Download symptom burden and its relationship

 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
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