date of admission and discharge, and discharge disposition. Procedure The two steps of procedure, the parent study and the proposed study, will now be explained. In the parent study, research nurses screened all ICU patients, daily, for study eligibility. All patients who met the study criteria were enrolled and the enrollment form completed at that time. Patients and family members were approached for consent when it was indicated that hospital discharge would take place within a few days. Patients were randomly assigned to the experimental group or disease management program or the control group which was considered usual care. For experimental patients, the openended question, SF-8, OASIS, and other tools were administered by an advanced-practice nurse (APN) within 24 hours of discharge from the index hospitalization. At the end of the eight week intervention period, the APNs once again administered the tools to complete data collection. 69 The procedure in this study was as follows. A new data base was created with the variables under study. The relevant variables from the parent study’s data base were added to this data base. The response to the open-ended question for each subject was extracted from the respective study chart. This raw data was reduced to meaningful symptom categories, i.e., the response of “pain” and “ache” was subsumed into the pain category, and the number of symptoms from 0-4 for each subject was calculated. The symptom burden variable for each subject was calculated from this data. Data cleaning methods were used to ensure the accuracy of this step in the procedure. Missing data for the variables under study from the parent study, though minimal, were identified. Analysis For research question one, what is the symptom burden in the CCI as measured by the most prevalent symptoms and number of symptoms in response to the open-ended question, “what symptoms are most bothersome at this time?” data reduction and then descriptive analysis were completed. Univariate statistics were run to describe the prevalence of symptoms in the sample. The data were examined for trends and similarities and differences among subgroups, such as gender and age. Hierarchical multiple regression was used to answer research question two, to what extent does symptom burden upon discharge from the index hospitalization predict functional status at 2 months after discharge, when controlling for age, gender, ethnicity, length of hospital stay (LOS), number of preexisting conditions, and preadmission health status (PCS-8 and MCS-8 of the pre-SF-8). In step one, the seven covariates were entered into the regression model as a block; and then the independent variable of symptom burden was added. 70 The analysis of research question three, what is the criterion-related validity of the open-ended question measuring symptom burden as compared to the SF-8, was exploratory only. The degree of agreement between symptom categories derived from responses to the open-ended question and the three SF-8 single item scales of pain, energy/vitality, and mental health was analyzed. In order to use the kappa statistic, responses to the three SF-8 items were categorized with 0=absent and 1=present. Second, the correlation between the symptom burden variable and the overall score of the SF-8 at discharge was analyzed using Pearson’s r. 71 Chapter IV Results The underlying premise of this study was that the prolonged nature of chronic critical illness causes a diffuse, nonspecific symptom experience making it difficult for the patient or caregiver to differentiate symptoms or report the most bothersome. It was hypothesized that the patient’s response to a single question could be an alternative approach to symptom burden assessment. Thus, the purpose of this study was to gain a better understanding of symptom burden and the role it plays in the post-discharge experience of the CCI. Given the prolonged nature and resource-intensiveness of the syndrome of chronic critical illness, it was important to understand the cumulative effects on symptom burden and the effect of this burden on functional status. The study model found in Figure 2 explains the specific variables of symptom burden and functional status, covariates, and hypothesized relationships examined in this investigation. The relationship between symptom burden and functional status was analyzed using multiple regression. The independent variables of age, gender, ethnicity, length of stay, number of preexisting conditions, and preadmission health status were controlled for in the multiple regression analysis. In this chapter, demographics of the sample will be presented, followed by findings specific to the three research questions. The research questions were: 1. What is the symptom burden in the CCI as measured by the most prevalent symptoms and number of symptoms in response to the open-ended question, “what symptoms are most bothersome at this time?” 72 2. To what extent does symptom burden upon discharge from the index hospitalization predict functional status at 2 months after discharge, when controlling for age, gender, ethnicity, length of stay, number of preexisting conditions, and preadmission health status? 3. What is the criterion-related validity of the open-ended question measuring symptom burden as compared to the Medical Outcomes Study Short Form (SF-8): a. What is the degree of agreement between responses to the open-ended question and the SF-8 question, “how much bodily pain have you had during the past 24 hours?” b. What is the degree of agreement between responses to the open-ended question and the SF-8 question, “during the past 24 hours, how much energy did you have?” c. What is the degree of agreement between responses to the open-ended question and the SF-8 question, “during the past 24 hours, how much have you been bothered by emotional problems (such as feeling anxious, depressed or irritable)?” Description of the Sample The sample used for analysis was drawn from all 231 patients randomized into the experimental group of the parent study. The experimental patients received the disease management intervention. Figure 3 shows the distribution of the sample including the number of patient self-responders and proxy responders and the number of patients who remained in the parent study for the entire 2 month post-discharge period. Of the 231 experimental patients, 129 (55.8%) were able to self report symptoms, 65 (28.2%) were unable to complete study forms and therefore symptom reports were by proxy, and 37 (16%) had a missing or blank open-ended symptom question. Descriptive analysis to answer research question one was conducted on the combined sample of patients (n = 73 194), those who were able to self-report symptoms and those for whom proxies reported symptoms in response to the open-ended question. Some experimental patients did not complete the 2 month study period and therefore functional status data at 2 months were not available. Forty-five (19%) patients (23 patient responders, 22 proxy responders) did not complete the 2 month intervention due to death, study withdrawal or were lost to follow up. Covariate data were missing for three additional patients. Thus, a final sample of 103 patient self responders, with responses to the open-ended question and 2 month functional status, was derived from the original sample and used for multiple regression analysis to answer research question two. Comparison of Subjects with Symptom Data and Subjects with No Symptom Data The difference in demographic and key variables between the subjects from the parent study for whom symptom data were available (n = 194) and for those subjects with missing or no data (n = 37) was analyzed. Of the 37 cases missing symptom data, 14 were patient self responders and 23 were in the proxy responder group. There was no significant difference in age, gender, ethnicity, number of preexisting conditions, length of hospital stay, or preadmission health status as measured by the physical component summary (PCS-8) and mental component summary (MCS-8) of the SF-8. There was a significant difference between subjects with symptom reports (n = 194) and without symptom reports (n = 37) in mean functional status at discharge (19.48 vs. 25.89; t = -4.06; p = < .001) and mean functional status at 2 months (9.59 vs. 16.34; t = -3.01; p = .003). At both time points, functional status was worse for the group with no symptom reports. Due to the skewness of the functional status variable, parametric and 74 nonparametric tests (Mann Whitney U) were compared and yielded the same result in statistical significance for functional status. In addition, the difference in key variables was examined between the patient selfresponders who completed the 2 month study period and those who did not. Those patients who completed the 2 months were younger (57.6 vs. 68.6 years; t = 2.91; p = .004) and had fewer preexisting conditions (5.46 vs. 7.39; t = 2.70; p = .008), shorter lengths of hospital stay (22.72 vs. 32.35 days; t = 2.71; p = .008) and better functional status at discharge (13.4 vs. 21.8; t = 3.50; p = .001). These differences were statistically significant. There was no significant difference in gender, ethnicity or preadmission health status. As shown in Figure 3, death was the most common reason why a patient did not complete the 2 month study period. Demographics of the Total Sample, Patient Self-responder Group, and Proxy Group Table 2 displays the demographics of the total sample and the comparison of key variables between patients able to self-report symptoms, the patient self-responder group, and patients for whom proxies reported symptoms at hospital discharge, the proxy responder group. Subjects in the combined sample (n = 194) had a mean age of 60.9 years, reported 5.6 preexisting conditions and were predominantly female (54.1%) and Caucasian (66.5%). The patient self-responder group was slightly younger (59.5 years) 75 Experimental patients N = 231 6 missing open ended question (OEQ) n = 225 10 blank OEQ~ Patient responders n = 129 Did not complete 2 mo: 23 Died = 20 Dropped = 2 Lost to follow up = 1 21 blank OEQ~ Proxy responders n = 65 Did not complete 2 mo: 22 Died = 18 Dropped = 4 Lost to follow up = 0 Missing covariate data =3 Symptom responses at discharge and FS* at 2 months n = 103 Symptom responses at discharge and FS* at 2 months n = 43 Figure 3. Sample distribution. Abbreviations include: ~OEQ=open ended question; * FS= functional status. 76 and reported slightly more preexisting conditions (5.81), lower symptom burden (1.81) and lower preadmission physical health status( 43.12) but higher mental health status (46.12) than the proxy responder group. These differences were not statistically significant. Length of hospital stay was the same (24.4 days vs. 24.1 days), but the length of mechanical ventilation was longer, though not significantly, for the proxy group (13.18 days) when compared to the patient group (10.39). The symptom burden variable was created for each case from responses to the open ended question, “what symptoms are most bothersome at this time?” This variable was the total number of symptoms identified by the patient or proxy responder. The possible range was 0-4. The mean symptom burden in the patient self-responder group was 1.81 as compared to the proxy responder group of 2.03. This difference was not statistically significant. Functional Status The only significant difference between the patient self-responders and the proxy responders was in mean functional status at discharge (14.89 vs. 28.53; t = -11.98, p = < .001) and at 2 months (6.38 vs.17.44; t = -5.29, p = < .001). Due to the skewness of the functional status variable, parametric and nonparametric tests were compared; statistical significance was the same using both methods. The functional status variable was the total score computed from the raw scores of eight OASIS variables: grooming, dressing upper body, dressing lower body, bathing, toileting, transferring, ambulation, and eating. The possible range was 0-33 with higher scores indicating greater dependency in functional status. 77 Table 2 Comparison of Demographics and Key Variables among the Total Sample, the Patient Self-responder Group and the Proxy Responder Group Variable Total sample Patient self- Proxy responder t test p value / x2 responder (n = 194) (n = 129) (n = 65) Mean (SD) Mean (SD) Mean (SD) Age 60.91(16.79) 59.54(16.87) 63.62(16.43) -1.60 .111 LOS*,d 24.33(14.75) 24.44(15.80) 24.11(12.52) 0 .15 .882 LOMV~,d 11.32(10.90) 10.39(10.46) 13.18 (11.6) -1.70 .092 Number of 5.56 (3.35) 5.81 ( 3.17) 5.08 (3.66) 1.43 .156 44 (12.32) 43.12 (12.31) 45.75(12.27) -1.39 .167 45.70(12.95) 46.12(13.86) 44.84(10.95) 0.69 .490 1.89 (1.03) 1.81 ( 0.95) 2.03 (1.16) -1.39 .166 preexisting conditions SF-8 PCS# normed SF-8 MCS# normed Symptom burden 78 Variable Total sample Patient self- Proxy responder t test / x2 p value responder Functional 19.41(11.31) 14.89(10.85) 28.53(4.97) -11.98 <.001 54.1% 52.7% 56.9% 0.31 .579 Caucasian 66.5% 62.8% 73.8% 5.14 .162 African- 32.5% 36.4% 24.6% American 1.0% .8% 1.5% 9.59(11.41) 6.38(9.31) 17.44(12.36) -5.29 <.001 status∞ at discharge Female gender Race: Other Functional status at 2 months (n=148) Note. *length of hospital stay; ~length of mechanical ventilation; ∞ functional statusrange 0-33, higher scores indicate worse functional status; # normed physical and mental component summary score of the Medical Outcomes Study SF-8 form, possible range 867, higher scores = better health 79 More detailed analysis of functional status as it relates to disposition at hospital discharge and at 2 months revealed differences between the patient self responder group and the proxy responder group. In the total sample of 194, 19 (9.8%) patients were discharged home without home care and 27(13.9%) with home care. Eighty five (65.9%) of patient self-responders were discharged to post acute care facilities as compared to 63 (96.9%) of the proxy responder group. The difference between the patient self-responders and proxy responder group in hospital disposition was statistically significant (x2 = 27.60; p = < .001). Disposition of the patient self-responder group and proxy responder group at hospital discharge and at 2 months is displayed in Table 3. At hospital discharge, all 17 (13%) of 129 patient self-responders who were completely independent in functional status or needing assistance with only one activity of daily living (ADL) were discharged to home with or without home care. In comparison, only 2 (3%) of the 64 patients in the proxy responder group were discharged to home. The best functional status at discharge in the proxy responder group was eight, reflecting need for assistance with multiple ADL’s. At 2 months, 42 (32.6%) and 16 (12.4%) patient self-responders had a functional status of complete independence or minimal assistance with only one ADL, respectively. Of the 42 (40%) patient selfresponders completely independent in ADL, all were living at home and only 4 (3.8%) were receiving home care. Of the 16 (15.2%) patient self responders needing assistance with only one ADL, four were in a skilled nursing facility, eight were at home without home care and four were at home with home care. As predicted, the patients in the proxy responder group had better functional status at 2 months than at discharge though this group was still less independent than patients able to self-report. Of the seven (16.3%) in 80 the proxy responder group who had nearly independent function (score 0-2) at 2 months, only one was living at home and receiving home care. All 10 (23.3%) in the proxy responder group who were totally dependent (score 31-33) were institutionalized. Table 3 Disposition of Patient Self-responder and Proxy Responder Group Group Home without Home with Post acute care home care home care facility# Patient self-responder Discharge (n = 129) 19 (14.7%) 25 (19.4%) 85 (65.9%) 2 months (n = 111) 53 (47.7%) 27 (24.3%) 28 (25.2%)* Discharge (n = 65) 0 2 (3.1%) 63 (96.9 %) 2 months (n = 44) 8 (18.2%) 9 (20.5%) 27 (61.3%) Proxy responder Note. # Skilled nursing facilities, rehabilitation facilities, long term acute care hospital; *Does not equal 100% due to other/not applicable category Symptom Prevalence The determination of symptom prevalence was completed in two phases. The first phase consisted of data retrieval, data cleaning, and data reduction or categorization and the second phase consisted of descriptive analysis. In the first phase, the study chart of each of the 231 experimental patients from the parent study was reviewed three times, by this investigator, with appropriate time intervals in between. Verbatim responses to the open ended question, “what symptoms are most bothersome at this time?” on the parent 81 study form, “preference elicitation guide”, were documented and entered into an Excel file. Patient or proxy responder was verified by checking frequencies between the parent study data base and this study’s data base. If a discrepancy occurred, the parent study chart was reviewed again. Symptom Categories Though capturing symptom burden in the actual words of patient responders or proxy responders was important to this study, data reduction was necessary to enable descriptive analysis. Thus, raw data were reduced into meaningful categories through blind review and comparison between this investigator and an expert in palliative care and symptom management. This process was repeated three times until agreement was reached between the two reviewers on the name and meaning of the symptom category and placement of raw data into each category. A category of concerns emerged from the raw data, in response to the open-ended question, which did not fit more typical symptom categorization. Due to lack of agreement between the investigator and the palliative care expert, a third reviewer was solicited to code this “other” category. This reviewer was an expert in oncology and palliative care nursing, ELNEC (End-of-Life Nursing Education Consortium) certified and has substantial experience in managing the CCI. This “other” category was named “concerns/consequences of chronic critical illness”. References to tubes commonly used in the critical care setting, such as tracheostomies, feeding tubes, percutaneous endoscopic gastrostomy (PEG) tubes, drains, and foley catheters, were mentioned by 27 (14%) of the subjects. Unless a specific symptom was associated with the tube, i.e., “trach discomfort”, these references were subsumed into the “concerns/consequences of 82 chronic critical illness” category. Some symptoms, though initially seen as distinct, were merged with other symptom categories due to the low frequency of the particular response. For example, originally sleep disturbances was a separate category but since the frequency was only six of the total sample, it was placed into the “sensory changes” category. Through this process, 11 symptom categories were finalized. Each symptom response by patient or proxy responders in the sample of 194 was placed into one of these 11 categories and entered into the data base. The 11 final symptom categories were: pain, loss of independence, fatigue, respiratory discomfort, communication, psycho-emotional, nutrition related, elimination discomforts, sensory changes, concerns/consequences of chronic critical illness, and impaired cognition. Table 4 shows the frequency of symptom responses in each of the 11 categories and examples of actual symptom responses included in the corresponding category. In response to the question, “what symptoms are most bothersome at this time?” some responders reported two or more symptoms within the same category, such as “shoulder pain” and “abdominal pain” or “anxiety” and “depression”. In these cases, each symptom was added to the total symptom burden variable even if in the same category. The categories in which patients reported two or more symptoms included pain (13 occurrences), loss of independence (1), fatigue (1), respiratory discomfort (4), psychoemotional (1), nutrition (1), and concerns of chronic critical illness (3). The categories in which proxy responders reported two or more symptoms included pain (4 occurrences), loss of independence (2), communication (1), psycho-emotional (2), concerns of chronic critical illness (1), and impaired cognition (1). 83 Symptom Burden The second phase of descriptive analysis had two steps. To compute the symptom burden variable for the 194 cases in which the open ended question was completed, the number of symptoms was added and entered into this study’s data base. Symptom burden of the combined sample and patient self-responders and proxy responders is reported in Table 5. The percent of the total sample, patient self-responders and proxy responders reporting one or more symptoms was 94.8%, 95.3% and 93.8%, respectively. Five percent of the total sample reported no symptoms and 8.8 % reported four symptoms. More patient responders reported two symptoms than proxy responders (39.5% vs. 27.7%) but more proxy responders reported three symptoms than did patients themselves (20% vs. 14%). The mean symptom burden in the proxy group was higher (2.03) than the symptom burden in the patient self-responder group (1.81) though not significantly different. To adequately describe symptom prevalence, the 11 symptom categories were dichotomized into present or absent in the second step of descriptive analysis. For example, for the pain variable, 0 was entered if the subject did not report pain and 1 was entered if the subject did report pain. The same process was repeated for each of the 10 remaining categories. Prevalence of symptoms in the 11 categories as compared between the patient self-responder group and the proxy responder group is presented in Table 6. 84 Table 4 Symptom Categories and Prevalence Symptom category 1. Pain Total # 90 Symptom responses Pain, discomfort, soreness, achiness, tingling, stiffness 2. Loss of independence 39 Inability to care for self, lack of body control, inability to help self, loss of control over ADL, inability to move independently, inability to walk, dependence on ventilator, immobility, disuse, paralysis 3. Fatigue 32 Fatigue, lack of energy, weakness, tires easily, deconditioning 4. Respiratory discomfort 39 Shortness of breath, dyspnea, breathing, cough, congestion, sputum, mucous 5. Communication 23 Difficulty communicating, can’t talk or communicate needs, difficult speech 6. Psycho-emotional 20 Anxiety, depression, worry, panic attacks, nerves, feel hyper, fear, frustration 7. Nutrition related 11 Thirst, dry mouth, hunger, inability to eat, nausea, anorexia, fullness, early satiety, appetite changes 8. Elimination discomforts 15 Diarrhea, constipation, change in bowel function, urinary frequency and fullness 85 Symptom category 9. Sensory changes Total # 16 Symptom responses Visual disturbances, loss of sight, hearing, blurred vision, sleep disturbances, dizziness, rash, pruritis 10. Concerns/consequences of 25 chronic critical illness My general health, my feet, my kidneys, people drawing blood, water retention, arm veins, where are my glasses, foot ulcer, incision drainage, being hospitalized, restraints, feeding tube, tracheostomy, left bumper puss, valve surgery 11. Impaired cognition 17 Impaired memory, awareness, confusion, groggy, can’t read, foggy Pain was reported by almost half of the patient self-responders (48.1%) and proxies (43.1%).The most prevalent symptoms for patients able to self-report were pain (48.1%), respiratory discomfort (24%), fatigue (23.3%) and concerns/consequences of chronic critical illness (11.6%). The most prevalent symptoms for patients requiring proxy report of symptoms were pain (43.1%), loss of independence (32.3%), communication barriers (26.2%), and impaired cognition (16.9%). The greatest discrepancy between patient responders and proxy responders was in the category of fatigue. Thirty (23.3%) patients reported fatigue whereas only 2 (3.1%) proxies reported fatigue in the patient. There was a statistically significant difference in the prevalence of 86 fatigue, loss of independence, communication and impaired cognition between the patient and proxy groups. Analysis of the difference between the patient self-responder group and the proxy responder group in the effects of age, gender and ethnicity on symptom burden was also completed. There was no significant difference in symptom burden between the two groups based on age (younger = ≤ 64 years; older = ≥ 65 years). Likewise, there was no significant difference in symptom burden based on gender. However, there was a significant difference in symptom burden between Caucasians (2.05) and non-Caucasians (1.55) but only in the patient self-responder group (t = 2.56; p = .012). Table 5 Symptom Burden of the Total Sample, Patient Self-Responders and Proxy Responder Group # of Symptoms Total sample Patient Proxy (N = 194) (n = 129) (n = 65) None 10 (5.2%) 6 (4.7%) 4 (6.2%) One 67 (34.5%) 46 (35.7%) 21 (32.3%) Two 69 (35.6%) 51 (39.5%) 18 (27.7%) Three 31 (16%) 18 (14%) 13 (20%) Four 17 (8.8%) 8 (6.2%) 9 (13.8%) Symptom 1.89 (1.03) 1.81 (.95) 2.03 (1.16) Burden Mean (SD) T test/p value -1.39 / .166 87 Table 6 Prevalence of Symptoms in the Patient Self-responder Group and Proxy Responder Group Category x2 Patient Proxy (n = 129) (n = 65) 62 (48.1%) 28 (43.1%) .43 .511 Loss of independence 18 (14%) 21 (32.3%) 9.07 .003* Fatigue 30 (23.3%) 2 (3.1%) 12.78 <.001* Respiratory discomfort 31 (24%) 8 (12.3%) 3.70 .054 6 (4.7%) 17 (26.2%) 19.12 <.001* Psycho-emotional 13 (10.1%) 7 (10.8%) .02 .881 Nutrition related 7 (5.4%) 4 (6.2%) .04 .836 Elimination discomforts 12 (9.3%) 3 (4.6%) 1.33 .249 Sensory changes 9 (7.0%) 7 (10.8%) .82 .365 15 (11.6%) 10 (15.4%) .54 .461 6 (4.7%) 11 (16.9%) 8.14 .004* Pain Communication Concerns/consequences of p value chronic critical illness Impaired cognition Note. *p < .05 To determine if any clustering occurred between pain and the other 10 symptom categories, nonparametric testing for nominal data was completed using the Phi statistic. There were significant correlations between pain and the symptom categories of independence, fatigue and respiratory discomfort. The correlations between pain and the remaining seven symptom categories were not statistically significant. 88 Relationship between Symptom Burden and Functional Status The relationship between symptom burden and functional status was examined using descriptive statistics, correlations and multiple regression for the sample of 103 patient self-responders who completed the 2 month study period. Logistic regression was also performed. Preadmission Health Status Based on the study model, it was hypothesized that preadmission health status was one of the six covariates that influenced the relationship between symptom burden and functional status in the CCI. The physical component summary (PCS) and mental component summary (MCS) of the SF-8 were used as a measure of preadmission health status. The SF-8 was completed by patient or proxy in the parent study during the index hospitalization. The recall period for the SF-8 was the 4 weeks prior to admission and was labeled “preSF-8” in the parent data base. As described in Chapter III, the eight single item scales of the SF-8 were developed to measure the eight domains of the SF-36. Raw scores were converted to scaled scores by assigning mean SF-36 scores from 2000 general U.S. population data. After this step, a higher score indicated better health status. The scaled scores were then normed by adding a constant resulting in aggregate PCS and MCS scores that were standardized to have the same mean as SF-36 PCS and MCS in the general population. The normed PCS and MCS scores from the parent data base were entered into the regression equation. Data Cleaning 89 To ensure accuracy of the data prior to conducting the multivariate analysis, missing data, outliers, and univariate normality were analyzed. The regression analysis was run using the 103 patient self-responders for whom all covariates, symptom burden data and functional status at 2 months data were available. Thus, no data were missing. Second, Cook’s D and Mahalanobis distance revealed no influential outliers. The critical value for x2 using 10 degrees of freedom for the total of independent and dependent variables at a p = <.001 was 29.5883. Since case #95 had the highest Mahalanobis distance of 27.538 and this was below the critical value, there were no outliers of concern that needed to be removed from the analysis. Third, histograms, normality plots, and skewness and kurtosis were analyzed to determine univariate normality for the dependent variable of functional status at 2 months and for each of the independent variables. Length of stay was positively skewed and the mental component summary of the SF-8 was negatively skewed. However, data transformation was not necessary due to adequate sample size. Functional Status Analysis of univariate normality of the outcome variable, functional status at 2 months, revealed positive skewness for the patient responder group and a bimodal distribution in the proxy responder group. For the combined sample (n = 194), skewness of functional status at 2 months was 4.40 when divided by the standard error of the skewness. Skewness beyond ± 1.96 indicates that the data are not normally distributed (Munro, 2001). Thus, this variable was not normally distributed. The distribution of 2 month functional status in the patient responder and proxy responder groups is displayed in Figure 4 and 5, respectively. In the patient self-responder group, positive skewness 90 Histogram 60 50 Frequency 40 30 20 10 Mean =6.38 Std. Dev. =9.306 N =105 0 0.00 10.00 20.00 30.00 40.00 fs_tot2mo Figure 4. 2 month functional status of patient self-responders Histogram 12 10 Frequency 8 6 4 2 Mean =17.44 Std. Dev. =12.358 N =43 0 0.00 10.00 20.00 30.00 40.00 fs_tot2mo Figure 5. 2 month functional status in the proxy responder group. 91 existed due to the high number (42/103) of patients with functional status scores of 0, complete independence, and 16 with functional status of 1, needing assistance with only one ADL. Functional status in the proxy responder group (n=43) was bimodal due to higher incidences of nearly independent function (score 0-2) or total dependence (score 31-33). In addition, functional status at discharge for the combined sample was also positively skewed with a calculated skewness of -2.75. Regression Diagnostics Prior to running multiple regression, correlations and regression diagnostics were examined. The number of preexisting conditions and age had the strongest significant positive correlation with 2 month functional status (r = .36, p = .000 and r = .25, p = .005, respectively). The physical component summary (PCS) of the pre SF-8 had the strongest negative correlation with functional status at r -.37 (p ≤ .001). The correlation between symptom burden and functional status was in the negative direction; that is, as symptom burden increased the functional status improved. This relationship, though not statistically significant (r = -.11, p = .135) was an unexpected finding. The mental component summary (MCS) of the SF-8 was also negatively correlated with functional status but not significantly. Length of stay had a positive but non-significant correlation with functional status. Correlations can be found in Table 7. 92 Table 7 Correlation between Independent Variables and Functional Status at 2 Months Variable r p value Age in years .25 .005* Number of preexisting conditions .36 < .001* - .11 .135 Symptom burden variable Length of hospital stay .059 .278 PCS/preSF8∞ -.37 < .001* MCS/preSF8~ -.05 .296 Note. ∞PCS=physical component summary; ~MCS=mental component summary;* p <.05 The critical assumptions of independence, zero mean, and normal distribution of the dependent variable must be met prior to running the multiple regression. Thus, regression diagnostics were analyzed. Independence indicates that the value of one residual does not influence another; in other words, the error term of one subject must not be influenced by another subject. The Durbin-Watson in the Model Summary was 2.094 and, thus independence was not violated. The second critical assumption, zero mean, was also met as there was a constant in the equation in the Coefficients Table. The dependent variable, functional status at 2 months, was positively skewed. The property of equal variance between residuals is important, though not critical. Scatterplot analysis supported the presence of homoscedasticity or equal variance. Collinearity and the presence of influential data points were also examined. Collinearity is not a residual assumption but is an important step in regression analysis. It reveals whether independent variables are highly related or multicollinear to the point of 93 essentially measuring the same variables and thus limiting R square. Tolerance, the proportion of the independent variable not accounted for by other independent variables in the equation, ranges from 0-1 and should be greater than 0.2. The reciprocal of tolerance, the variable inflation factor (VIF) should be below 10 to avoid multicollinearity. Multicollinearity was not present in this equation as evidenced by the lowest tolerance of .676 for the number of preexisting conditions and the highest VIF of 1.278 also for preexisting conditions. The scatterplot showed that values were consistently spread out indicative of multivariate normality and homoscedasticity. Two cases, # 95 and #140, were greater than ±3 standard deviations from the mean functional status at 2 months. These cases were left in the analysis since Cook’s D and the centered leverage value indicated that these were not influential. Regression results Hierarchical multiple regression was conducted to determine if the variable of symptom burden predicted functional status at 2 months in the CCI, when controlling for age, gender, ethnicity, number of preexisting conditions, preadmission health status, and length of stay. The six covariates were entered in the first step. These variables predicted 18.3% of the variance in functional status at 2 months (adjR2 = .18; F = 4.26; p = <.001). Review of the beta weights revealed that only two variables, the physical component summary (PCS) of the pre SF-8 β = -.31, t = -2.95 and the number of preexisting conditions β = .21, t = 1.97, significantly contributed to the model. Addition of symptom burden in step two of the regression model explained an additional 6.9% of the variance in functional status and did not make a significant contribution to functional status (R2 change = .004; F change = .51; p = .479). The overall model was significant in predicting 94 17.9% of the variance in functional status at 2 months (F = 3.77; p = .001). Bivariate and partial correlation coefficients can be found in Table 8. A summary of the regression model is presented in Table 9. Although the study model did not include the covariate of functional status at discharge, regression analysis was run with this variable as a seventh covariate. The overall model accounted for 42.3% of variance in functional status at 2 months (R2 = .47, R2adj = .42, F = 9.30, p = < .001). Symptom burden did not make a significant contribution to the overall model, adding only .2% of variance in the model and explaining only 4.5% of variance in functional status (R2 change = .002, F change = .301, p = .585, Beta = -.045). Examination of beta weights revealed that three variables, number of preexisting conditions (β = .16, t = 1.86), physical component summary (β = -.17 , t = -1.92), and functional status at discharge (β =.56, t = 6.44) significantly contributed to the model. Discharge functional status made the largest single contribution to 2 month functional status. 95 Table 8 Coefficients for Final Regression Model with 2 Month Functional Status as Dependent Variable Variable β Beta t Bivariate r Partial r standardized Age .082 .146 1.474 .253 .150 Gender 1.47 .079 .865 .146 .088 Ethnicity -.963 -.051 -.533 -.079 -.055 LOS1 .049 .077 .822 .059 .084 Preexisting2 .622 .207 1.972* .359 .198 PCS3 -.229 -.306 -2.949* -.365 -.290 MCS4 .048 .073 .733 -.053 .075 Symptom -.665 -.069 -.710 -.110 -.073 burden Note. * p ≤.10; 1 = length of stay; 2 = number of preexisting conditions; 3 = physical component summary score of the SF-8; 4 = mental component summary of the SF-8 Table 9 Model Summary Step R R2 R2adj ΔR2 F p df 1 .489 .239 .183 .239 4.261 <.001* 7 2 .493 .243 .179 .004 3.772 .001* 8 Note. *p < .05 96 Logistic Regression Since the outcome variable, functional status at 2 months, was positively skewed in the patient self-responder group and since square root transformation could not correct the deviation from normal, logistic regression was performed. All raw 2 month functional status scores of 0 were recoded into the dichotomized value of 0 (no functional impairment) and scores of 0.1 – 33 were recoded into the value of 1 (presence of functional impairment). Logistic regression was conducted to determine if symptom burden, age, gender, ethnicity, length of stay, preadmission health status (PCS-8 and MCS-8), and number of preexisting conditions were predictors of functional impairment in the CCI two months after discharge from the index hospitalization. Data were screened to check for outliers and multicollinearity. Case #95 was the only outlier due to a length of stay of 102 days. The lowest tolerance was 0.76 for the number of preexisting conditions; thus, no multicollinearity existed so all independent variables were retained in the model. Regression results were identical with and without outlier #95 so the case was retained in the final analysis. Results indicated that the overall model fit of eight predictors was fair (-2 Log Likelihood = 103.25) and statistically reliable in classifying patients into the two groups of no functional impairment or presence of functional impairment (x2 = 35.23;p = < .001). The model correctly classified 74.8% of the cases or 24.8% above that expected by chance alone. Wald statistics showed that age, ethnicity and the number of preexisting conditions were significant predictors of functional impairment. The odds ratios indicated small changes in the likelihood of developing functional status impairment. For every one year of age, there was a 4.6% increase in the likelihood of functional impairment. For 97 every additional preexisting condition, there was a 23.6% increase in the likelihood of functional impairment. Caucasians were 80% less likely to have functional status impairment than non-Caucasian subjects. A summary of the logistic regression results is found in Table 10. Criterion-related Validity of the Single-item Open-ended Question Criterion-related validity of the open-ended question was evaluated in two steps. First, correlations were run between the symptom burden variable, derived from responses to the open- ended question, and the raw total score of the discharge SF-8. Then, correlations were completed between three categories derived from the open-ended question and the raw scores of the single-item scales of the discharge SF-8 most specific to symptoms. These are the scales of bodily pain, “how much bodily pain have you had during the past 24 hours?”, vitality, “during the past 24 hours, how much energy did you have?”, and , mental health, “during the past 24 hours, how much have you been bothered by emotional problems (such as feeling anxious, depressed or irritable)?” In the parent study, responses to the open-ended question and the discharge SF-8 were obtained within 24 hours of the patient’s discharge from the index hospital stay. The raw SF-8 score was computed by adding the raw score of each SF-8 single item scale. The range was 0-42 with higher scores indicating worse health status. The mean for the sample was 30.19 (SD 6.52). Patients who were unable to complete study forms, therefore, requiring proxy reports (34.42; SD 4.31) had significantly worse health status as compared to patients able to respond (28.05, SD 6.42; t = -8.168; p = <.001). The unweighted kappa statistic was used to measure agreement between the symptom categories of pain, fatigue and psycho-emotional problems, derived from responses to the open-ended question, 98 Table 10 Logistic Regression Results Variable Wald p value Exp(B) Symptom burden 1.308 .253 1.370 Age 7.544 .006* 1.046 Male gender 1.977 .160 .504 Caucasian ethnicity 6.259 .012* .209 Length of stay 2.004 .157 1.027 # Preexisting conditions 3.770 .052* 1.236 Physical Component Summary – SF-8 2.64 .104 .963 Mental Component Summary – SF-8 .823 .364 .982 Note. * p < .10 and the pain, vitality and mental health questions of the discharge SF-8. The unweighted kappa was used since the amount of agreement and not the amount of disagreement is more important in this study. These steps were completed on the combined data base of 194 patient and proxy responders. The three SF-8 scales were dummy coded prior to running the kappa statistic. The SF-8 pain scale has 6 responses from “none” to “very severe pain”. The SF-8 mental health scale has 5 responses from “not at all” to “extremely” bothered by emotional problems. The SF-8 vitality scale has 5 responses from “very much energy” to “none” or “no energy”. Thus, the SF-8 vitality scale captures the reduction in or absence of energy rather than the presence of fatigue. Responses to this study’s open-ended question such as “fatigue”, “lack of energy”, and “feeling tired” were placed in the symptom category of 99 fatigue. Since the presence of fatigue was more consistent with the presence of pain or emotional problems, presence of fatigue was used. Thus, if the subject’s response on the SF-8 was “very much, quite a lot, some or a little energy”, fatigue was coded as not present. If the response was “no energy”, fatigue was coded as present. Each of the three SF-8 variables were recoded to 0 = no presence of the symptom and 1 = presence of the symptom. Frequencies were checked between the original variable and the dummy coded variables and all matched. Agreement was strongest, though at a low level, between the pain category and the SF-8 pain scale across the combined group (kappa = .247). For patient selfresponders, the kappa was .245 and for the proxy responder group the kappa was .256. All were statistically significant. In the combined group of 193 subjects, 118 were classified the same in response to the presence of pain. Agreement was very low and not statistically significant between the fatigue category and the SF-8 vitality scale and between the psycho-emotional category and the SF-8 mental health scale. In the combined patient and proxy group, 64 of 193 were classified the same for psychoemotional problems. The greatest discrepancy occurred in the fatigue category. Thirty patient self-responders and only 2 proxy responders reported fatigue in response to the open-ended question. In comparison, 146 subjects from the total sample of 193 reported some, a little or no energy to the SF-8 vitality item. Thus, subjects were more likely to report loss of energy on the SF-8 than report fatigue as the most bothersome symptom to the open ended question. Complete kappa results can be found in Table 11. 100 Table 11 Agreement between Three Symptom Categories and Corresponding Items of the SF-8 Symptom category Combined sample Patient self-responder Proxy responder (n = 193) (n = 128) (n = 65) Kappa p Kappa p Kappa p Pain .247 <.001* .245 .002* .256 .008* Fatigue -.053 .438 .053 .548 -.061 .211 Psycho-emotional .033 .24 .031 .430 .033 .294 Note. * p < .05 The symptom burden variable and the SF-8 at discharge had a small but positive correlation at a significant level (r = .173, p = .016) for the combined sample. The symptom burden of both the patient self-responder (r = .128, p = .151) and the proxy responder group (r = .207, p = .098) were positively correlated with health status at discharge as measured by the SF-8, but the separate correlations were not significant. The mental health single-item scale of the SF-8 had the largest positive correlation at a significant level with the symptom burden variable for the combined sample (r = .174; p = .016) and for the patient self-responder group (r = .190; p = .031). Though small, the only negative correlation was found between the symptom burden variable and the vitality scale of the SF-8 in the proxy group (r = -.008; p = .947). Bivariate correlations between the symptom burden variable and the total raw score, pain, vitality and mental health scales of the discharge SF-8 are displayed in Table 12. 101 Table 12 Correlation between Symptom Burden and the SF-8 Variable Total sample Patient self-responders Proxy responders (n = 193) (n = 128) (n = 65) r p value r p value p value SF8_total raw score .173 .016* .128 .151 .207 .098 SF8_pain .098 .174 .039 .666 .204 .103 SF8_vitality .077 .286 .079 .377 - .008 .947 SF8_mental health .174 .016* .190 .031* .092 .467 r Note. *correlation is significant at the .05 level (2-tailed) Additional Exploratory Analysis In order to more richly describe symptom burden in the CCI, additional analyses were performed in three areas. The difference in symptom burden and functional status as related to ventilator status at discharge was examined. The differences in demographic and key clinical variables between subjects with low and high symptom burden were analyzed. Finally, the difference in functional status between patients with and without symptoms in the 11 symptom categories was examined. Effect of Ventilator Status In the sample of subjects for whom symptom data were obtained at hospital discharge (n = 194), the mean number of days on mechanical ventilation for the combined sample, patient self-responder group and proxy responder group was 11.32 (SD10.90), 10.39 (SD 10.46), and 13.18 (SD 11.60), respectively. Twenty seven (13.9%) patients were discharged on the ventilator and 57 (29.4%) were discharged 102 with a tracheostomy. Though the patient self-responder group had an average of three less days on the ventilator during the index hospitalization, this difference was not statistically significant. Eighteen (27.7%) of the subjects in the proxy responder group were discharged on the ventilator as compared to nine (7%) of those in the patient-self responder group. The nine patient self-responders on the mechanical ventilator at discharge reported a relatively low symptom burden of either one or two symptoms, only. Proxy responders for the 18 subjects discharged on the ventilator reported a wider range of symptoms from one subject with no perceived symptoms, 11 with one or two symptoms, and six with a higher symptom burden of three or four symptoms. Due to concerns about the skewness of the functional status variables, parametric and nonparametric tests were run. The Mann-Whitney U test, though not as powerful as its parametric equivalent, should be used when data violate the underlying assumptions, especially normal distribution (Munro, 2001). Thus, the Mann-Whitney was run to examine the differences in functional status and symptom burden between patients on and off the ventilator at discharge. Ventilator status made a significant difference in functional status at discharge (Mann Whitney U rank = 547.5; p = <.001) and in functional status at 2 months (Mann Whitney U rank = 165.5; p < .001) but there was not a statistically significant difference in symptom burden (Mann Whitney U rank = 2163.5; p = .724). To minimize the risk of a Type I error, a Bonferroni correction was made by dividing the level of significance (p = .05) by the number of pairwise comparisons (2). Thus, the probability of .025 must be achieved to be significant. Even with this more stringent application, the difference in functional status associated with ventilator status was still significant. Further analysis showed that the only other 103 variable in which ventilator status at discharge made a significant difference was in the number of preexisting conditions (Mann Whitney U rank = 1483.5; p = .005) Levels of Symptom Burden The difference in clinical and demographic variables between subjects reporting low symptom burden (0, 1, 2) and high symptom burden (3,4) was analyzed. Twentysix (20.2%) of the 129 patient self-responders reported a high symptom burden as compared to 22 (33.8%) of the 65 subjects in the proxy responder group. The high symptom burden group was younger (59.77 vs. 61.28 years), predominantly female (52%) and Caucasian (81%) as compared to the low burden group. The high burden group had fewer preexisting conditions (5.45 vs. 5.60), lower SF-8 physical component scores (43.13 vs. 44.27) and lower SF-8 mental component scores (44.57 vs. 46.06) but longer length of hospital stay (26.50 vs. 23.62) and greater number of days on mechanical ventilation (12.75 vs. 10.86). Also, functional status at discharge was slightly worse in the high symptom burden group as compared to the low symptom burden group (20.29 vs. 19.12) but better at 2 months (8.47 vs. 9.98). None of these differences between the high and low symptom burden groups were statistically significant using parametric, when appropriate, or nonparametric tests, though the number of ventilator days and the length of hospital stay would be clinically significant. Table 13 shows the differences in key variables between subjects with low and high symptom burden. 104 Table 13 Differences in Key Variables between Subjects with Low and High Symptom Burden Variable Symptom burden(SB) Statistic Low SB1 (n = 146) t test/MannWhitney U p value High SB2 (n = 48) Mean age (years) 61.28 t .540 .590 3475.0 .931 3397.5 .919 3455.5 .885 t .548 .585 3213.0 .760 3305.5 .602 1988.0 .649 59.77 Mean LOS3 (days) 23.62 26.50 Mean # preexisting 5.60 conditions 5.45 Mean days on 10.86 ventilator 12.75 PCS-SF8 44.27 43.13 MCS-SF-8 46.06 44.57 FS_dc4 19.12 20.29 FS_2 months5 9.98 8.47 Note. 1 Low symptom burden = 0, 1, 2; 2 High symptom burden = 3, 4; 3 length of stay; 4 functional status at discharge; 5 functional status at 2 months; * p < .05. 105 Effect of Symptom Expression on Key Variables For the total sample (n = 194), the difference in functional status at discharge and at 2 months was also analyzed for those patients who expressed a symptom in one of the 11 symptom categories and those patients who did not. The nonparametric Mann Whitney U test was used due to the positive skewness of the functional status variables though results, when compared to the t test, were similar. The association of fatigue and communication barriers with functional status was significant both at discharge and 2 months. There was a statistically significant difference in functional status at discharge but not at 2 months in those who reported loss of independence vs. those who did not. The presence of psychoemotional symptoms made a significant difference in functional status at 2 months but not at discharge. None of the other seven symptom categories, including the presence of pain, had a significant effect on functional status at either time point. When parametric testing was run to compare the difference between the patient self-responders and the proxy responder group in the relationship between functional status and symptom expression, all results were non-significant or the data violated the underlying assumption of normality. Summary Descriptive analysis of the prevalence of symptom burden in the CCI and multivariate analysis of the relationship between symptom burden and functional status when controlling for age, ethnicity, gender, number of preexisting conditions, length of stay, and preadmission health status has been presented. Symptoms were very prevalent in the sample and pain was the most prevalent symptom. The regression model predicted 17.9% of variance in functional status at 2 months. Symptom burden did not 106 make a significant contribution to the variance in functional status. Due to the skewness of the functional status variable, logistic regression was performed. The model fit was fair and statistically reliable in predicting the presence or absence of functional impairment. Age, ethnicity, and the number of preexisting conditions were significant predictors of functional impairment. Criterion related validity was supported, by kappa results, between the symptom category of pain and the SF-8 bodily pain question. The categories of fatigue and psycho-emotional symptoms did not agree significantly with the SF-8 categories of vitality and mental health, respectively. Additional analysis showed that ventilator status at discharge made a significant difference in functional status at discharge and at 2 months but not in symptom burden. Also, there was no significant difference in functional status as related to low or high symptom burden. Finally, the impact of fatigue and communication barriers on functional status was statistically significant. Further discussion will be presented in Chapter V. 107 Chapter V Discussion The symptom burden experienced by the chronically critically ill (CCI) is poorly understood. Though two studies have examined symptom burden in the post-acute phase, the phenomenon remains understudied and no reports were available about symptom burden at ICU discharge. Thus, the purpose of this study was to examine the symptom burden reported by CCI patients and proxies and its relationship to functional status in a group enrolled in a 2 month disease management intervention. Following a discussion of sampling and instrumentation issues, major findings will be reviewed and explained using pertinent literature. Then, study limitations, implications for nursing practice, and recommendations for future research will be presented. Major Findings Although some differences existed, the results of this study of symptom burden in the CCI generally matched the other reports of symptoms in this population and in similar populations, such as the critically ill, terminally ill and heart failure. Though overall symptom burden was lower than expected, individual symptoms were highly prevalent and the presence of pain was a major contributor to burden in both the patient selfresponder and the proxy responder group. Though there remains no universal definition of the CCI, there were some notable differences in this sample’s demographics as compared to previous reports, especially in functional status, which was better than previously reported. Also, since this was the first reported study that elicited patient and proxy reports of symptoms in the CCI, this analysis provided insight about the different perspective and different meanings of symptoms. Generally, patients reported burden 108 related to physical symptoms such as pain, fatigue, and respiratory discomfort as compared to proxies who perceived burden in the CCI patient due to cognitive impairment, communication barriers, and loss of independence. Finally, the usefulness of an open-ended single-item tool to elicit symptom reports in the CCI and to “hear” the person’s symptom burden in their own words was supported by the results of this study. Sample Description Descriptive analysis was completed on the 194 subjects from the parent study for whom symptom data were elicited at discharge from the index hospitalization. The demographics of these subjects were similar to other studies of the CCI. The mean age of 60.9 years was slightly younger than the range of 62-72 years reported in previous studies (Bigatello, Stelfox, Berra, Schmidt, & Gettings, 2007; Chelluri et al, 2004; Combes et al., 2003; Daly et al, 2005; Douglas et al., 2002; Engoren et al., 2004; Nelson et al, 2004; Nelson et al., 2006) and the median age of 71.8 in a recent multi-center study of 1,419 patients on prolonged mechanical ventilation (PMV) cared for in LTACH (Scheinhorn et al., 2007). However, the age of this sample was consistent with the report of decreasing age in the ventilated population (Carson, 2006; Carson, Cox, Holmes, Howard, & Carey, 2006). The 24 day mean length of hospital stay was less than the 31 to greater than 40 days previously reported (Combes et al., 2003; Douglas et al, 2002; Engoren et al., 2004) and partially reflected the trend of earlier transfer to LTACH or other subacute facilities (Carson, 2006; Douglas, Daly, Genet Kelly, O’Toole, & Montenegro, 2007; Nasraway et al., 2000; Nelson et al., 2006; Scheinhorn et al., 2007; Seneff et al., 2000). Though different methods or instruments have been used to measure preexisting conditions, comparison between this study’s results and other studies of the CCI was 109 informative. This comparison was important due to the association between comorbidities, length of mechanical ventilation and outcomes as reported by multiple investigators (Carson & Bach, 2002; Carson, 2006; Daly et al., 2005; Douglas et al., 2007; Nelson, 2002; Nelson et al., 2004). The Charlson Weighted Index of Comorbidity (Charlson) has been used frequently to measure comorbidities and estimates the risk of death from comorbid disease with higher scores indicating higher numbers and seriousness of comorbid conditions (range 0-33). The increasing incidence of mechanical ventilation has been shown to be associated with increasing comorbidities and decreasing age (Carson et al., 2006). In the CCI population, Charlson scores have ranged from 1.0 to 4.5 (Douglas et al, 2002; Nelson et al., 2004; Spicher and White, 1987; Quality of Life After Mechanical Ventilation in the Aged Study Investigators, 2002). A significant increase in Charlson scores of 1.76 to 1.89 over a six year period (1996-2002) was reported for mechanically ventilated patients (both < 96 and > 96 hours) in a population based study in North Carolina (Carson et al., 2006). The difference in the Charlson between short-term (< 96 hours) and long-term ventilator patients (> 96 hours) was 1.5 vs. 1.6, though statistically non-significant (Douglas et al., 2002). A mean Charlson of 1.53 was found in a preliminary analysis of 392 CCI patients on the ventilator for more than 72 hours and enrolled in an intensive communication intervention study (Daly, 2005). Since Charlson scores estimate the mortality risk and since it remains unclear how the Charlson index might be related to the expression of symptom burden in the CCI, the actual numbers of preexisting conditions in this and similar studies were compared. The mean number of preexisting conditions in the parent study was 5.5, higher than previous 110 reports of 2.3 to 3.7 (Carson et al., 1999; Douglas et al., 1997; Nelson et al., 2006; Scheinhorn et al., 2007). This increase is consistent with the higher burden of preexisting conditions as reported by others (Carson et al., 2006; Nelson et al., 2006), the increasing incidence of chronic illness in the general population (Hardin, 2002), and the supposition that the incidence and exacerbation of underlying chronic illness contributes, at least partially, to the development of chronic critical illness (Carson & Bach, 2002; Daly et al., 2005; Nelson et al., 2004; Scheinhorn et al., 2007). This study’s incidence of 28.2% proxy report was similar to other studies. Proxy report was 27.1 % and 34.1% in patients requiring mechanical ventilation longer than 4 and 5 days, respectively, (Douglas et al 2002; Douglas et al., 2001), 25.8% in seriously ill patients participating in the SUPPORT study (Desbiens et al., 1999), and 25% in CCI patients (Nelson et al., 2004). In the study by Nelson and colleagues (2006) of cognitive impairment in the CCI, 75% were cognitively impaired upon admission to a Regional Care Unit. Additionally, neurological impairment was often cited as one of the major reasons for prolonged mechanical ventilation; it was present in 37.2% of subjects in the parent study (Daly et al., 2005) and 28% in the Nelson study (2004). Functional Status The only statistically significant difference between the patient self-responder group and the proxy responder group was in functional status at hospital discharge (14.89 vs. 28.53) and at 2 months (6.38 vs. 17.44). As expected, functional status improved over the 2 month period in both groups and was worse, overall, in the proxy responder group but was much better than expected, especially in the patient self-responder group. Chronic critical illness is usually associated with a substantial decline in functional status 111 at hospital discharge and at various time points up to 12 months (Bigatello, Stelfox, Berra, Schmidt, & Gettings, 2007; Carson, 2006; Chelluri et al., 2004; Douglas et al., 2002; Im et al., 2004; Nasraway et al., 2000; Nelson et al, 2006). An analysis of three major studies revealed that only 10% of CCI patients were functionally independent at long-term follow-up (Carson & Bach, 2002). In Scheinhorn and colleagues study (2007), 98.7% of these PMV patients were admitted to LTACH with poor functional status and nearly all were totally bedridden in spite of pre-hospitalization status of independent home living in 86.5% of the sample. These studies support the major decline in functional status typically found in the CCI. Nelson’s study (2004) of CCI patients admitted to a Regional Care Unit (RCU) for ventilator weaning reported better than expected functional status scores of 75.2 upon admission to the RCU but with a notable drop in these scores to 46.1 at 3 months. These investigators used a modified FIM scale (range 13 dependent to 91 completely independent). These scores, at first inspection, would seem to be consistent with this study’s findings of higher functional status but the decline over time in Nelson’s sample may indicate differences in instrumentation or sampling. The FIM scores reported by Nelson and colleagues compared to 58.5 (full FIM range 18-126) in stroke patients at hospital discharge (Mauthe, Haaf, Hayn, & Krall, 1996) and 99.1 in community dwelling persons with multiple sclerosis (Granger, Cotter, Hamilton, Fiedler, & Hens, 1990). Since existing reports of functional status in the CCI were inconsistent with this study’s finding of almost totally independent functional status in 17 (13%) of 129 patient self-responders at hospital discharge and 58 (45%) of 103 self-responders at 2 months, further analysis of hospital discharge disposition was conducted. The 23.7% (n = 46) rate 112 of home discharge in this sample was consistent with the reported trend of decreasing home discharges in patients with prolonged mechanical ventilation (> 96 hours) from 33% in 1993 to 17% in 2002 (Carson, 2006; Carson et al., 2006) or the 34.3% home disposition rate in a 2002 comparison of short-term (< 96 hours) and long-term (> 96 hours) ventilator patients (Douglas et al., 2002). However, this rate of home discharge was considerably higher than the 8-11% home discharge rate in patients ventilated longer than 21 days (Seneff et al., 2000) and the 4% home discharge rate in Nelson’s study (2004). This study’s 2 month home disposition of 62.6% (n = 97) was also higher than reports of 50% (Im et al., 2004) and 37.6% at 6 months (Nelson et al., 2006). Sampling differences, among other factors, probably contributed to these disparate results. A comparison of clinical and demographic characteristics between those patients discharged to home and those discharged to post-acute care facilities showed significant differences. Patients who were discharged to home from the index hospitalization were younger (51.5 years vs. 63.8) with fewer preexisting conditions (4.5 vs. 5.9), shorter length of hospital stay (17.9 vs. 26.3), fewer days of mechanical ventilation (5.9 vs. 13.0), lower symptom burden (1.0 vs. 1.04) and better functional status (5.04 vs. 23.9). These differences were all statistically significant using parametric and nonparametric tests, when indicated. Thus, the functional status and home disposition of the subjects in this sample was better than previous studies, especially for the patient self-responder group. This may be due to inherent measurement problems in the OASIS items from which the functional status scores were derived and/or to the liberal enrollment criteria for the CCI in the parent study. 113 Measurement issues with OASIS functional domain items due to non-uniform response categories, differences in difficulty (i.e. ambulation vs. eating), and ordinal level of the items have been noted (Fortinsky, Garcia, Sheehan, Madigan, & TullaiMcGuinness, 2003). Other investigators have reported that the OASIS functional items were sensitive in predicting clinical outcomes such as readmission risk (Bowles & Cater, 2003) and that strong internal consistency (coefficient alpha .86-.91) and agreement (weighted kappas .41 to 1.0) exist for the functional domain of the OASIS (Madigan & Fortinsky, 2000; Madigan & Fortinsky, 2004). However, measurement error could have occurred due to the non-uniform OASIS response categories and/or when the ordinal OASIS items were combined into person-level disability scores by summing the eight ADL items. The use of Rasch modeling, a type of item response theory, has been suggested to improve the precision of measurement when using ADL items from the OASIS (Fortinsky et al., 2003) but was beyond the scope of this study. The enrollment criteria and characteristics of the parent study also explain the functional status found in this study. In the parent study, 1,041 patients who met the criteria of mechanical ventilation longer than 72 hours were enrolled. Length of mechanical ventilation was 3, 4, and 5 days for 13 (6.7%), 27 (14%) and 27 (14%) of patients in this study’s sub-sample of experimental patients (n = 193), respectively. A total of 102 patients or 52.8% required 7 days or less of mechanical ventilation and twenty (10.4%) had very good functional status scores of two or less in this sample. Additionally, there was a significant difference in length of mechanical ventilation between those patients discharged directly home and those discharged to post-acute care facilities (5.9 vs. 13.0 days). Though functional status in this study was better than other 114 reports, it should be noted that out of the total parent study sample of 1,041 ventilated patients, only 97 patients made it home at 2 months. This 9% home disposition rate actually reflects the substantial decline in functional status, overall, and may support the existence of two different populations requiring mechanical ventilation as suggested by other investigators (Carson & Bach, 2002; Seneff et al, 2000): those with single organ dysfunction and those more typical CCI with multiple organ dysfunction, prolonged dependence and poor functional status. These findings support the importance of clarity in defining the CCI for research and clinical purposes. Symptom Prevalence Symptoms were very prevalent in this sample of the CCI as 95.3% of the patient self responders and 93.8% of the proxy responders reported 1 or more symptom, though overall symptom burden was moderate (1.81 and 2.03). This prevalence was similar to the 80-90% overall prevalence of symptom burden reported in the CCI (Nelson et al., 2004, 2006) and the 50-80% prevalence of symptoms in the seriously or chronically ill (Desbiens et al., 1999; Walke et al, 2004). This finding was also consistent with the analysis of unmet needs of symptom management and palliative care for the critically ill and the CCI (Carson & Bach, 2002; Nelson, 2002; White & Luce, 2004). Responses to the open-ended question, “what symptoms are most bothersome at this time?” were coded into 11 categories: pain, loss of independence, fatigue, respiratory discomfort, communication, psycho-emotional, nutrition related, elimination discomforts, sensory changes, concerns/consequences of chronic critical illness, and impaired cognition. These categories encompass all of the symptoms found on widely used symptom assessment tools such as the Memorial Symptom Assessment Scale (MSAS) 115 and the Edmonton Symptom Assessment System (ESAS) with the exception of problems with sexual activity. The MSAS measures the frequency and distress associated with 32 symptoms. The ESAS measures the severity of nine symptoms, on a scale of 0 (none) to 10 (severe), common in cancer patients. The fact that the symptoms elicited by using this open-ended question matched those on tools with established psychometrics added face validity to the use of this single-item tool. No studies have been reported that compare the effectiveness of using a single-item tool with multi-item symptom assessment tools to measure symptom burden in the CCI. As expected, pain was the most prevalent symptom in both the patient (48.1%) and proxy (43.1%) group. This finding was consistent with the report of pain in 44% CCI patients (Nelson et al., 2004) and 40-50% of moderate to severe pain reported in critically ill or seriously ill patients (Desbiens et al., 1999; Li and Puntillo, 2006; Nelson et al., 2001; Puntillo 2001, 2004) and added further support to the argument that pain is undertreated in the acute care setting. Overall, patients reporting pain had a significantly higher symptom burden when compared to patients with no pain reports (1.64 vs. 2.17; t = -3.65; p = < .001). Although respiratory discomfort (24%) had the next highest prevalence in the patient self-responder group, this was considerably lower than the 100% incidence of dyspnea in ICU patients reported previously (Li and Puntillo, 2006), 60% prevalence in the CCI (Nelson et al, 2004) or the reported overall highest prevalence (19.2%) of dyspnea when compared to any symptom in a SUPPORT study cohort (Desbiens et al., 1999). The lower incidence of dyspnea or related respiratory discomforts may have been due to the timing of measurement in the parent study or patient-related variables. Since 116 symptom reports were obtained at hospital discharge, a majority of patients (83.1%) had been liberated from the ventilator suggesting that respiratory status had stabilized. Possibly desensitization, presented as a possible rationale for patient underreporting of dyspnea (Campbell, 2008) or cognitive fatigue (Campbell, 2007) might explain the low report of dyspnea. More research will be needed to determine the prevalence of dyspnea and related respiratory discomforts in the CCI and their ability to report associated distress. The symptom categories of fatigue and thirst should be noted. Fatigue was prevalent in 23.3% (n = 30) of the patient self-responders but was only reported by 3.1% (n = 2) of proxy responders. This prevalence was much lower than the 47-100% prevalence reported in the CCI and other chronically ill populations (Barnes et al., 2006; Higgins, 1998; Klinkenberg et al., 2004; Kutner et al., 2001; Nelson et al., 2004; Walke et al., 2004). Though severe deconditioning has been acknowledged as almost universal to the syndrome of chronic critical illness, this finding suggested that the symptom of fatigue may be underreported, overlooked and poorly assessed in both cognitively intact and impaired patients. The symptom of thirst was only mentioned twice by subjects in this sample of CCI. This finding was very dissimilar from other reports of highly prevalent thirst in 64% of CCI patients (Nelson et al., 2004) and 40% to 62% in the critically ill (Li & Puntillo, 2006; Rotondi et al., 2002) and was probably affected by fluctuations in thirst not captured by the one-time symptom assessment upon hospital discharge, the large percentage of proxy reports, or resolution of thirst mechanisms after extubation and weaning from the ventilator. 117 The unexpected and previously unreported category of concerns and consequences of chronic critical illness emerged in response to the open-ended question. Drains, tubes, being hospitalized, surgery and loss of personal items such as eye glasses, were placed in this general category. This finding was consistent with the report by Scheinhorn and group (2007) that 90% of PMV patients admitted to LTACH had at least three indwelling tubes or catheters. Though not traditional symptom responses, patient and proxy responders reported these concerns to be the most bothersome. The contribution of these concerns to symptom burden in the CCI would not have been captured with more structured multi-item tools, but how to manage the worry or state underlying the concern remains unclear. Symptom Burden In this study, symptom burden was defined as the distress or bother resulting from one or more symptoms, in isolation or with a cumulative effect, as perceived by the patient and reported by the patient or significant other. The open-ended question allowed the patient and proxy responders to report symptoms that were bothersome to them, thereby attaching their own meaning and related burden. The symptom burden found in this sample of the CCI, though at a moderate level of 1.81 (patient self-responders) to 2.03 (proxy responders), was lower than expected given the conceptualization of the many physiological, psychological, and situational factors that contribute to a prolonged, cumulative and diffuse symptom experience in the CCI. The possible range was 0-4 and though no patients were instructed to limit the number of symptom reports to four, only one responder in the sample reported more than four bothersome symptoms. This approach was different from multi-item symptom tools that actually list 10 or 20 118 symptoms and may have a “coaching” effect on the subject, thereby prompting more responses. Relationship between Symptoms and Overall Burden The relationship between individual symptoms and overall symptom burden was examined using parametric and nonparametric tests. Due to the inequality in group sizes, nonparametric tests were used so as to not violate the underlying assumptions (normal distribution and homogeneity of variance) of parametric tests. In the combined sample of patient self-responders and proxy responders, patients reporting pain had a significantly higher symptom burden when compared to patients with no pain reports (1.64 vs. 2.17; t = -3.65; p = < .001). In addition, there was a significant difference in symptom burden between those who did and did not report a symptom in all remaining categories except fatigue and impaired cognition. The greatest difference in symptom burden (i.e. total number of symptoms reported) between those who expressed a symptom and those who did not occurred in the categories of psychoemotional (1.78 vs. 2.8; t test = -4.40; p = < .001) and concerns and consequences of chronic critical illness (1.76 vs. 2.76; t test = -4.81; p = < .001). The effect of the report of pain on the difference in symptom burden between the patient self-responder and proxy responder group was not significant. In the patient self-responder group, there was a significant difference in symptom burden between those who did and did not report pain, fatigue, respiratory discomfort, psychoemotional symptoms, concerns and consequences of chronic critical illness and impaired cognition using parametric and nonparametric tests. As in the combined group, the greatest differences in symptom burden occurred between those patient selfresponders who did and did not report psychoemotional distress and concerns or 119 consequences of chronic critical illness. This finding may indicate that emotional worry or distress in the CCI contributes to more symptom burden much like fatigue has been found to contribute to greater symptomatology in cancer patients (Given et al., 2001). Patients with coexisting chronic medical illness and depression or anxiety have been found to report significantly higher numbers of medical symptoms (Katon, Lin, & Kroenke, 2006). Measurement Issues as related to Symptom Burden The use of different symptom measurement tools made the comparison of the symptom burden found in this sample to that reported by other investigators more difficult. Symptom prevalence was often measured by multi-item scales such as the MSAS and the ESAS. Nelson et al. (2004) reported a mean of 9 symptoms in the CCI using a modified MSAS scale of 16 items, Zambroski et al. (2005) showed a mean of 15.1 symptoms in heart failure patients on a 32 item MSAS-heart failure tool, and Walke et al. (2004) found the mean number of moderate to severe symptoms in community dwelling elders with COPD and heart failure to be 3.3 and 2.0, respectively, using the ESAS. Single item questions, which produced a mathematical average as in this study, were used by Given et al. (2001) in a study of elderly cancer patients and Klinkenberg et al. (2004) in proxy reports of patients’ symptoms at end-of-life. Given reported a mean of 4.7 symptoms and Klinkenberg a mean of 2.7 symptoms in their respective studies. The symptom burden found in this CCI sample was more consistent with these findings and though the populations were not identical, important clinical characteristics were similar. The similarities between the CCI and those patients at the end-of-life warrants future 120 examination, particularly their ability to report symptom burden. It is possible that communication barriers and general weakness or deconditioning have lowered the ability to report what would appear to be a high symptom burden. However, differences in measurement do not fully explain the level of symptom burden found in this study or the factors that might have influenced patient and proxy reporting of symptoms. Multidimensionality of symptoms and responder factors may have impacted report of symptoms. Symptoms are complex and include the generally accepted dimensions of intensity, timing, quality and distress. Distress has been defined as the affective domain, the physical or mental anguish or suffering associated with a specific symptom (Armstrong, 2003; Campbell, 2008; Rhodes & Watson, 1987). Most existing symptom assessment instruments measure intensity (frequency and severity) rather than distress (Armstrong, 2003) and few measure impact of symptoms on activities of daily living (Cleeland, et al., 2000). Possibly, this study captured the component of distress more than other features of burden, as conceptualized, including a cumulative effect, lowering the overall burden captured. Subject related factors may have also affected the response rate. Other investigators have noted that patients are unwilling to report pain or complain about the lack of treatment (Cleeland, 2004; Cohen, Williams, Knight, Snider, Hanzik, & Fisch, 2004) even in the presence of obvious nociceptive stimuli. Past experiences with the health care establishment, undesirable side effects of medication such as constipation or sedation, cultural influences or meaning of symptoms and disease, and social desirability could have reduced the reports of symptoms. Cognitive impairment may have also lowered the patient and proxy reports, though this was not found in the studies by Nelson 121 (2004; 2006). Also, it was possible that the phenomenon of response shift, when patient’s internal standards and meaning of quality of life change over the course of the illness (Sprangers et al., 2002), affected report of symptoms. It would be understandable if CCI survivors had experienced some degree of adjustment or relief in surviving the lifethreatening episode affecting their response to “the most bothersome symptom”. In fact, additional analysis showed that 35.8% of the subjects in this sample rated their health, just prior to discharge, as excellent, very good or good in response to the SF-8 question, “overall, how would you rate your health in the last 24 hours?” The possible response range for this item is 1 (excellent) to 6 (very poor). When the patient self-responders and proxy responders were compared, there was a statistically significant difference in rating of overall health (3.61 vs. 4.68; t test = -5.95; p = < .001) The report of good, very good or excellent general health at 2 months in 50% of survivors (n = 368) of prolonged mechanical ventilation, in spite of a significant decline in functional status (Quality of Life After Mechanical Ventilation in the Aged Study Investigators, 2002), also supports the premise that CCI survivors may perceive symptoms as less and general health as better than outward appearances. Thus, social desirability or optimism may obscure the patient’s report of health status. A qualitative study of symptom masquerade, overt absence of pain expression despite obvious pain source and poor quality of life, found that patients did not report pain and seek treatment because they wanted to stay alert and that the meaning of the symptom altered their report (Cohen et al., 2004). These investigators found that the report of symptoms depended on what question was asked, who asked it, and when. It is not known if these factors influence the CCI and will need to be tested in future studies. 122 The findings of this study supported the need for development of the concept of symptom burden. Cleeland and Reyes-Gibby (2002) defined symptom burden as a summative indicator of the severity of the disease and the patient’s perception of the impact of symptoms on daily living such as quality of life, ability to work, and personal relationships. Understanding the cumulative impact of symptoms existing together has been recognized as critical to the development of the concept of symptom burden (Cleeland & Reyes-Gibby, 2002). The missing link in the study of symptom burden in the CCI may be blending these two as this study’s approach captured the patients’ or proxies’ perception of the impact but perhaps not the severity of the symptoms associated with chronic critical illness. Though accepted as one of the four dimensions of the symptom experience, distress does not capture the cumulative and nondiscrete features of the burden resulting from one symptom or multiple symptoms of varying severity that coexist for a longer duration. As this study’s model (Figure 1) was derived from the Theory of Unpleasant Symptoms (Lenz et al., 1995, 1997), multiple physiologic, psychologic and situational factors were hypothesized as contributing to symptom burden in the CCI. The symptom burden resulting from the prolonged exposure to these multiple factors remains hypothetical though several studies supported the presence of symptom burden in the CCI and seriously ill populations (Desbiens et al., 1999; Nelson et al., 2004, 2006; Puntillo, 1990). It remains imperative to understand what accounts for the bulk of symptom burden in the CCI in order to target aggressive clinical management. Preliminary analysis of the effect of low and high symptom burden on functional status did not allow clear conclusions about clinical significance so further study is needed. Though patients with high symptom burden had three more hospital days and 123 two more ventilator days than those with low symptom burden, it is not clear as to which clinical or demographic variables may account for this difference. It should be noted that this study’s results did not add evidence to the presence of symptom clusters. Symptom clusters, defined as the presence of three or more concurrent and related symptoms, have been studied by multiple investigators, though predominantly in the oncology population and focusing on the cluster of pain, fatigue and depression (Dodd et al., 2001; Dodd, Miaskowski, & Lee, 2004; Dodd, Miaskowski, & Paul, 2001; Gaston-Johansson, Fall-Dickson, Bakos, & Kennedy, 1999; Given, Given, Azzouz, & Stommel, 2001; Kim, McGuire, Tulman, & Barsevick, 2005). Using nonparametric tests in this sample, there were significant correlations between the category of pain and the symptom categories of independence, fatigue and respiratory discomfort. The correlation between pain and psychoemotional symptoms, which included depression, was not significant. These findings were inconclusive. Investigators have noted that symptom cluster research is very early in its development, particularly noting the absence of clarity about the synergistic effect and temporal pattern of the antecedent symptom (Dodd et al., 2004). Relationship of Symptom Burden to Functional Status The relationship between symptom burden and functional status was analyzed using both multiple regression and logistic regression due to the positive skewness of the functional status variable. To run logistic regression, functional status was dichotomized into no impairment (score = 0) and the presence of impairment (score = 0 .1-33). However, the multiple regression results are more meaningful for several reasons. First, the operationalization of functional status as a continuous variable is more consistent, 124 both clinically and conceptually, with the continuum of complete independence to complete dependence that CCI patients actually experience. Second, the dichotomization of functional status into only two levels did not completely capture the changes in this variable due to the predictors. Ideally, three or more categories of functional status would have been more appropriate but this complex statistical analysis was beyond the scope of this study. Finally, power was high to detect significance, at alpha of .10, in the relationship between symptom burden and functional status when controlling for the seven covariates using multiple regression with the sample size of 103 patient selfresponders. Though symptom burden did not make a significant contribution to the model, the overall regression model did predict 18.3% of variance in functional status at 2 months. The only two variables that significantly contributed to the model were the physical component summary (PCS) of the SF-8 and the number of preexisting conditions. These findings were consistent with several reports in the literature. The Quality of Life After Mechanical Ventilation in the Aged Study Investigators (2002) found that older age and comorbidities were associated with increased mortality and poorer functional status in 817 patients receiving at least 48 hours of mechanical ventilation. Clarke and colleagues (2000) reported that the risk of functional status impairment doubled in chronic heart failure patients (N = 2, 992) with the comorbidities of diabetes, chronic lung disease or stroke. In a study of 4,112 primary care patients, those with chronic lung disease, heart disease, chronic back pain and arthritis were found to have worse functional status overall and this was associated with increased age and a higher number of comorbidities (Wensing, Vinerhoets, & Grol, 2001). 125 Since the enrollment criteria of the parent study were liberal, sampling error likely occurred thereby diluting the hypothesized relationship between symptom burden and functional status. Sampling error is the difference between the sample statistic and the population parameter and, if large, the sample results will not be representative of the population. This sample included patients who had not progressed to the full syndrome of chronic critical illness as evidenced by the higher than expected functional status at discharge and at 2 months, the high incidence of home disposition at discharge (14.7%) and especially at 2 months (47.7%), and the significantly shorter length of mechanical ventilation in those discharged to home versus those discharged to a post-acute care facility (5.9 vs. 13 days). Also, it was likely that this study captured those patients at the better end of the symptom burden and functional status continuum. The tool and timing of functional status measurement may be another reason why no effect was found in the relationship between symptom burden and functional status. The limitation of deriving this study’s functional status variable from ADL items in the OASIS has already been discussed in a previous section but it should be added that other investigators have used the same or very similar ADL items (Fortinsky et al., 2003; Im et al., 2004; Mauthe et al., 1996; Nelson et al., 2006). The 2 month time period between symptom burden assessment and functional status measurement may more fully explain this study’s inconclusive finding. In the multiple studies that reported a statistically significant relationship between symptoms and functional status, both variables were measured at the same time point. Symptoms as significant predictors of functional status included a composite measure of symptom severity in 129 elderly persons with lung cancer (Kurtz et al., 2000); pain and fatigue in 93 community dwelling patients with 126 cancer (Dodd et al., 2001); dyspnea in 100 community dwelling patients with COPD (Reishtein, 2005); symptom distress explained 6% of variance in functional status in chronic hemodialysis patienst (Thomas-Hawkins, 2000); depression, fatigue, sleep disturbance, cognitive impairment, and pain in 73 outpatients with high-grade gliomas (Fox, Lyon, & Farace, 2007); and, the overall number of distressful symptoms in 104 ethnically diverse HIV-positive women (Hudson, Lee & Portillo, 2003). Remote measurement of functional status was conducted by Nelson and colleagues (2006) in 203 CCI cared for in their Regional Weaning Center. They measured the effect of the calculated percentage of total days with either coma or delirium on functional status at discharge and at 3 and 6 months while controlling for age, sex, ethnicity, Charlson, APACHE, and number of acute comorbidities. In the 85 survivors, the days spent in coma or delirium were significantly associated with poorer functional status at 3- and 6-months. The disparate results in the symptom burden and functional status relationship in the CCI between this study and Nelson’s study indicate that further study is needed. There is no gold standard for ADL items but the eight OASIS items used in this study have been used elsewhere as measures of functional status. However, it was possible that the inclusion of only ADL and not instrumental activities of daily living (IADL) reduced the sensitivity of the functional status measure to the effects of symptom burden. Since IADL require higher cognitive skills that can impact return to work, math ability, independent living, and personal relationships, these components may have been more meaningful to the patients in this study. Future analysis of the impact of symptom 127 burden on functional status should include IADL and the patient’s own report of what aspects of functional status are most meaningful to them. Validity of the Single-item Scale A single-item scale in an open-ended question format was used to measure symptom burden in the CCI. This approach has not been reported previously. Though this question, “what symptoms are most bothersome at this time?” had face validity, this investigator attempted to establish construct validity by comparing agreement between the symptom burden variable derived from this question and the overall SF-8 score and this study’s symptom categories of pain, psychoemotional symptoms and fatigue to the three comparable SF-8 single-item scales, namely, the pain, mental health and vitality scales. There was a small positive and significant correlation between the symptom burden variable and the SF-8 score (r = .173; p = .016) in the combined sample of all patient self-reporters and proxy reporters. Using the kappa statistic, agreement was low though significant between the pain categories (kappa = .247; p = < .001) and was not significant for the mental health-psychoemotional or fatigue-vitality comparisons. Kappa scores provide a measure of the extent of agreement beyond chance and .41-.60 is considered moderate and above .80 almost perfect agreement (Landis & Koch, 1977). Frequencies of symptoms may have affected these findings as fatigue and psychoemotional symptoms were identified in 32 and 20 cases, respectively, whereas pain was noted in 90 cases. This may also explain the large discrepancy between fatigue reported to the open-ended question (32 cases) and the SF-8 vitality question (146/193). Though there was support for the approach used in this study’s design, agreement was 128 less than expected between responses to the single-item open-ended question and the SF8 so criterion-related validity was not supported. There was strong empirical support for the approach used in this study. Dodd and colleagues (2001) used three symptom specific items from the established Quality of Life-Cancer instrument to measure symptoms, presence of symptom clusters and effect on functional status in 93 patients with cancer. Though specific validity testing was not reported, these investigators did conclude that the design had validity though it may have contributed to the inability of certain symptoms to predict changes in functional status. Additional studies also supported the use of a single-item scale to measure symptoms. Two simple questions with only one measurement were used by Desbiens and the SUPPORT investigators (1999) to assess symptom burden from nine symptoms in the seriously ill, who share many of the same characteristics with the CCI. Their method was very effective in finding the high symptom burden, often cited, that nearly 50% of 1,582 patients reported one or more symptom at least moderately severe half of the time or severe any time. The large sample size contributed to this significance as low frequency of individual symptoms was probably not a factor. Given and colleagues (2001) found that a single item to measure pain and fatigue in 841 elderly patients with cancer did elicit significant findings as pain and fatigue were independent predictors of the numbers of other symptoms reported. Validity and reliability of a single, global quality of life question for patients with esophageal cancer was established by de Boer and group (2004) and high correlations were reported between a single item cancer-related fatigue item and established multi-item scales by Kirsh et al. (2001). Additionally, comparison of a single-item indicator against a multi-item scale in over 2000 breast cancer patients 129 showed that the single-item scale had validity as an indicator of emotional well-being (Hurny et al., 1996) and very good reliability and validity of investigator developed scales to measure coughing and wheezing in lung cancer patients, when compared to established multi-item scales, was noted by Chernecky, Sarna, Waller and Brecht (2004). Bernhard and group (2002) reported acceptable psychometrics using a single-item measure of treatment burden in 249 oncology patients. Though the results of validity testing were not conclusive about the use of a single-item open-ended question in the CCI, there remain strong arguments, with some empirical support, for continued use of such an approach and the need for further research. The CCI are an extremely vulnerable population with major barriers to the communication, and therefore, management, of symptoms. This vulnerability is compounded by the prolonged nature of the syndrome that exposes the patient to repeated physiological, psychological and situational sources of symptom burden. Higgins and Daly (1999) recognized this vulnerability in patients on the ventilator greater than 7 days by designing bedside interviews that minimized response fatigue. Their secondary finding that these patients were able to recall fatigue status over the past several days but not the past week was consistent with the finding of Rotondi and group (2002) that 150 patients discharged from the ICU were unable to remember the ICU experience or the endotracheal tube. Additionally, those patients who did not remember the experience on average were more severely ill than the 300 who did remember the ICU and ventilator experience. If CCI and critically ill patients cannot remember or communicate burdensome symptoms and if debilitation is universal, then assessment techniques must be simplified 130 to allow whatever symptoms are bothersome to be expressed and minimize the burden of questioning. The single-item open-ended question has these essential features that also balance the reduction in reliability and discriminant validity (Bernhard et al, 2002; Bowling, 2005; Cleeland and Reyes-Gibby, 2002), the effect on response rate by personal factors such as anticipation of a bothersome symptom (Bernhard et al, 2002) or coexisting conditions (Given et al., 2001) and minimizes the effects of acquiescence or the tendency of subjects to agree irrespective of item content (Schaeffer & Presser, 2003). Long symptom measurement tools with exhaustive list of symptoms will tire the CCI patient and potentially reduce completion rates and accuracy of symptom expressions. Though more research is needed and the ideal instrument for measuring symptom burden in the CCI remains to be developed, the ideal tool should focus on the most prevalent and distressing symptoms for those using the tool (Cleeland & Reyes-Gibby, 2002). Finally, and perhaps most importantly, the use of this single-item open-ended question revealed sources of distress that would not have been captured with conventional, multi-item scales. Allowing CCI patients and their proxies to use their own words was more consistent with the conceptualization of symptom burden and allowed examination beyond prevalence or frequency of symptoms to understand the meaning and perception of burden in their own words. Limitations of the Study Several limitations of this study as related to internal and external validity will be discussed. Though there were definite advantages to conducting a secondary analysis of preexisting data, there was no chance to establish accuracy of the measures used to capture the key variables of symptom burden and functional status. This added to the risk 131 of conceptual slippage (Clark & Cossette, 2000). Also, since criterion-related validity of the open-ended question against the SF-8 with established psychometrics was not supported, there remains concern about whether the open-ended question captured symptom burden. Further development of the concept of symptom burden may help to clarify the best tool to measure the phenomenon. Missing Data The lack of functional status data at 2 months due to the differential loss of 45 (19%) subjects in the post discharge period, mostly to death, affected internal validity of the study’s results. Symptom burden in the sickest patients (36 missing) may not have been captured due to cognitive impairment or proxy reluctance in the setting of severe patient cognitive impairment. In addition, functional status was unexpectedly good in the overall sample but worse at both time points for subjects with no symptom reports. It is likely that the prevalence of symptom burden was even higher than expected overall and worse at both time points for those subjects for whom symptom reports were missing and patients at the end of the functional status continuum were not fully represented. Selection Bias The ability to generalize this study’s findings to other samples of the CCI was limited due to the selection bias and the lack of a universal definition of the CCI. The liberal enrollment criteria used in the existing data base resulted in the capture of some mechanically ventilated patients with single organ disease who did not progress into the full syndrome of chronic critical illness and who were younger and with better functional status and ability to return home than previously reported. However, these findings should be noted in the context of the total enrolled sample of 1,041 ventilated patients. 132 Also, the 28.2 % proxy response rate and the 28.5% refusal rate of the parent study further limits the ability to generalize these findings of symptom burden and functional status to the population of the CCI. Thus, while this sample may not be comparable in all aspects to other CCI samples, important evidence was still gained about the prevalence of pain, fatigue, respiratory discomforts and other symptoms in those patients mechanically ventilated for greater than 3 days. The reliability of the open-ended single-item question and the functional status items derived from the OASIS may have affected the applicability of these results. The open-ended question was an untested tool with no repeated trial or interrater reliability testing in the parent study. Also, a single-item scale has reduced ability to discriminate symptoms so it was possible that more and/or different symptoms may have been reported with the use of a multi-item scale. However, this approach did reduce respondent burden and the tool did capture a high prevalence of symptoms and the most bothersome symptoms in the personal words of the patient self-responders and proxy responders. Since this was the first study to attempt such an approach in a vulnerable population with considerable debilitation and major communication barriers, understanding of the meaning behind symptom reports in the CCI was increased. Proxy Reports A final issue was the 28.2 % incidence of proxy reports. The substantial difference in the type of symptoms expressed by patient self-responders and proxy responders indicated that limitations in our ability to assess symptoms in the cognitively impaired remain worrisome. Though this study was the first to examine proxy reports of symptom burden in the CCI, previous work may explain the differences in the most 133 prevalent symptoms between the patient self-responder group and the proxy responder group and provide rationale for the continued use of proxies. Proxy data is more reliable with observable, objective aspects of the patient experience such as dyspnea, vomiting, and fatigue and poorest in the more subjective aspects such as pain, emotions, and thoughts (McPherson & Addington-Hall, 2003). Age, gender, the level of patient impairment, patient-proxy relationship, proximity and degree of involvement of the proxy, degree of caregiver burden, memory and recall bias have been associated with patient-proxy agreement. Agreement does improve over time and if the proxy is in close proximity to the patient. Since most CCI require care in a health care facility, proxy reporters for the CCI are not in close proximity or involved in the daily care of the patient. Thus, agreement between the patient and proxy could be reduced. However, this would be balanced by the familiarity of the proxy with the patient’s preferences and nonverbal signs of distress. Several studies have established the reliability of the use of proxy reporting in vulnerable populations like those at end-of-life (Klinkenberg, Willems, van der Wal, & Deeg, 2004; Kutner, Bryant, Beaty, & Fairclough, 2006). Campbell (2008) established reliability and convergent and discriminant validity in an investigator developed respiratory observations scale used to assess distress from dyspnea in patients unable to self-report. This last approach could be applied to the CCI in whom cognitive dysfunction and communication barriers challenge symptom reporting. The relationship of the primary caregiver to the CCI patient may partially explain why the patient self-responders reported more physical symptoms as compared to the proxy reports of symptoms related more to role change such as loss of independence, 134 communication barriers and impaired cognition. In this study, 84 (39.8%) of primary caregivers were the spouse and 66 (31.3%) were adult children. Siblings and “other” category made up the next two largest categories of caregiver relationship to patient. Spouses and adult children are more likely to be concerned with the impact of the patient’s illness on family roles including income generation, companionship, work ability, decision making, and need for care or supervision. In summary, the differences in patient and proxy responses to the “most bothersome symptom” reflected not only the nature of the syndrome but the nature of what each one experiences and the meaning attached. The patient self-responders endure the moment to moment physical cost of being chronically critically ill, the procedures and discomforts, and for those cognitively intact, the emotional stress. The proxies, most of whom are primary caregivers, endure the emotional burden of the syndrome, the uncertainty of their loved one’s survival or return to a meaningful quality of life, and usually the need to bear the responsibility of short-term decisions and long-term care planning. Thus, CCI patients experience the burden of symptoms while their proxies experience the burden of the illness. Though oversimplified, this partially explains the report of physical versus emotional or role change symptoms between the two groups. Still, the high incidence of cognitive impairment and communication barriers in the CCI will necessitate the need for proxy reporting of symptom burden as an adjunct to comprehensive clinical management. Since inadequate assessment is the greatest barrier to effective management (Cleeland, 2004), multiple sources of assessment data will need to be used to fully manage symptoms in the CCI, including proxy reports of symptom burden. 135 Implications for Nursing Practice The results of this study have several implications for practice. Symptoms overall and pain are highly prevalent in the experience of the CCI. These results have added to the abundance of evidence that pain and other distressful symptoms are undertreated in the acute care setting and in the vulnerable population of the CCI. Education of critical care nurses and physicians about this symptom prevalence and the use of systematic evaluation of pain and other distressing symptoms may be more important to the effective relief of symptom burden in the CCI and the critically ill than the use of any particular tool (Hamill-Ruth, 2006). A recent study by Chanques and colleagues (2006) found a significant decrease in pain and agitation and a significant decrease in length of mechanical ventilation and nosocomial infections with the implementation of a protocol for systematic evaluation of pain and agitation and timely, rapid medical intervention. Another study tested the impact of a clinical pharmacist enforced sedation protocol and found a significant reduction in length of mechanical ventilation and ICU and hospital length of stay (Marshall, Finn, & Theodore, 2008). Since exposure to the critical care environment was conceptualized as one of the situational factors underlying burden in the CCI, any intervention that could potentially reduce symptoms and length of stay holds considerable promise. The combination of a single-item tool with systematic evaluation may be a powerful clinical tool to improve management and relief of symptom burden. The prevalence of symptoms other than pain also has clinical implications. Though awareness has certainly been raised about the need to assess pain, symptoms with less or no behavioral cues such as thirst and fatigue remain problematic. Those caring for 136 the CCI, whether in the acute or post-acute setting, need to have increased sensitivity to picking up subtle cues for the presence of atypical distressing symptoms. The substantial difference between the concerns of patient self-reporters and proxy reporters in this study and the different meanings behind symptom reports also holds implications for practice. Family caregivers of the CCI patient appear to be concerned with the role change aspects of the syndrome such as inability to communicate, loss of independence and cognitive impairment. These findings should definitely be considered when meeting with families of the CCI. As recommended, regularly scheduled meetings with families of the critically ill and CCI should be held to discuss the plan of care, prognosis, patient and family preferences, and provide support to family members during a very stressful period. Many investigators have acknowledged the need for improved communication with families in the critical care setting (Ahrens, Yancey, & Kollef, 2003; Davidson et al., 2007; Lilly & Daly, 2007; Stapleton, Engelberg, Wenrich, Goss, & Curtis, 2006). Recommendations for Future Research Several recommendations for future research of symptom burden in the CCI are presented. These recommendations include development of the concept of symptom burden, further exploration of the relationship between symptom burden and functional status in the CCI, refinement of a single-item open-ended tool for symptom expression in vulnerable populations, reliability of proxy reporting of symptom burden, and, finally, consensus about the definitions and classification of the syndrome of the chronic critical illness. 137 First, the concept of symptom burden will need to be explored further using mixed methods. The meaning of burden to individuals experiencing the syndrome and to caregivers watching the progression of events needs to be better understood. Second, a modification of the research design to include more than one symptom measurement and more proximal functional status measurements would strengthen the analysis of the relationship between symptom burden and functional status. Also, instrumental ADL’s could be added to the functional status measurement. A third recommendation for future research is to design a study in which symptom reports are elicited with a single-item tool and established multi-item scale, simultaneously. A systematic evaluation of symptoms using these scales could also be designed. Also, reliability of proxy reports of symptom burden in the CCI needs to be investigated. Since the use of proxies will continue to be an essential component of symptom burden management in the CCI, studies should be designed to compare patient self-reports, proxy reports and nurse reports of perceived burden. Nurse reports should be added since these caregivers typically have the closest proximity to the CCI and may be in the best position to assess and report symptom burden in the absence of patient selfreporting. An important area for future research would be to test the prevailing notion that early tracheostomy provides comfort for the mechanically ventilated patient. Clinically, this rationale is offered repeatedly by critical care clinicians when family members are approached for consent. However, little if any evidence supports this claim. In the study by Combes, Luyt, Nieszkowska, Trouillet, Gibert & Chastre (2007), tracheostomy was 138 associated with lower risk of ICU and in-hospital death. However, there was no measurement of the effect of early tracheostomy on symptom expression or burden. Finally, studies could be designed with a more conservative definition of the CCI. The parent study’s major eligibility criteria of mechanical ventilation greater than 72 hours should still be used but the additional criterion of length of ICU stay of at least 7 days should also be added. The addition of this criterion would eliminate most of the patients who require mechanical ventilation longer than 3 days but do not progress to become CCI due to single organ failure. Summary Symptom burden in the CCI and its relationship to functional status was the focus of this secondary analysis of an existing data base. This investigation afforded the opportunity to analyze previously collected symptom data and address research questions not examined in the parent study. The prolonged course and extremely poor outcomes of the syndrome of chronic critical illness have been studied vigorously but the symptom burden carried by those experiencing the syndrome was mostly unexplored. This investigator attempted to add to the results of the two existing studies (Nelson et al., 2004, 2006) that symptom burden is high in the CCI and to clarify not only influencing factors but the relationship of burden to the outcome of functional status. A final analysis of this study’s results was informative in regards to the role that mechanical ventilation plays in the syndrome of chronic critical illness, optimal methods by which clinicians and researchers could study the phenomenon of symptom burden, and the symptom burden experienced and reported by the CCI. Prolonged mechanical 139 ventilation (PMV) is a hallmark of the CCI and is associated with increased morbidity and mortality but it is not clear how much PMV is associated with symptom burden or functional status nor whether early tracheostomy really does improve comfort and outcomes in this population. Bigatello and colleagues (2007) reported that failure to wean from the initial PMV episode was associated with a seven-fold likelihood of death within one year. This study’s finding of a significant difference in the length of mechanical ventilation between those patients discharged to home versus those discharged to a postacute care facility (5.9 vs. 13 days) added more evidence to the negative role that prolonged mechanical ventilation plays in the course of the illness. The optimal method by which to assess symptom burden in vulnerable patients with communication barriers and high incidence of cognitive impairment will need to be studied further. However, this study’s results did point to the usefulness of a single openended question that allowed the patient or their proxy responder to report symptoms in their own words with their own meaning attached. This approach reduced the measurement burden associated with longer, multi-item tools. In addition, some rich descriptive data was obtained on which to build future investigation or clinical interventions. The universal definition of chronic critical illness remains elusive even after more than 25 years of research and clinical experience. Nelson (2006) asserted that prominent features of the syndrome include ongoing ventilator dependence, severe debility, metabolic abnormalities, failure of other organ systems and recurring infections and symptom burden. Though not universally defined, the CCI are certainly increasing in the context of an aging society, increasing chronicity and ever growing use of technology 140 (Carson & Bach, 2002; Carson, 2006; Daly et al., 2005; Danis, 2004). This incidence will increase as critical care continues to be good at rescue but poor at returning the CCI to full functional status and quality of life. Though MacIntyre and colleagues (2005) have proposed a formal definition of prolonged mechanical ventilation as 21 days or more of mechanical ventilation for at least 6 hours/day, it is concerning that this could be the identifier for chronic critical illness. If clinicians wait three weeks and/or for the performance of a tracheostomy due to failure to wean, the suffering from a poorly defined and undertreated symptom burden will continue for the CCI. 141 Appendix I Open‐Ended Question ID: _____ CASE PROJECT PREFERENCE ELICITATION GUIDE (DISCHARGE: experimental patients) Variable Description Data Person completing 1 = Patient _____ Preference form 2 = Proxy dprefper SYMPTOM MANAGEMENT Symptoms that are most bothersome at this time: (list in order) 1. 2. 3. 4. _________________ _________________ _________________ _________________ Overall what is your biggest concern at this time? _________________________________________________ Current plan for symptom relief: _____ medications (list): _____activity modifications _____diet modifications _____assistance with physical tasks _____change in household routine 142 Appendix II Pre SF‐8 (Patient) ID#____ CASE PROJECT Person completing SF 8 1 = Patient _____ 2 = Proxy PREPERSON We would like to better understand how you and other persons in this study feel, how well you are able to do your usual activities, and how you rate your own health. To help us better understand these things about you and other persons, I have 8 short questions to ask you about your (or the patient’s) health status 4 weeks prior to this hospital admission. Please think back to 4 weeks before you came into the hospital. 1) Overall, how would you rate your health during the 4 weeks before coming to the hospital? 1 = Excellent 2 = Very good 3 = Good 4 = Fair 5 = Poor 6 = Very poor 2) How much did physical health problems limit your usual physical activities (such as walking or climbing stairs) during the 4 weeks before coming to the hospital? 1 = Not at all 2 = Very little 3 = Somewhat 4 = Quite a lot 5 = Could not do physical activities 3) How much difficulty did you have doing your daily work, both at home and away from home, because of your physical health during the 4 weeks before coming to the hospital? 1 = Not at all 2 = A little bit 3 = Some 4 = Quite a lot 5 = Could not do daily work 4) How much bodily pain did you have during the 4 weeks before coming to the hospital? 1 = None 2 = Very mild 3 = Mild 4 = Moderate 5 = Severe 6 = Very severe 143 5) How much energy did you have during the 4 weeks before coming to the hospital? 1 = Very much 2 = Quite a lot 3 = Some 4 = A little 5 = None 6) How much did your physical health or emotional problems limit your usual social activities with family or friends during the 4 weeks before coming to the hospital? 1 = Not at all 2 = Very little 3 = Somewhat 4 = Quite a lot 5 = Could not do social activities 7) How much were you bothered by emotional problems (such as feeling anxious, depressed or irritable) during the 4 weeks before coming to the hospital? 1 = Not at all 2 = Slightly 3 = Moderately 4 = Quite a lot 5 = Extremely 8) How much did personal or emotional problems keep you from doing your usual work, school or other daily activities during the 4 weeks before coming to the hospital? 1 = Not at all 2 = Very little 3 = Somewhat 4 = Quite a lot 5 = Could not do daily activities 144 Appendix III Outcome and Assessment Information Set (OASIS‐B1) For Patients discharged to Extended Care/Rehab Facilities ID # _____________ CASE PROJECT Time of OASIS 1 = Discharge 2 = 2 months Location of patient: 3 = Nursing Home 4 = Rehabilitation Center 5 = Long term acute care (M0290) High Risk Factors characterizing this patient: (Mark all that apply.) 1 - Heavy smoking 2 - Obesity 3 - Alcohol dependency 4 - Drug dependency 5 - None of the above UK - Unknown SUPPORTIVE ASSISTANCE (M0360) Primary Caregiver taking lead responsibility for providing or managing the patient's care, providing the most frequent assistance, etc. (other than ECF): 0 - No one person 1 - Spouse or significant other 2 - Daughter or son 3 - Other family member 4 - Friend or neighbor or community or church member 5 - Paid help 6 - Unknown (M0370) How often does the patient receive assistance from the primary caregiver? 1 2 3 4 5 6 7 - Several times during day and night Several times during day Once daily Three or more times per week One to two times per week Less often than weekly Unknown INTEGUMENTARY STATUS (M0440) Does this patient have a Skin Lesion or an Open Wound? This excludes "OSTOMIES." 0 - No 1 - Yes (M0445) Does this patient have a Pressure Ulcer? 0 - No 1 - Yes 145 RESPIRATORY STATUS (M0500) Respiratory Treatments utilized: (Mark all that apply.) 1 2 3 4 - ADL/IADLs Oxygen (intermittent or continuous) Ventilator (continually or at night) Continuous positive airway pressure None of the above Record what the patient is able to do currently (M0640) Grooming: Ability to tend to personal hygiene needs (i.e., washing face and hands, hair care, shaving or make up, teeth or denture care, fingernail care). 0 1 2 3 4 - Able to groom self unaided, with or without the use of assistive devices or adapted methods. Grooming utensils must be placed within reach before able to complete grooming activities. Someone must assist the patient to groom self. Patient depends entirely upon someone else for grooming needs. Unknown (M0650) Ability to Dress Upper Body (with or without dressing aids) including undergarments, pullovers, front-opening shirts and blouses, managing zippers, buttons, and snaps: 0 1 2 3 4 - Able to get clothes out of closets and drawers, put them on and remove them from the upper body without assistance. Able to dress upper body without assistance if clothing is laid out or handed to the patient. Someone must help the patient put on upper body clothing. Patient depends entirely upon another person to dress the upper body. Unknown (M0660) Ability to Dress Lower Body (with or without dressing aids) including undergarments, slacks, socks or nylons, shoes: 0 1 2 3 4 - Able to obtain, put on, and remove clothing and shoes without assistance. Able to dress lower body without assistance if clothing and shoes are laid out or handed to the patient. Someone must help the patient put on undergarments, slacks, socks or nylons, and shoes. Patient depends entirely upon another person to dress lower body. Unknown (M0670) Bathing: Ability to wash entire body. Excludes grooming (washing face and hands only). 0 1 2 - 3 4 5 6 - Able to bathe self in shower or tub independently. With the use of devices, is able to bathe self in shower or tub independently. Able to bathe in shower or tub with the assistance of another person: (a) for intermittent supervision or encouragement or reminders, OR (b) to get in and out of the shower or tub, OR (c) for washing difficult to reach areas. Participates in bathing self in shower or tub, but requires presence of another person throughout the bath for assistance or supervision. Unable to use the shower or tub and is bathed in bed or bedside chair. Unable to effectively participate in bathing and is totally bathed by another person. Unknown 146 (M0680) Toileting: Ability to get to and from the toilet or bedside commode. 0 1 2 3 4 5 - Able to get to and from the toilet independently with or without a device. When reminded, assisted, or supervised by another person, able to get to and from the toilet. Unable to get to and from the toilet but is able to use a bedside commode (with or without assistance). Unable to get to and from the toilet or bedside commode but is able to use a bedpan/urinal independently. Is totally dependent in toileting. Unknown (M0690) Transferring: Ability to move from bed to chair, on and off toilet or commode, into and out of tub or shower, and ability to turn and position self in bed if patient is bedfast. 0 1 2 3 - 4 5 6 - Able to independently transfer. Transfers with minimal human assistance or with use of an assistive device. Unable to transfer self but is able to bear weight and pivot during the transfer process. Unable to transfer self and is unable to bear weight or pivot when transferred by another person. Bedfast, unable to transfer but is able to turn and position self in bed. Bedfast, unable to transfer and is unable to turn and position self. Unknown (M0700) Ambulation/Locomotion: Ability to SAFELY walk, once in a standing position, or use a wheelchair, once in a seated position, on a variety of surfaces. 0 1 2 3 4 5 6 - Able to independently walk on even and uneven surfaces and climb stairs with or without railings (i.e., needs no human assistance or assistive device). Requires use of a device (e.g., cane, walker) to walk alone or requires human supervision or assistance to negotiate stairs or steps or uneven surfaces. Able to walk only with the supervision or assistance of another person at all times. Chairfast, unable to ambulate but is able to wheel self independently. Chairfast, unable to ambulate and is unable to wheel self. Bedfast, unable to ambulate or be up in a chair. Unknown (M0710) Feeding or Eating: Ability to feed self meals and snacks. Note: This refers only to the process of eating, chewing, and swallowing, not preparing the food to be eaten. 0 1 - 2 3 4 5 6 - Able to independently feed self. Able to feed self independently but requires: (a) meal set-up; OR (b) intermittent assistance or supervision from another person; OR (c) a liquid, pureed or ground meat diet. Unable to feed self and must be assisted or supervised throughout the meal/snack. Able to take in nutrients orally and receives supplemental nutrients through a nasogastric tube or gastrostomy. Unable to take in nutrients orally and is fed nutrients through a nasogastric tube or gastrostomy. Unable to take in nutrients orally or by tube feeding. Unknown 147 (M0770) Ability to Use Telephone: Ability to answer the phone, dial numbers, and effectively use the telephone to communicate. 0 1 - Able to dial numbers and answer calls appropriately and as desired. Able to use a specially adapted telephone (i.e., large numbers on the dial, teletype phone for the deaf) and call essential numbers. Able to answer the telephone and carry on a normal conversation but has difficulty with placing calls. Able to answer the telephone only some of the time or is able to carry on only a limited conversation. Unable to answer the telephone at all but can listen if assisted with equipment. Totally unable to use the telephone. Patient does not have a telephone. 2 3 4 5 6 Medications Total number of oral/inhalant/PR medications Total number of IV/IM/SQ medications 148 CASE PROJECT Appendix IV ENROLLMENT FORM PATIENT ENROLLED: FORM FILLED OUT DURING HOSPITALIZATION Variable ID Randomization Age Description Data Study ID number _____ _____ Randomization 1 = Experimental 2 = Control 99 = Not randomized Age upon hospital admission _____ AGE _____ SEX _____ RACE Gender 1 = Female 2 = Male Race 1 = Caucasian 2 = African American 3 = Other Advance directive 1 = No 2 = Yes 3 = Not documented _____ ADIRECT Marital Status 1 = Married 2 = Not married 3 = Unknown _____ MARIT Living Status Prior to Hospitalization (prior to this acute illness event) Hospital Admission Source 1 = Hospital _____ 2 = Nursing Home PRELIVE 3 = Rehabilitation Center 4 = Long term Acute care 5 = Assisted Living 6 = Home 7 = Other Specify________________________________ 1 = Admitted from ER (UHC) _____ 2 = Admitted from home (or MD office) ADMIT 3 = Transfer from another hospital (Inc ER) 4 = Transfer from nursing home (ECF) 149 Variable Admitting ICU Service Type of Admission Employment Status Prior to Hospitalization Primary Payment Source Secondary Payment Source Description Data 1 = CICU 2 = CTICU 3 = MICU 4 = SICU 5 = NSU ______ ICUADM 1 = Planned 2 = Unplanned 3 = Unknown _____ ADMTYPE 1 = Employed 2 = Retired 3 = Disabled 4 = Not Employed 5 = Other_________________ _____ PREEMPLO 1 = Self 2 = Private insurance 3 = Medicare 4 = Medicaid 5 = Other _____ PAY1 1 = Self 2 = Private insurance 3 = Medicare 4 = Medicaid 5 = Other _____ PAY2 Number of medications Total # of medications ____ taking prior to this acute (highest number listed on admission admission note)(do not inc any OTC drugs) PREMEDS Primary Primary category for reason _____ diagnostic classification for admission (select only one from adm note) ADMCAT 1 = Cardiologic 2 = Neurologic 3 = GI 4 = Genitourinary 5 = Metabolic 6 = Respiratory 7 = Trauma 8 = Hematologic 9 = Infectious Disease 10 = Miscellaneous 150 DESCRIPTION VARIABLE Chronic dialysis 1 = No prior to admission (listed in PMH) 2 = Yes History of diabetes prior to admission(listed in PMH) DATA _____ PREDIALY PREDIAB 1 = No History of stroke _____ 2 = Yes 1 = No _____ prior to admission (listed in PMH) 2 = Yes PRESTROK History of CHF(must be stated “CHF”) 1 = No _____ prior to admission (listed in PMH) 2 = Yes PRECHF Number of pre‐existing Count the number of med/surg _____ Med/surg conditions diagnoses listed in chart (PMH/PSH) PREXIST (not smoking/do not combine med‐surg cond)(not including primary condition) Please list all those documented in chart 1._____________________________ 2._____________________________ 3._____________________________ 4._____________________________ 5._____________________________ 6._____________________________ 7._____________________________ 8______________________________ 9.______________________________ 10._____________________________ 11._____________________________ 12._____________________________ Precipitating Factor 1 = Routine operative ventilation _____ for Mechanical 2 = Respiratory insuff, distress, failure VENT Ventilation 3 = Neuro impairment/ airway protection 4 = Post‐arrest Reason for MV > 3d – information to be obtained from progress notes from day 1‐4 of MV Pneumonia 1 = No (states“pneumonia” or “suspect/possible 2 = Yes pneumonia”, or “infiltrate” more than once) Multi‐organ system failure 1 = No (should state “MOSF” also cannot check 2 = Yes “yes” for “cardiac” (heart failure), “sepsis” or “other” such as liver failure, renal failure, or other major system failure) _____ REAPNE REAMOSF _____ 151 ARDS (should state “ARDS”) Impaired neuro status (including encephapathy) Cardiac (must state“CHF” or “cardiogenic shock”) 1 = No 2 = Yes _____ REAARDS 1 = No _____ 2 = Yes REANEU 1 = No 2 = Yes _____ REACARD 1 = No 2 = Yes _____ REASEP Sepsis (should state “sepsis”) Fluid Overload (should state “fluid overload”) Other pulmonary 1 = No 2 = Yes ______ FLDOVER 1 = No 2 = Yes ______ REAPULM COPD, restrictive/obstructive disease, emphysema, asthma, lung mass, PE pulmonary edema, mucous plugging) 1 = No 2 = Yes ______ REAOTH **** Other (includes obesity, metabolic imbalances, esophageal disease, quad above C2, “malnutrition” if it is stated as a cause of prolonged MV) (please specify)____________________________________ Admission Date / / MM DD YR Discharge Date / / ADMDATE **** (or death date if MM DD YR DCDATE in‐hospital death) Length of hospital stay Date of hospital admission‐Date d/c (or death) **** *Computer Calculates* LOS Vent Episodes Pt. on vent until interrupted period >24 hrs. 1st period of vent: from: ___/___/___ To:___/___/___ 2nd period of vent: from:___/___/___ To:___/___/___ 3rd period of vent: from:___/___/___ To:___/___/___ 4th period of vent: from:___/___/___ To:___/___/___ 152 Length of mechanical vent (inc those days before pt qualifies for study) Total # of days on vent LOMV Episodes of ventilation Total # of episodes on vent (inc those episodes before pt qualifies for study) ICU episodes Time spent in ICU 1st stay from: ___/___/___ 2nd stay from:___/___/___ 3rd stay from:___/___/___ 4th stay from:___/___/___ *Length of ICU stay _____ _____ EPIMV To:___/___/___ To:___/___/___ To:___/___/___ To:___/___/___ Total amount of time in the ICU _____ ICUS *wherever the patient is at 12 midnight is their location Major surgery 1 = No during this 2 = Yes SURG hospitalization (including this episode of care if transferred from OSH) (exclude trachs, Pegs, PICCS, dialysis access) If yes to surgery, what type of surgery was the first surgery? Type of Surgery 1 = Elective _____ 2 = Emergent TYSURG If more than 1 major surgery, please list__________________________________ Discharge Information: Location of 1 = CICU patient at discharge 2 = CTICU 3 = MICU 4 = SICU 5 = NSU 6 = Regular nursing division 99 = Not applicable/died in the hospital _____ DCLOC Number of transfers As is _____ TRANS Primary admission As is diagnosis ICD1 (ICD‐9) 153 Number of Secondary ICD‐9s Add up # of 20 ICD‐9s Disposition: 1 = Hospital 2 = Nursing Home 3 = Rehabilitation Center 4 = Long term Acute Care 5 = Assisted Living 6 = Home without home care 7 = Death (did not survive index hospitalization) 8 = Other Specify___________________________ 9 = Home with home care _____ ICD2 _____ DISP Hospice Services 1 = No _____ 2 = Yes HOSP Reason for Disposition ___________________________________ (e.g. why was the patient dc’d to nursing home) Discharge location 1 = < 30 miles _____ distance for pts 2 = 30‐80 miles DCDIS in the DC portion 99 = not applicable of the study DNR at discharge 1 = No _____ 2 = Yes DNRDC 3 = Unknown 99 = Not applicable Discharge oxygen status 1 = Without oxygen at all 2 = On oxygen Specify (Liters)___________ 99=Not applicable _____ DOXYGEN Discharge vent status 1 = None 2 = Partial < 24 hrs. mechanical vent 3 = Full 24 hr. 4 = Other Specify ____________ 99=Not applicable _____ DVENT 154 1 = No _____ *If DC’d to #1‐4(variable‐“DISP”) on vent, is it expected 2 = Yes DWEAN that active weaning will 99=Not applicable take place at the facility? Discharge trach status 1 = No 2 = Yes 99=Not applicable _____ DTRACH Was consent refused? 1 = No 2 = Yes 3 = Pt/family never approached (pt. ineligible _____ for post‐dc portion of study due to death etc.) REFUSE‐DID If YES, reason for refusal: ________________________________________________ If consent was refused, 1 = Patient who refused consent? 2 = Family/significant other/proxy 3 = MD 4 = Other 99 = not applicable _____ WHOREF‐DID 155 Bibliography Ahlberg, K., Ekman, T., Wallgren, A., & Gaston-Johansson, F. (2004). Fatigue, psychological distress, coping and quality of life in patients with uterine cancer. Journal of Advanced Nursing, 45, 205-213. Ahrens, T., Yancey, V., & Kollef, M. (2003). Improving family communications at the end-of-life: Implications for length of stay in the Intensive Care Unit and resource use. American Journal of Critical Care, 12, 317-324. Almanza, J., Downhill, J., & Nierman, D. (2000). Psychiatric disorders in chronically critically ill patients in a respiratory care unit. Psychosomatics, 41, 159. Armstrong, T. (2003). Symptoms experience: A concept analysis. Oncology Nursing Forum, 30, 601-606. Amaya-Villar, R., Garnacho-Montero, J., Garcia-Garmendia, J., Madrazo-Osuna, J., Garnacho-Montero, M., Luque, R., et al (2005). Steroid-induced myopathy in patients intubated due to exacerbation of chronic obstructive pulmonary disease. Intensive Care Medicine, 31, 157-161. Balkwill, F. (2000). Cytokine molecular biology: a practical approach. (3rd ed.). New York: Oxford University Press. Barnes, S., Gott, M., Payne, S., Parker, C., Seamark, D., Bariballa, S. et al. (2006). Prevalence of symptoms in a community-based sample of heart failure patients. Journal of Pain and Symptom Management, 32, 208-216. Barsevick, A., Whitmer, K., Nail, L., Beck, S., & Dudley, W. (2006). Symptom cluster research: Conceptual, design, measurement, and analysis issues. Journal of Pain and Symptom Management, 31, 85-95. 156 Bennett, S., Perkins, S., Lane, K., Deer, M., Brater, D., & Murray, M. (2001). Social support and health-related quality of life in chronic heart failure patients. Quality of Life Research, 10, 671-682. Bergbom-Engberg, I. & Haljamae, H. (1989). Assessment of patients’ experience of discomforts during respiratory therapy. Critical Care Medicine, 17, 1068-1072. Bernhard, J., Maibach, R., Thurlimann, B., Sessa, C., & Aapro, M. (2002). Patients’ estimation of overall treatment burden: Why not ask the obvious? Journal of Clinical Oncology, 20, 65-72. Bernhard, J., Sullivan, M., Hurny, C., Coates, A., & Rudenstam, C. (2001). Clinical relevance of single item quality of life indicators in cancer clinical trials. British Journal of Cancer, 84, 1156-1165. Bigatello, L., Stelfox, H., Berra, L., Schmidt, U., & Gettings, E. (2007). Outcome of patients undergoing prolonged mechanical ventilation after critical illness. Critical Care Medicine, 35, 2491-2497. Block, S. (2000). Assessing and managing depression in the terminally ill patient. Annals of Internal Medicine, 132, 209-218. Bosworth, H., Steinhauser, K., Orr, M., Lindquist, J., Grambow, S., & Oddone, E. (2004). Congestive heart failure patients’ perceptions of quality of life: the integration of physical and psychosocial factors. Aging and Mental Health, 8, 83-91. Bowles, K. & Cater, J. (2003). Screening for risk of rehospitalization from home care: Use of the Outcomes Assessment Information Set and the Probability of Readmission Instrument. Research in Nursing and Health, 26, 118-127. 157 Bowling, A. (2005). Just one question: If one question works, why ask several? Journal of Epidemiology and Community Health, 59, 342-345. Burnes, N. & Grove, S. (2001). The practice of nursing research: Conduct, critique and utilization. Philadelphia: W.B. Saunders Co. Campbell, M. (2007). Fear and pulmonary stress behaviors to an asphyxial threat across cognitive states. Research in Nursing and Health, 30, 572-583. Campbell, M. (2008). Psychometric testing of a respiratory distress observation scale. Journal of Palliative Medicine, 11, 44-50. Carels, R. (2004). The association between disease severity, functional status, depression, and daily quality of life in congestive heart failure patients. Quality of Life Research, 13, 63-72. Carey, C., Lee, H., & Woeltje, K. (Eds.). (1998). The Washington Manual of Medical Therapeutics. Philadelphia: Lippincott-Raven. Carson, S. (2006). Outcomes of prolonged mechanical ventilation. Current Opinion in Critical Care, 12, 405-411. Carson, S. & Bach, P. (2001). Predicting mortality in patients suffering from prolonged critical illness: an assessment of four severity-of-illness measures. Chest, 120, 928-933. Carson, S. & Bach, P. (2002). The epidemiology and costs of chronic critical illness. Critical Care Clinics, 18, 461-76. Carson, S., Bach, P., Brzozowski, L., & Leff, A. (1999). Outcomes after long-term acute care. An analysis of 133 mechanically ventilated patients. American Journal of Respiratory and Critical Care Medicine, 159, 1568-1573. 158 Chanques, G., Jaber, S., Barbotte, E., Violet, S., Sebbane, M., Perrigault, P., et al. (2006). Impact of systematic evaluation of pain and sedation in an intensive care unit. Critical Care Medicine, 34, 1691-1699. Chelluri, L., Im, K., Belle, S., Schulz, R., Rotondi, A., Donohoe, M., et al., (2004). Long-term mortality and quality of life after prolonged mechanical ventilation. Critical Care Medicine, 32, 61-69. Chernecky, C., Sarna, L, Waller, J., & Brecht, M. (2004). Assessing coughing and wheezing in lung cancer: A pilot study. Oncology Nursing Forum, 31, 1095-1101. Clarke, S. & Cossette, S. (2000). Secondary analysis: Theoretical, methodological, and practical considerations. Canadian Journal of Nursing Research, 32, 109-129. Clarke, S., Frasure-Smith, Lesperance, & Bourassa, M. (2000). Psychosocial factors as predictors of functional status at 1 year in patients with left ventricular dysfunction. Research in Nursing and Health, 23, 290-300. Cleeland, C., Bennett, G., Dantzer, R., Dougherty, P., Dunn, A., Meyers, C. et al. (2003). Are the symptoms of cancer and cancer treatment due to a shared biologic mechanism? A cytokine-immunological model of cancer symptoms. Cancer, 97, 2919-2925. Cleeland, C. (2004). Assessment of pain in cancer. Journal of the National Cancer Institute Monographs, 32, 79. Cleeland, C., Mendoza, T., Wang, X., Chou, C. Harle, M., Morrissey, M. et al. (2000). Assessing symptom distress in cancer patients: The M.D. Anderson Symptom Inventory. Cancer, 89, 1634-1646. 159 Cleeland, C. & Reyes-Gibby, C. (2002). When is it justified to treat symptoms? Measuring symptom burden. Oncology, 16, 64-70. Cohen, M. Williams, L, Knight, P, Snider, J., Hanzik, K., & Fisch, M. (2004). Symptom masquerade: Understanding meaning of symptoms. Support Care Cancer, 12, 184-190. Cole, S., Kawachi, I., Maller, S., & Berkman, L. (2000). Test of item-response bias in the CES-D scale: Experience from the New Haven EPESE Study. Journal of Clinical Epidemiology, 53, 285-289. Combes, A., Costa, M., Trouillet, J., Baudot, J., Mokhtari, M., Gibert, C., et al. (2003). Morbidity, mortality, and quality-of-life outcomes of patients requiring ≥ 14 days of mechanical ventilation. Critical Care Medicine, 31, 1373-1381. Combes, A., Luyt, C., Nieszkowska, A., Trouillet, J., Gibert, C. & Chastre, J. (2007). Is tracheostomy associated with better outcomes for patients requiring long-term mechanical ventilation? Critical Care Medicine, 35, 802-807. Connelly, B., Gunzerath, L., & Knebel, A. (2000). A pilot study exploring mood state and dyspnea in mechanically ventilated patients. Heart and Lung, 29, 173-179. Cooper, A., Gabor, J., & Hanly, P. (2001). Sleep in the critically ill patient. Seminars in Respiratory and Critical Care Medicine, 22, 153-163. Cooper, A., Thornley, K., Young, G., Slutsky, A., Stewart, T., Hanly, P. (2000). Sleep in critically ill patients requiring mechanical ventilation. Chest, 117, 809-818. Curfs, J., Meis, J., & Korstanje-Hoogkami, A. (1997). A primer on cytokines: sources, effects, and inducers. Clinical Microbiological Review, 10, 742-780. 160 Cutler, R., Fishbain, D., Steele-Rosomoff, R., & Rosomoff, H. (2003). Relationships between functional capacity measures and baseline psychological measures in chronic pain patients. Journal of Occupational Rehabilitation, 13, 249-258. Daly, B. (2005). RO1. Intensive communication intervention in chronically critically ill. [Incidence of PEG tubes]. Unpublished raw data. Daly, B., Douglas, S., Kelley, C., O'Toole, E., & Montenegro, H. (2005). Trial of a disease management program to reduce hospital readmissions of the chronically critically ill. Chest, 128, 507-517. Daly, B., Phelps, W., & Rudy, E. (1991). A nurse-managed special care unit. Journal of Nursing Administration, 21, 31-38. Danis, M. (2004). How will we respond to chronic critical illness? Critical Care Medicine, 32, 1617-1618. Dantzer, R. (2001). Cytokine sickness behavior: mechanisms and implications. Annals of New York Academy of Sciences, 993, 222-234. Davidson, J., Powers, K., Hedayat, K., Tieszen, M., Kon, A., Shepard, E., et al. (2007). Clinical practice guidelines for support of the family in the patient-centered Intensive Care Unit: American College of Critical Care Medicine Task Force 2004-2005. Critical Care Medicine, 35, 605-622. de Boer, A., van Lanschot, J., Stalmeier, P, van Sandick, J., Hulshcer, J. de Haes, J. et al. (2004). Is a single-item visual analogue scale as valid, reliable and responsive as multi-item scales in measuring quality of life? Quality of Life Research, 13, 311320. DeJonghe, B., Lacherade, J-C., Durand, M-C., & Sharshar, T. (2007). Critical illness 161 neuromuscular syndromes. Critical Care Clinics, 23, 55-69. De Jonghe, B., Sharshar, T., Lefaucheur, J., Authier, F., Durand-Zaleski, I., Boussarsar, M., et al., (2002). Paresis acquired in the intensive care unit: a prospective multicenter study. JAMA, 288, 2859-2867. Desbiens, N., Mueller-Rizner, N., Connors, A., Wenger, N., & Lynn, J. (1999). The symptom burden of seriously ill hospitalized patients. Journal of Pain and Symptom Management, 17, 248-255. Desbiens, N., Wu, A., Broste, S., Wenger, N., Connors, A., Lynn, J. et al. (1996). Pain and satisfaction with pain control in seriously ill hospitalized adults: findings from the SUPPORT research investigations. For the SUPPORT investigators. Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment. Critical Care Medicine, 24, 1953-1961. Dinarello, C. (2000). Proinflammatory cytokines. Chest, 118, 503-508. DiSario, J., Baskin, W., Brown, R., DeLegge, M., Fang, J., Ginsberg, G., et al (2002). Endoscopic approaches to enteral nutritional support. Gastrointestinal Endoscopy, 55, 901-908. Dodd, M., Janson, S., Facione, N., Faucett, J., Froelicher, E., Humphreys, J., et al. (2001). Advancing the science of symptom management. Journal of Advanced Nursing, 33, 668-676. Dodd, M., Miaskowski, C., & Lee, K. (2004). Occurrence of symptom clusters. Journal of the National Cancer Institute Monographs, 32, 76-78. Dodd, M., Miaskowski, C., & Paul, S. (2001). Symptom clusters and their effect on the functional status of patients with cancer. Oncology Nursing Forum, 28, 162 465-470. Douglas, S. & Daly, B. (2003). Caregivers of long-term ventilator patients: physical and psychological outcomes. Chest, 123, 1073-1081. Douglas, S., Daly, B., Brennan, P., Gordon, N., & Uthis, P. (2001). Hospital readmission among long-term ventilator patients. Chest, 120, 1278-1286. Douglas, S., Daly, B., Brennan, P., Harris, S., Nochomovitz, M., & Dyer, M.A. (1997). Outcomes of long-term ventilator patients: A descriptive study. American Journal of Critical Care, 6, 99-105. Douglas, S., Daly, B., Genet Kelley, C., O’Toole, E., & Montenegro, H. (2005). Impact of a disease management program upon caregivers of chronically critically ill patients. Chest, 128, 3925-3936. Douglas, S., Daly, B., Genet Kelley, C., O’Toole, E., & Montenegro, H. (2007). Chronically critically ill patients: Health-related quality of life and resource use after a disease management intervention. American Journal of Critical Care, 16, 447-457. Douglas, S., Daly, B., Gordon, N., & Brennan, P. (2002). Survival and quality of life: Short-term versus long-term ventilator patients. Critical Care Medicine, 30, 26552662. Druschky, A., Herkert, M., Radespiel-Troger, M., Druschky, K., Hund, E., Becker, C., et al. (2001). Critical illness polyneuropathy: clinical findings and cell culture assay of neurotoxicity assessed by a prospective study. Intensive Care Medicine, 27, 686-693. Ely, E., Gautam, S., Margolin, R., Francis, J., May, L., Speroff, T. et al. (2001). 163 The impact of delirium in the intensive care unit on hospital length of stay. Intensive Care Medicine, 27, 1297-1304. Engoren, M., Arslanian-Engoren, C., & Fenn-Buderer, N. (2004). Hospital and long-term outcome after tracheostomy for respiratory failure. Chest, 125, 7-9. Epstein, J. & Breslow, M. (1999). The stress response of critical illness. Critical Care Clinics, 15, 17-33. Erdfelder, E., Faul, R., & Buchner, A. (1996). GPOWER: A general power analysis program. Behavior Research Methods, Instruments, & Computers, 28, 1-11. Estenssoro, E., Reina, R., Canales, H., Saenz, M., Gonzalez, F., Aprea, M. et al. (2006). The distinct clinical profile of chronically critically ill patients: a cohort study. Critical Care, 10, 1-9. Finocchiaro, C., Galletti, R., Rovera, G., Ferrari, A., Todros, L, Vuolo, A., et al (1997). Percutaneous endoscopic gastrostomy: a long-term follow-up. Nutrition, 13, 520-523. Freedman, N., Gazendam, J., Levan, L, Pack, A., & Schwab, R. (2001). Abnormal sleep/wake cycles and the effect of environmental noise on sleep disruption in the intensive care unit. American Journal of Respiratory and Critical Care Medicine, 163, 451-457. Freedman, N., Kotzer, N., & Schwab, R. (1999). Patient perception of sleep quality and etiology of sleep disruption in the intensive care unit. American Journal of Respiratory and Critical Care Medicine, 159, 1155-1162. Fried, T., Bradley, E., Towle, V., & Allore, H. (2002). Understanding the treatment 164 preferences of seriously ill patients. New England Journal of Medicine, 346, 1061-1066. Fortinsky, R., Garcia, R., Sheehan, J., Madigan, E., & Tullai-McGuinness, S. (2003). Measuring disability in Medicare home care patients: Application of Rasch Modeling to the Outcome and Assessment Information Set. Medical Care, 41, 601-615. Fox, S., Lyon, D., & Farace, E. (2007). Symptom clusters in patients with high-grade glioma. Journal of Nursing Scholarship, 39, 61-67. Gabor, J., Cooper, A., Crombach, S., Lee, B., Kadikar, N., Bettgar, H., et al. (2003). Contribution of the intensive care unit environment to sleep disruption in mechanically ventilated patients and healthy subjects. American Journal of Respiratory and Critical Care Medicine, 167, 708-715. Gallagher, R. & Verma, S. (1999). Managing pain and comorbid depression: A public health challenge. Seminar of Clinical Neuropsychiatry, 4, 203-230. Garland, A., Dawson, N., Thomas, C., Phillips, R., Tsevat, J., Desbiens, N. et al., (2004). Outcomes up to 5 years after severe, acute respiratory failure. Chest, 126, 1897-1904. Garnacho-Montero, J., Amaya-Villar, R., Garcia-Garmendia, J., Madrazo-Osuna, J., & Ortiz-Leyba, G. (2005). Effect of critical illness polyneuropathy on the withdrawal from mechanical ventilation and the length of stay in septic patients. Critical Care Medicine, 33, 349-354. Garnacho-Montero, J., Madrazo-Osuna,J., Garcia-Garmendia, J., Ortiz-Leyba, C., 165 Jimenez-Jimenez, F., Barrero-Almodovar, A., et al. (2001). Critical illness polyneuropathy: risk factors and clinical consequences. A cohort study in septic patients. Intensive Care Medicine, 27, 1288-1296. Gaston-Johansson, F., Fall-Dickson, J., Bakos, A., Kennedy, J. (1999). Fatigue, pain and depression in pre-autotransplant breast cancer patients. Cancer Practice, 7, 240247. Geary, P., Tringali, R., & George, E. (1997). Social support in critically ill adults: A replication. Critical Care Nursing Quarterly, 20, 34-41. Gelinas, C., Fortier, M., Viens, C., Fillion, L., & Puntillo, K. (2004). Pain assessment and management in critically ill intubated patients: A retrospective study. American Journal of Critical Care, 13, 126-135. Gift, A., Jablonski, A., Stommel, M., & Given, C. (2004). Symptom clusters in elderly patients with lung cancer. Oncology Nursing Forum, 31, 203-212. Gift, A. & McCrone, S. (1993). Depression in patients with COPD. Heart and Lung, 22, 289-297. Girard, K. & Raffin, T. (1985). The chronically critically ill: to save or let die? Respiratory Care, 30, 339-347. Given, C., Given, C., Azzouz, F., & Stommel, M. (2001). Physical functioning of elderly cancer patients prior to diagnosis and following initial assessment. Nursing Research, 50, 222-232. Given, C., Given, B., Azzouz, F., Kozachik, & Stommel, M. (2001). Predictors of pain and fatigue in the year following diagnosis among elderly cancer patients. Journal of Pain and Symptom Management, 21, 456-466. 166 Gogos, C., Drosou, E., Bassaris, H., & Skoutelis, A. (2000). Pro- versus antiinflammatory cytokine profile in patients with severe sepsis: a marker for prognosis and future therapeutic options. Journal of Infectious Diseases, 181, 176-180. Gordon, B., Parker, T., Levine, D., Saal, S., Wang, J., Sloan, B., et al. (2001). Relationship of hypolipidemia to cytokine concentrations and outcomes in critically ill surgical patients. Critical Care Medicine, 29, 1563-1568. Granger, C., Cotter, A., Hamilton, B., Fiedler, R., & Hens, M. (1990). Functional assessment scales: A study of persons with multiple sclerosis. Archives of Physical Medicine Rehabilitation, 71, 870-875. Hamill-Ruth, R. (2006). Managing pain and sedation in the critically ill. Critical Care Medicine, 34, 1838-1839. Hardin, S. (2002). Financial impact. In I. Lubkin & P. Larsen (Eds.), Chronic Illness, Impact and Interventions (pp. 471-490). Boston: Jones and Bartlett Publishers. Holland, C., Cason, C., & Prater, L. (1997). Patients’ recollections of critical care. Dimensions of Critical Care Nursing, 16, 132-141. Heyland, D., Konopad, E., Noseworthy, T., Johnston, R., & Gafni, A. (1998). Is it worthwhile to continue treating patients with a prolonged stay (>14 days) in the ICU? Chest, 114, 192-198. Higgins, P. (1998). Patient perception of fatigue while undergoing long-term mechanical ventilation: incidence and associated factors. Heart and Lung, 27, 177-183. Higgins, P. (2001). RO1. Adult failure to thrive in long-term ventilator patients. [Social support in long-term ventilator patients]. Unpublished raw data. 167 Higgins, P. & Daly, B. (1999). Research methodology issues related to interviewing the mechanically ventilated patient. Western Journal of Nursing Research, 21, 773784. Higgins, P., Daly, B., Lipson, A., & Guo, Su-Er (2006). Assessing nutritional status in chronically critically ill patients. American Journal of Critical Care, 15, 166-177. Higgins, P., Winkelman, C., Lipson, A., Guo, S., & Rodgers, J. (2007). Light measurement in the hospital: a comparison of two methods. Research in Nursing and Health, 30, 120-128. Hopkins, R., & Jackson, J. (2006). Long-term neurocognitive function after critical illness. Chest, 130, 869-878. Hudson, A., Lee, K., & Portillo, C. (2003). Symptom experience and functional status among HIV-infected women. AIDS Care, 15, 483-492. Hupcey, J. (2001). The meaning of social support for the critically ill patient. Intensive Critical Care Nursing, 17, 206-212. Hurny, C., Bernhard, J., Coates, A., Peterson, H., Castiglione-Gertsch, M., Gelber, R., et al. (1996). Responsiveness of a single-item indicator versus multi-item scale: Assessment of emotional well-being in an international adjuvant breast cancer trial. Medical Care, 34, 234-248. http://www.cms.hhs.gov http://www.ninr.nih.gov Im, K., Belle, S., Schulz, R., Mendelsohn, A., & Chelluri, L. (2004). Prevalence and 168 outcomes of caregiving after prolonged (≥ 48 hours) mechanical ventilation in the ICU. Chest, 125, 597-606. Jacobs, P. & Noseworthy, T. (1990). National estimates of intensive care utilization and costs: Canada and the United States. Critical Care Medicine, 18, 1282-1286. Joynt, K., Whellan, D., & O’Connor, C. (2004). Why is depression bad for the failing heart? A review of the mechanistic relationship between depression and heart failure [Abstract]. Journal of Cardiac Failure, 10, 258-271. Katon, W., Lin, E., & Kroenke, K. (2007). The association of depression and anxiety with medical symptom burden in patients with chronic medical illness. General Hospital Psychiatry, 29, 147-155. Kiecolt, K. & Nathan, L. (1985). Secondary analysis of survey data [Quantitative Applications in the social science series No.52]. Beverly Hills, CA: Sage. Kim, H-J., McGuire, D., Tulman, L., & Barsevick, A. (2005). Symptom clusters. Concept analysis and clinical implications for cancer nursing. Cancer Nursing, 28, 270282. Kirsch, K., Passik, S., Holtsclaw, E., Donaghy, K., & Theobald, D. (2001). I get tired for no reason: A single item screening for cancer-related fatigue. Journal of Pain and Symptom Management, 22, 931-937. Klinkenberg, M., Willems, D.L., van der Wal, G., & Deeg, J.H. (2004). Symptom burden in the last week of life. Journal of Pain and Symptom Management, 27, 5-13. Knebel, A., Janson-Bjerklie, S., Malley, J., Wilson, A., & Marini, J. (1994). Comparison of breathing comfort during weaning with two ventilatory modes. American Journal of Respiratory Critical Care Medicine, 149, 14-18. 169 Korkeila, M., Ruokonen, E., & Takala, J. (2000). Costs of care, long-term prognosis and quality of life in patients requiring renal replacement therapy during intensive care. Intensive Care Medicine, 26, 1824-1831. Kurtz, M., Kurtz, J., Stommel, M., Given, C., & Given, B. (2000). Symptomatology and loss of physical functioning among geriatric patients with lung cancer. Journal of Pain and Symptom Management, 19, 249-256. Kurzrock, R. (2001). The role of cytokines in cancer-related fatigue. Cancer, 92, 16841688. Kutner, J. S., Kassner, C. T., & Nowels, D. E. (2001). Symptom burden at the end of life: Hospice providers’ perceptions. Journal of Pain and Symptom Management, 21, 473-480. Kutner, J., Bryant, L., Beaty, B., & Fairclough, D. (2006). Symptom distress and qualityof-life assessment at the end of life: The role of proxy response. Journal of Pain and Symptom Management, 32, 300-310. Landis, J. & Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159-174. Langen, R., Korn, S., & Wouters, E. (2005). Serial review: role of reactive oxygen and nitrogen species (ROS/RNS) in lung injury and diseases. Free Radicals and Biological Medicine, 35, 226-235. Leavey, S., Strawderman, R., Jones, C., Port, F., & Held, P. (1998). Simple nutritional indicators as independent predictors of mortality in hemodialysis patients. American Journal of Kidney Disease, 31, 997-1006. 170 Leijten, F., de-Weerd, A., Poortvliet, D., De Ridder, V., Ulrich, C., Harink-De Weerd, J. (1996). Critical illness polyneuropathy in multiple organ dysfunction syndrome and weaning from the ventilator. Intensive Care Medicine, 22, 856-861. Lenz, E., Pugh, L., Milligan, R., Gift, A. & Suppe, F. (1997). The middle-range theory of unpleasant symptoms: an update. Advanced Nursing Science, 19, 14-27. Lenz, E., Suppe, F., Gift, A., Pugh, A. & Milligan, R. (1995). Collaborative development of middle-range nursing theories: Toward a theory of unpleasant symptoms. Advances in Nursing Science, 17, 1-13. Li, D. & Puntillo, K. (2006). A pilot study on coexisting symptoms in intensive care patients. Applied Nursing Research, 19, 216-219. Lilly, C. & Daly, B. (2007). The healing power of listening in the ICU. New England Journal of Medicine, 356, 513-514. MacIntyre, N., Epstein, S., Carson, S., Scheinhorn, D., Christopher, K. & Muldoon, S. (2005). Management of patients requiring prolonged mechanical ventilation: report of a NAMDRC consensus conference. Chest, 2005, 3937-3954. Madigan, E. & Fortinsky, R. (2000). Additional psychometric evaluation of the Outcomes and Assessment Information Set (OASIS). Home Health Care Services Quarterly, 18, 49-62. Madigan, E.& Fortinsky, R. (2004). Interrater reliability of the Outcomes and Assessment Information Set: results from the field. The Gerontologist, 44, 689-692. Madigan, E., Tullai-McGuinness, S., & Fortinsky, R. (2003). Accuracy in the Outcomes and Assessment Information Set (OASIS): results of a video simulation. Research in Nursing and Health, 26, 273-283. 171 Mallis, M. & DeRoshia, C. (2005). Circadian rhythms, sleep, and performance in space. Aviation Space Environmental Medicine, 76, B94-107. Marshall, J., Finn, C., & Theodore, A. (2008). Impact of a clinical pharmacist-enforced intensive care unit sedation protocol on duration of mechanical ventilation and hospital stay. Critical Care Medicine, 36, 427-433. Mauthe, R., Haaf, D., Hayn, P., & Krall, J. (1996). Predicting discharge destination of Stroke patients using a mathematical model based on six items from the Functional Independence Measure. Archives of Physical Medicine Rehabilitation, 77, 10-13. McMahon, M., Hurley, D., Kamath, P., & Mueller, P. (2005). Medical and ethical aspects of long-term enteral tube feedings. Mayo Clinic Proceedings, 80, 1461-1476. McPherson, C. & Addington-Hall, J. (2003). Judging the quality of care at the end-oflife: Can proxies provide reliable information? Social Science and Medicine, 56, 95-109. McWilliams, L., Cox, B., & Enns, M. (2003). Mood and anxiety disorders associated with chronic pain: an examination in a nationally representative sample. Pain, 106, 127-133. Merriam-Webster’s collegiate dictionary (9th ed.) (1991). Springfield, MA: MerriamWebster. Meyer, T., Eveloff, S., Bauer, M., Schwartz, W., Hill, N., & Millman, R. (1994). Adverse environmental conditions in the respiratory and medical ICU settings. Chest, 105, 1211-1216. 172 Meyers, C., Seabrooke, L, Albitar, M. & Estey, E. (2003). Association of cancer-related symptoms with physiological parameters: A case report [letter to the editor]. Journal of Pain and Symptom Management, 24, 359-361. Muller Kobold, A., Tulleken, J., Zijistra, J., Sluiter, W., Hermans, J., Kallenberg, C., et al. (2000). Leukocyte activation in sepsis: correlations with disease state and mortality. Intensive Care Medicine, 26, 1824-1831. Mundigler, G., Delle-Karth, G., Koreny, M., Zehetgruber, M., Steindi-Munda, P., Marktl, W., et al. (2002). Impaired circadian rhythm of melatonin secretion in sedated critically ill patients with severe sepsis. Critical Care Medicine, 30, 536-540. Murberg, T. & Bru, E. (2001). Social relationships and mortality in patients with congestive heart failure. Journal of Psychosomatic Research, 51, 521-527. Mutlu, G., Mutlu, E., & Factor, P. (2001). GI complications in patients receiving mechanical ventilation. Chest, 119, 1222-1241. Nasraway, S., Button, G., Rand, W., Hudson-Jinks, T., & Gustafson, M. (2000). Survivors of catastrophic illness: Outcome after direct transfer from intensive care to extended care facilities. Critical Care Medicine, 28, 19-25. Nelson, J. (2002). Palliative care of the chronically critically ill patient. Critical Care Clinics, 18, 659-81. Nelson, J., Meier, D., Litke, A., Natale, D.,Siegel, R., & Morrison, R. (2004). The symptom burden of chronic critical illness. Critical Care Medicine, 32, 1527-34. Nelson, J., Meier, D., Oei, E., Nierman, D., Senzel, R., Manfredi, P., et al. 173 (2001). Self-reported symptom experience of critically ill cancer patients receiving intensive care. Critical Care Medicine, 29, 277-282. Nelson, J., Tandon, N., Mercado, A., Camhi, S., Wesley, E., & Morrison, R. (2006). Brain dysfunction: Another burden for the chronically critically ill. Archives of Internal Medicine, 166, 1993-1999. Nierman, D. (2002). A structure of care for the chronically critically ill. Critical Care Clinics, 18, 477-491. Nierman, D. & Mechanick, J. (1998). Bone hyperresorption is prevalent in chronically critically ill patients. Chest, 14, 954-955. Nierman, D. & Mechanick, J. (1999). Hypotestosteronemia in chronically critically ill men. Critical Care Medicine, 27, 2418-2412. Novaes, M., Aronovich, A., Ferraz, M., Knobel, F. (1997). Stressors in the ICU: Patients’ evaluation. Intensive Care Medicine, 23, 1282-1285. Opal, S. & DePalo, V. (2000). Anti-inflammatory cytokines. Chest, 117, 1162-1172. Payen, J., Bru, O., Bosson, J., Lagrasta, A., Novel, E., Deschaux, I. et al. (2001). Assessing pain in critically ill sedated patients by using a behavioral pain scale. Critical Care Medicine, 29, 2258-2263. Puntillo, K. (1990). Pain experiences of intensive care unit patients. Heart Lung, 19, 526-33. Puntillo, K. (1994). Dimensions of procedural pain and its analgesic management in critically ill surgical patients. American Journal of Critical Care, 3, 116-122. Puntillo, K., Morris, A., Thompson, C., Stanik-Hutt, J., White, C., & Wild, L. (2004). Pain behaviors observed during six common procedures: Results from 174 Thunder Project II. Critical Care Medicine, 32, 421-427. Puntillo, K., White, C., Morris, A., Perdue, S., Stanik-Hutt, J., Thompson, C. et al. (2001). Patients' perceptions and responses to procedural pain: results from Thunder Project II. American Journal of Critical Care, 10, 238-51. Quality of Life After Mechanical Ventilation in the Aged Study Investigators, (2002). 2month mortality and functional status of critically ill adult patients receiving prolonged mechanical ventilation. Chest, 121, 549-558. Rabuel, C., Renaud, E., Brealey, D., Ratajczak, P, Damy, T., Alves, A., et al (2004). Human septic myopathy: induction of cycloxygenase, heme oxygenase and activation of the ubiquitin proteolytic pathway. Anesthesiology, 101, 583-590. Redeker, N. (2000). Sleep in acute care settings: An integrative review. Journal of Nursing Scholarship, 32, 31-38. Reishtein, J. (2005). Relationship between symptoms and functional performance in COPD. Research in Nursing and Health, 28, 39-47. Rhodes, V. & Watson, P. (1987). Symptom distress: The concept past and present. Seminars in Oncology Nursing, 3, 242-247. Rosenberg, A. Zimmerman, J., Alzola, C., Draper, E., & Knaus, W. (2000). Intensive care length of stay: recent changes and future challenges. Critical Care Medicine, 28, 3465-3473. Rotondi, A., Chelluri, L, Sirio, C., Mendelsohn, A., Schulz, R., Belle, S., et al. (2002). Patients’ recollections of stressful experiences while receiving mechanical ventilation in an intensive care unit. Critical Care Medicine, 30, 746-752. Schaeffer, N. & Presser, S. (2003). The science of asking questions. Annual Review of 175 Sociology, 29, 65-88. Shaughnessy, P., Schlenker, R., Crisler, K., Powell, M., Hittle, D., Kramer, A., et al. (1994). Measuring outcomes of home health care. Denver, CO: Center for Health Policy Research and Center for Health Services Research. Scheinhorn, D., Stearn Hassenpflug, M., Votto, J., Chao, D., Epstein, S., Doig, G., et al. (2007). Ventilator-dependent survivors of catastrophic illness transferred to 23 long-term care hospitals for weaning from prolonged mechanical ventilation. Chest, 131, 76-84. Schuit, K., Sleijfer, D., Meijler, W., Otter, R., Shakenraad, J., van den Bergh, C. (1998). Symptoms and functional status of patients with disseminated cancer visiting outpatient departments. Journal of Pain and Symptom Management, 16, 290-297. Seneff, M., Wagner, D., Thompson, D., Honeycutt, C. & Silver, M. (2000). The impact of long-term acute-care facilities on the outcome and cost of care for patients undergoing prolonged mechanical ventilation. Critical Care Medicine, 28, 342350. Sereika, S. & Clochesy, J. (1996). Left ventricular dysfunction and duration of mechanical ventilatory support in the chronically critically ill: A survival analysis. Heart and Lung, 25, 45-51. Simini, B. (1999). Patients’ perceptions of intensive care. Lancet, 354, 571-572. Simmons, E., Himmelfarb, J., Sezer, M., Chertow, G., Mehta, R., Paganini, E., et al (2004). Plasma cytokine levels predict mortality in patients with acute renal failure. Kidney International, 65, 1357-1365. 176 Spadoni, G., Stratford, P., Soloman, P., & Wishart, L. (2004). The evaluation of change in pain intensity: A comparison of the P4 and single-item numeric pain rating scales. Journal of Orthopedic and Sports Physical Therapy, 34, 187-193. Spicher, J. & White, D. (1987). Outcome and function following prolonged mechanical ventilation. Archives of Internal Medicine, 147, 421-425. Sprangers, M., Mornpour, C., Moynihan, T., et al., (2002). Assessing meaningful change in quality of life overtime: A user’s guide for clinicians. Mayo Clinic Proceedings, 77, 561-571. Stapleton, R., Engelberg, R., Wenrich, M., Goss, C., & Curtis, R. (2006). Clinician statements and family satisfaction with family conferences in the intensive care unit. Critical Care Medicine, 34, 1679-1685. Stewart, A., Greenfield, S., Hays, R., Wells, K., Rogers, W., Berry, S. et al. (1989). Functional status and wellbeing of patients with chronic conditions: results from the Medical Outcomes Study. JAMA, 262, 907-913. Sullivan, M., Levy, W., Russo, J. & Spertus, J. (2004). Depression and health status in patients with advanced heart failure: a prospective study in tertiary care. Journal of Cardiac Failure, 10, 390-396. SUPPORT Study Investigators (1995). A controlled trial to improve care for seriously ill hospitalized patients. Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatment. Journal of the American Medical Association, 274, 1591-1598. Tanaguchi, T., Koido, Y., Aiboshi, J., Yamashita, T., Suzaki, S., Kurokawa, A. (1999a). 177 Change in the ratio of interleukin-6 to interleukin-10 predicts a poor outcome in patients with systemic inflammatory response syndrome. Critical Care Medicine, 27, 1262-1264. Tanaguchi, T., Koido, Y., Aiboshi, J., Yamashita, T., Suzaki, S., Kurokawa, A. (1999b). The ratio of interleukin-6 to interleukin-10 correlates with severity in patients with chest and abdominal trauma. American Journal of Emergency Medicine, 17, 1-7. Thomas-Hawkins, C. (2000). Symptom distress and day-to-day changes in functional status in chronic hemodialyis patients. Nephrology Nursing Journal, 27, 369-379. Van den Berghe, G. (2002). Neuroendocrine pathobiology of chronic critical illness. Critical Care Clinics, 18, 509-528. Walke, L., Gallo, W., Tinetti, M., & Fried, T. (2004). The burden of symptoms among community-dwelling older persons with advanced chronic disease. Archives of Internal Medicine, 164, 2321-2324. Walker, L. & Avant, K. (1995). Strategies for theory construction in nursing (3rd ed.). Norwalk, CT: Appleton and Lange. Weaver, T., Richmond, T., & Narsavage, G. (1997). An explanatory model of functional status in chronic obstructive pulmonary disease. Nursing Research, 46, 26-31. Wensing, M., Vingerhoets, E., & Grol, R. (2001). Functional status, health problems, age and comorbidity in primary care patients. Quality of Life Research, 10, 141-148. White, D. & Luce, J. (2004). Palliative care in the intensive care unit: barriers, advances, and unmet needs. Critical Care Clinics, 20, 329-343. 178 Widar, M., Ahlstrom, G., & Ek, A. (2004). Health-related quality of life in persons with long-term pain after a stroke. Journal of Clinical Nursing, 13, 497-505. Winkelman, C. (2004). Inactivity and inflammation: Selected cytokines as biologic mediators in muscle dysfunction during critical illness. AACN Clinical Issues, 15, 74-82. Winkelman, C. (2007). Inactivity and inflammation in the critically ill patient. Critical Care Clinics, 23, 21-34. Winkelman, C., Higgins, P., Chen, Y., & Levine, A. (2007). Cytokines in chronically critically ill patients after activity and rest. Biological Research for Nursing, 8, 261-271. Zambroski, C., Moser, D., Bhat, G., & Ziegler, C. (2005). Impact of symptom prevalence and symptom burden on quality of life in patients with heart failure. European Journal of Cardiovascular Nursing, 4, 198-206.