© The Author 2007. Published by Oxford University Press.
Conceptual Issues in Symptom Clusters Research and Their Implications for Quality-of-Life Assessment in Patients With Cancer
Affiliations of authors: Department of Physiological Nursing (CM, BEA, MD) and Department of Community Health Systems (BC), University of California, San Francisco, CA
Correspondence to: Christine Miaskowski, RN, PhD, FAAN, Department of Physiological Nursing, University of California, 2 Koret Way, Box 0610—N631Y, San Francisco, CA 94143-0610 (chris.miaskowski{at}nursing.ucsf.edu).
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ABSTRACT |
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 Top Abstract IntroductionDefinitions of and Conceptual...Two Conceptual Approaches to...Future Directions in Symptom...Implications of Symptom Clusters...Summary and ConclusionsNotesReferences |
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The majority of the research on the various aspects of symptom management has focused on individual symptoms. However, patients with cancer often experience multiple symptoms simultaneously as a result of their disease and treatment. In 2001, symptom management researchers began to study the impact of symptom clusters on patient outcomes. Over the past 6 years, a number of conceptual reviews as well as several research studies have been published on symptom clusters in oncology patients. This paper summarizes the conceptual basis for symptom cluster research, describes two conceptual approaches to symptom cluster research, and discusses the implications of symptom clusters for quality-of-life research. The paper concludes with an enumeration of the critical considerations that need to be addressed if this area of scientific inquiry is to move forward.
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INTRODUCTION |
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TopAbstractIntroductionDefinitions of and Conceptual...Two Conceptual Approaches to...Future Directions in Symptom...Implications of Symptom Clusters...Summary and ConclusionsNotesReferences |
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The majority of the research on symptom prevalence, assessment, and management in oncology patients has focused on a single symptom (e.g., pain, fatigue, nausea) (1,2). While a reductionistic approach is needed to study the epidemiology and consequences of each of these symptoms independently, in reality, patients with cancer experience multiple symptoms simultaneously. Recent work suggests that multiple symptoms are a major problem for patients and their family caregivers (3) and that these symptoms have deleterious effects on patient outcomes (e.g., functional status, quality of life [QOL]) (4–9).
In 2001, two papers published in the oncology literature presented compelling data regarding the deleterious effect of symptom clusters on patient outcomes (5–10). In addition, as part of the National Institutes of Health (NIH) State of the Science Conference on Symptom Management in Cancer: Pain, Depression, and Fatigue (11), the concept of a symptom cluster was explored in papers that focused on an overview of this concept (1), the occurrence of symptom clusters (12), the assessment of symptom clusters (13), and the treatment of symptom clusters (14). These two initial studies, as well as the papers from the NIH conference, stimulated a series of investigations on symptom clusters in oncology patients (9,15–22). The purposes of this paper are to summarize the conceptual basis for symptom cluster research, describe two conceptual approaches to symptom cluster research, and discuss the implications of symptom clusters for QOL research. While emphasis is placed on symptom cluster research in oncology patients, these ideas may apply to patients who experience symptom clusters associated with other chronic medical conditions.
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Definitions of and Conceptual Basis for Symptom Cluster Research |
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TopAbstractIntroductionDefinitions of and Conceptual...Two Conceptual Approaches to...Future Directions in Symptom...Implications of Symptom Clusters...Summary and ConclusionsNotesReferences |
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Oncology patients experience a variety of symptoms (e.g., anxiety, sleep disturbance, nausea) that occur as a result of their disease and/or treatment. Studies have evaluated the prevalence rates for many of these symptoms on an individual basis, as well as the deleterious effects that these "individual" symptoms have on patient outcomes. However, clinical experience suggests that oncology patients often report the co-occurrence of "multiple" symptoms. For example, as illustrated in Fig. 1, an oncology outpatient with metastatic bone pain may report that his pain causes sleep disruption, which results in daytime fatigue. This daytime fatigue leads to increased sleep disruption because of frequent late afternoon naps. Both the sleep disruption and the fatigue result in increased pain and depression. The co-occurrence of these multiple symptoms creates a vicious cycle of ongoing and unrelieved symptoms.
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This clinical scenario suggests the need for research on the prevalence and impact of multiple symptoms as well as on symptom clusters in oncology patients. Before a discussion of two conceptual approaches to symptom cluster research, the definitions of a symptom cluster and the conceptual basis for symptom cluster research are reviewed.
Definition of a Symptom Cluster
In 2001, Dodd et al. (5) defined a ymptom cluster as three or more concurrent symptoms that are related to each other but are not required to share the same etiology. Since that time, several conceptual reviews have been written on the topic of symptom clusters in oncology patients in general (22,23) as well as in patients with breast cancer (24), brain tumors (25), and advanced disease (26). In one of the most recent papers, Kim et al. (23) refined the definition of a symptom cluster by Dodd and colleagues as follows: "A symptom cluster consists of two or more symptoms that are related to each other and that occur together. Symptom clusters are composed of stable groups of symptoms, are relatively independent of other clusters, and may reveal specific underlying dimensions of symptoms. Relationships among symptoms within a cluster should be stronger than relationships among symptoms across different clusters. Symptoms in a cluster may or may not share a common etiology" (p. 278).
However, as noted by Barsevick et al. (22), neither has the term "symptom cluster" been defined systematically nor is there an accepted definition in oncology symptom management. Therefore, one of the first steps in symptom cluster research must be to determine the essential elements in the definition of a symptom cluster. For example, does a symptom cluster require a minimum of three symptoms? In addition, what constitutes the exact "relationship" among symptoms in a cluster needs to be determined (e.g., what does the strength of the correlation need to be for symptoms to be placed within a specific symptom cluster?).
Miaskowski et al. (1) suggested that symptoms can be "related" to each other through a common mechanism or etiology, through a shared common variance, or through the production of different outcomes than individual symptoms. Perhaps, all three of these elements of "relationship" are required before a group of symptoms can be classified as a symptom cluster. The explication of the nature and the strength of the relationships among the symptoms to qualify them as a cluster is one of the central questions in symptom cluster research. Finally, all of the elements of the definition need to be tested in a systematic fashion across patients with different cancer diagnoses, different stages of disease, and different cancer treatments.
Conceptual Basis for Symptom Cluster Research
While symptom cluster research is still in its infancy in oncology, this concept is not new in medicine. Since the 20th century, modern medicine has accepted that certain patterns of symptoms constitute a "syndrome" or characterize a disease state. Often these disease-specific symptoms result from a common underlying mechanism. For example, the symptoms of dyspnea on exertion, paroxysmal nocturnal dyspnea, and fatigue associated with congestive heart failure result from increased pulmonary venous pressures and decreased cardiac output. This triad of symptoms as well as specific findings on a physical examination (e.g., pedal edema) provide direction for additional laboratory and diagnostic tests. Once the phenotypic data are collected and evaluated, a specific diagnosis (e.g., congestive heart failure) is made and syndrome-specific or diagnosis-specific interventions are initiated.
As the science of symptom cluster research in oncology matures, one can envision that specific symptom clusters would become diagnostic entities. These "symptom cluster diagnoses" would have specific subjective and objective phenotypic criteria. In addition, specific treatment protocols would be developed, tested, and implemented in the management of the "symptom cluster diagnosis." For example, at the present time, the oncology patient with metastatic bone pain depicted in Fig. 1 would be described as experiencing four symptoms (i.e., pain, fatigue, sleep disturbance, and depression). Currently, it is not known whether these four symptoms in this specific patient population (i.e., patients with metastatic bone pain) are related to each other through a shared common variance, whether they share a common mechanism or etiology, and/or whether they produce synergistic effects on patient outcomes. One of the goals of symptom cluster research is to determine whether one or more of these relationships exist among these four symptoms. Once this symptom cluster is established, research studies need to be done in multiple samples of patients with metastatic bone pain to confirm the prevalence rates for this symptom cluster and to verify that the established relationships among the four symptoms are reproducible across multiple patient samples. Following the verification of this symptom cluster, subsequent research needs to determine whether it occurs in other patient samples and whether it constitutes a "symptom cluster diagnosis."
Additional research is needed to determine if symptom clusters in oncology patients share a common mechanism. These studies are challenging to conduct because many of the most prevalent symptoms that oncology patients report (i.e., pain, fatigue, depression, sleep disturbances) can occur as a result of the patients' cancer diagnosis, their treatment regimen, or interactions between the cancer, the treatment, and the patients' underlying comorbidities. While complex and challenging, these types of mechanistic studies are needed to characterize patients with specific symptom clusters that warrant specific treatment regimens. In the future, specific symptom clusters may be identified that constitute a yet to be named "symptom cluster diagnosis" with specific etiologic and mechanistic criteria.
Some evidence exists to support the idea that symptoms within a cluster may share common biologic mechanisms. In a recent review, Cleeland et al. (27) suggested that the symptoms of cancer and cancer treatment may be attributed in part to cytokine-induced sickness behavior (28–30). This sickness behavior can be induced in animals with the administration of pathophysiologic components of bacteria (e.g., lipopolysaccharide). The physiologic responses that characterize sickness behavior in animals injected with lipopolysaccharide include fever, pain, wasting, and increased activity in both the hypothalamic–pituitary–adrenal axis and autonomic nervous system (31). In addition, these animals exhibit a variety of behaviors including a general decrease in activity, somnolence, cognitive impairment, decreased social interaction and exploration, decreased sexual activity, and decreased food intake. This sickness behavior in animals appears to require communications between the brain and the immune system and is mediated through the release of proinflammatory cytokines (e.g., interleukin 6, tumor necrosis factor-
) (32–37).
While these physiologic and behavioral responses observed in animals are remarkably similar to some of the symptoms identified within the specific symptom clusters that were found in studies of patients with cancer (9,16,21), they do not account for all of the symptom clusters found to date. It is reasonable to suggest that various symptom clusters may occur through different mechanisms or through multiple mechanisms that result from interactions among the hosts, the cancer, and their cancer treatment.
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Two Conceptual Approaches to Symptom Cluster Research |
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TopAbstractIntroductionDefinitions of and Conceptual...Two Conceptual Approaches to...Future Directions in Symptom...Implications of Symptom Clusters...Summary and ConclusionsNotesReferences |
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The science of symptom cluster research will advance through the use of two distinct conceptual approaches to identify and evaluate the impact of multiple symptoms on patient outcomes. As illustrated in Fig. 2, A, the first approach involves the identification of symptom clusters in patients with cancer. The second approach involves the identification of different subgroups of oncology patients based on their experiences with a specific symptom cluster (Fig. 2, B). A subsequent step in this second approach is to evaluate whether these subgroups of patients, with different experiences with a specific symptom cluster, differ on important clinical outcomes.
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Identification of Symptom Clusters
As shown in Fig. 2, A, the identification of symptom clusters begins with the administration of a symptom inventory or symptom assessment tool. For example, the Memorial Symptom Assessment Scale (38,39) or the M. D. Anderson Symptom Inventory (16) could be administered to a heterogeneous or homogeneous sample of oncology patients. To determine the number and types of symptom clusters, the patients' ratings of the presence or absence of the various symptoms or the severity of the various symptoms are analyzed using the statistical procedures of either factor analysis or cluster analysis. Based on either of these statistical analyses, one or more symptom clusters are derived empirically or "de novo." The various symptom clusters are named based on the specific symptoms contained within the symptom cluster.
For example, as shown in Fig. 2, A, in this hypothetical situation, 18 symptoms were assessed using a symptom inventory. Following the statistical analysis (i.e., either factor analysis or cluster analysis) of the patients' responses to the symptom inventory, five symptom clusters were identified each of which contained three to five symptoms. A panel of experts in oncology and symptom management named the five symptom clusters based on the symptoms contained within the cluster.
As noted by Barsevick et al. (22), factor analysis procedures examine the relationships among a number of variables (e.g., symptom severity scores) based on the matrix of correlation coefficients between the variables. Factor analysis can be used to predict a set of latent factors that are responsible for covariance among a group of symptoms. Symptoms due to this latent factor would covary more strongly with each other than they would with symptoms that are affected by a different latent factor. Alternatively, the exploratory statistical procedure of cluster analysis can be used to identify groups of similar items (i.e., symptoms) (40–42). Cluster analysis is a method of analyzing relationships among variables and is used to classify similar cases into "like" groups or clusters. Usually cluster analysis is necessary when no a priori theoretical specification exists.
The majority of the symptom cluster research done to date in oncology patients has focused on the empiric or "de novo" identification of symptom clusters (9,16–21). The specific symptom clusters, the analytic techniques used to derive the symptom clusters, and the sample characteristics of the six studies that focused on the empiric or de novo identification of symptom clusters in oncology patients are listed in Table 1. Four of these studies (16–18,21) determined the number and types of symptom clusters through factor analysis of a symptom assessment scale. The other two studies (9,19) determined the number and types of symptom clusters through hierarchical cluster analysis of a symptom assessment scale.
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In these six studies (9,16–19,21), the number of symptom clusters identified ranged from one to seven. Of note, both statistical procedures resulted in the identification of a single symptom cluster and multiple symptom clusters. The three studies that identified a single symptom cluster (17–19) used homogeneous samples of patients with either breast or lung cancer. The studies that identified more than one symptom cluster used heterogeneous samples of patients who underwent a variety of cancer treatments (16,21) or palliative care (9).
An examination of the symptom clusters that were identified empirically or de novo in these six studies demonstrates a lack of consistency in the specific symptoms within the various clusters. These inconsistencies may be due in part to differences in sample characteristics, the questionnaires that were used to collect the symptom data, the timing of data collection, and the various analytic methods that were used to analyze the data. Future studies, focused on the empiric or "de novo" identification of symptom clusters in oncology patients, need to compare the absolute number of symptom clusters and the specific symptoms within each cluster that are derived using "both" factor analysis and cluster analysis techniques. In addition, symptom cluster identification studies need to be done with homogeneous and heterogeneous samples of patients in terms of cancer diagnoses, stage of disease, and treatments to determine which symptom clusters are disease specific and which ones are treatment specific.
Identification of Subgroups of Patients Based on Their Experiences With a Specific Symptom Cluster
An equally valuable and complementary approach in symptom cluster research is illustrated with a hypothetical situation in Fig. 2, B. The first step in this approach is the identification of a specific symptom cluster (e.g., pain, fatigue, sleep disturbance, depression). The rationale for choosing the symptoms within a specific symptom cluster needs to be specified a priori. Reasons for inclusion may be that these symptoms share a significant percentage of common variance, share a common mechanism, and/or have synergistic effects on patient outcomes. Following the identification of a specific symptom cluster, oncology patients complete a symptom inventory (e.g., Memorial Symptom Assessment Scale) or a number of symptom-specific questionnaires that focus on the symptoms in the cluster (e.g., pain, fatigue, depression, sleep disturbance). The patients' responses to the questionnaire(s) are analyzed using the statistical procedures of cluster analysis, latent profile analysis (for quantitative variables), or latent class analysis (for categorical variables). These three statistical procedures can be used to identify "subgroups of patients" based on their experiences with a specific symptom cluster (40–46).
These statistical approaches allow for the identification of subgroups of patients who may experience a specific symptom cluster with greater or lesser severity. For example, as illustrated in Fig. 2, B, from a sample of 19 patients, three distinct subgroups of patients were identified based on their different experiences with a specific symptom cluster. One subgroup was categorized as having "mild" levels of all of the symptoms in the specific symptom cluster (i.e., "ALL LOW" patient subgroup). Another subgroup of patients was categorized as having "moderate" levels of all of the symptoms in the specific symptom cluster (i.e., "ALL MODERATE" patient subgroup). Finally, the third subgroup of patients was categorized as having "severe" levels of all of the symptoms in the specific symptom cluster (i.e., "ALL HIGH" patient subgroup). This conceptual approach allows for the identification of patient subgroups that are at greater risk for more severe symptom experiences.
It should be noted that cluster analysis has been used, in a variety of patient populations, to identify subgroups of patients who experience multiple symptoms with greater or lesser severity and have different levels of risk for poorer outcomes. For example, cluster analysis has been used to characterize distinct subtypes of patients with chronic regional pain syndrome (47). In addition, cluster analysis has been used to identify subgroups of patients with different experiences with migraine headache based on their ratings of pain intensity, depression, and functional status (48).
Only one study has used the procedure of cluster analysis to identify subgroups of oncology outpatients based on their experiences with the specific symptom cluster of fatigue, depression, sleep disturbance, and pain (49). A total of 191 outpatients who were receiving active treatment for their cancer were recruited from four outpatient oncology practices. Four relatively distinct subgroups of patients were identified using cluster analysis based on their experiences with these four highly prevalent and related symptoms (i.e., fatigue, depression, sleep disturbance, and pain). One subgroup (n = 67, 35%) reported low levels of all four symptoms. In contrast, 15% of the sample (n = 28) reported high levels of all four symptoms. Two additional subgroups were identified that were categorized as having primarily high levels of fatigue and low levels of pain (n = 68, 35%) or low levels of fatigue and high levels of pain (n = 28, 15%).
Inherent in this type of cluster research is the identification of patients with a symptom experience that places them at increased risk for poorer outcomes. In the study by Miaskowski et al. (49), differences in functional status and QOL were evaluated among the four patient subgroups. Patients who reported low levels of all four symptoms reported significantly higher Karnofsky Performance Status scores than the other three patient subgroups. In addition, patients who reported high levels of all four symptoms reported the lowest QOL scores compared with patients in the subgroup who reported low levels of all four symptoms. The difference in QOL scores between these two subgroups was not only statistically significant but also clinically significant (i.e., a difference of 1.7 standard deviation units) (50,51).
This study is the first published report of the identification of subgroups of outpatients with cancer based on their experience with a specific symptom cluster (i.e., pain, fatigue, sleep disturbance, and depression). Of note, while differences in QOL outcomes were found among the patient subgroups based on their symptom experiences, no clinical characteristics (i.e., cancer diagnosis, current treatment, presence of distant metastasis, hemoglobin, hematocrit) predicted subgroup membership. The authors suggested that a potential explanation for the lack of disease and treatment effects is that the different subgroups of patients may harbor different determinants (e.g., genetic) for experiencing symptoms that are independent of demographic, disease, or treatment characteristics (49). As work in symptom cluster research progresses, the potential exists through the use of both observational studies (e.g., population genetics, genetic epidemiology) and experimental studies in animals and humans (e.g., molecular genetics, gene expression profiling) to identify subgroups of patients who are at increased risk for more severe symptoms and to better understand the mechanisms that underlie the interactions between and among symptoms within a cluster.
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Future Directions in Symptom Cluster Research |
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TopAbstractIntroductionDefinitions of and Conceptual...Two Conceptual Approaches to...Future Directions in Symptom...Implications of Symptom Clusters...Summary and ConclusionsNotesReferences |
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In many ways, symptom cluster research is the "new frontier" in symptom management research (1). Many aspects of this research can be classified within the realm of "basic science" research. For example, the essential elements in the definition of a symptom cluster need to be determined and tested in multiple samples of oncology patients. In addition, the two conceptual approaches to symptom cluster research present numerous opportunities for investigation.
Table 2 summarizes some of the critical areas that need to be addressed to move the science of symptom cluster research forward and to generate findings that will prevent or decrease the severity of these multiple symptoms as well as their deleterious effects on patient outcomes. These critical considerations are grouped into three areas: conceptual considerations, considerations associated with the empiric or de novo identification of symptom clusters, and considerations associated with the identification of patient subgroups based on their experiences with a specific symptom cluster.
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Within the area of conceptual considerations in symptom cluster research, the definition of a symptom cluster requires refinement. In addition, the use of consistent terminology will help to distinguish between studies that focus on the empiric or de novo identification of symptom clusters compared with the identification of patient subgroups (not "clusters" of patients) based on their experiences with a specific symptom cluster.
Numerous methodological considerations exist within the area of empiric or de novo identification of symptom clusters. A comparison needs to be done of the number and types of symptom clusters that are derived using different statistical procedures. In addition, the number and types of symptom clusters need to be compared based on whether the symptom clusters are determined using ratings of symptom prevalence (i.e., symptom is present or absent), symptom severity, and/or symptom distress. Another methodological issue that needs to be addressed is whether the number and types of symptom clusters differ based on the number of symptoms in the inventory. An additional area that warrants investigation is the determination of symptom clusters within and across cancer diagnoses, cancer treatments, and stages of disease.
Finally, within the area of the identification of patient subgroups based on their experiences with a specific symptom cluster, several areas require consideration and investigation. First, criteria to be used to select symptoms for inclusion in a specific symptom cluster need to be established. For example, what degree of correlation needs to exist among three or more symptoms for them to be considered a specific symptom cluster? In addition, the best approaches to measure a specific symptom cluster phenotype need to be determined to insure the most valid and reliable identification of distinct patient subgroups. For example, a study could be done that compared the subgroups of patients that are identified based on their experiences with a specific symptom cluster (e.g., pain, fatigue, depression) when a single-item symptom inventory (e.g., Memorial Symptom Assessment Scale) is used versus three symptom-specific questionnaires (e.g., Brief Pain Inventory, Lee Fatigue, Center for Epidemiologic Studies—Depression Scale). Additional research is needed to determine the optimal statistical approaches to identify subgroups of patients based on their experiences with a specific symptom cluster. Finally, research studies are needed to determine the most sensitive patient outcomes that can be used to distinguish among patient subgroups based on their experiences with a specific symptom cluster.
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Implications of Symptom Clusters for Quality-of-Life Research |
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TopAbstractIntroductionDefinitions of and Conceptual...Two Conceptual Approaches to...Future Directions in Symptom...Implications of Symptom Clusters...Summary and ConclusionsNotesReferences |
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Both of the conceptual approaches to symptom cluster research outlined above have implications for QOL research. Of the six studies that created symptom clusters empirically or de novo, only Chen and Tseng (21) "verified" the conceptual meaning of the symptom clusters. They hypothesized that advanced disease, poorer functional status, and pain would associate with higher scores on the sickness symptom cluster; that receiving chemotherapy would associate with higher scores on the gastrointestinal symptom cluster; and that depression and anxiety scores, measured by the Hospital Anxiety and Depression Scale, would associate with higher scores on the emotional symptom cluster. All of these hypotheses were supported and suggest that higher symptom cluster scores can have a negative impact on patients' functional status and mood states. Unfortunately, a global assessment of QOL was not administered in this study and could not be used in their verification process. However, future studies can use this verification method to evaluate the impact of various symptom clusters on patient outcomes.
The study of Miaskowski et al. (49) is the first example of a symptom cluster study that was designed explicitly to identify subgroups of patients with different experiences with a specific symptom cluster and to determine once the patient subgroups were identified whether or not these subgroups differed on two important outcomes (i.e., functional status and QOL). The Multidimensional Quality of Life Scale-Cancer (MQOLS-CA) (52,53), which was used to measure QOL in this study, has four subscale scores (i.e., physical well-being, psychologic well-being, social concerns, and spiritual well-being) as well as a total QOL score. Analyses of variance demonstrated significant differences among the four patient subgroups in all of the MQOLS-CA subscale scores except spiritual well-being. These findings suggest that subgroups of oncology patients with different symptom experiences report clinically meaningful differences in the various domains of QOL. Both the findings of distinct patient subgroups and differences in outcome measures require replication before definitive conclusions can be drawn about the impact that different symptom experiences that are produced by a specific symptom cluster have on patients' QOL.
Table 3 summarizes some additional questions that need to be addressed by researchers who are interested in the relationships between single symptoms, multiple symptoms, or symptom clusters and QOL outcomes. First, studies need to be done to determine the most appropriate QOL outcome measure for a particular symptom study. For example, in a study of cancer-related fatigue in patients with breast cancer, should QOL be measured using a generic QOL measure or a disease-specific QOL measure? Additional areas for consideration in symptom management research include the optimal timing of the QOL measures and who should evaluate changes in QOL. Equally important questions that need to be considered are when should a change in QOL be expected during a symptom management intervention study and what constitutes a clinically significant versus a statistically significant improvement in QOL in symptom research studies.
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Summary and Conclusions |
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TopAbstractIntroductionDefinitions of and Conceptual...Two Conceptual Approaches to...Future Directions in Symptom...Implications of Symptom Clusters...Summary and ConclusionsNotesReferences |
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The findings from the initial, though limited, number of studies in symptom cluster research suggest that this area of scientific inquiry has important clinical implications. As this science matures, it may warrant a Consensus Development Conference at the NIH to refine the concepts associated with symptom cluster research and to provide direction for future studies.
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NOTES |
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Drs C. Miaskowski, M. Dodd, and B. Cooper are funded by grants from the National Cancer Institute, the National Institute of Nursing Research, and the Oncology Nursing Foundation. Dr B. E. Aouizerat is funded through the National Institutes of Health Roadmap for Medical Research Grant (8K12RR023262).
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