European Journal of Oncology Nursing

European Journal of Oncology Nursing 14 (2010) 417e434 Contents lists available at ScienceDirect European Journal of Oncology Nursing journal homepage: www.elsevier.com/locate/ejon The state of science in the study of cancer symptom clusters Canhua Xiao School of Nursing, University of Pennsylvania, Claire M. Fagin Hall, 418 Curie Boulevard, Philadelphia, PA 19104-4217, USA abstract Keywords: Symptom experience Symptom clusters Oncology Purpose: To provide an integrative review of the literature on the science of symptom clusters in patients with cancer and establish implications for future studies. Methods: Sixty-one articles about cancer symptom clusters were selected for review from results of a search in MEDLINE, CINAHL, PsycINFO, Sociological Abstracts and Cochrane databases from 1950 to 2010. Results: This review discusses the current research on the definitions, theoretical frameworks, measurements, outcomes, and interventions of symptom clusters in oncology. Although symptom clusters were identified as groups of several related and coexisted symptoms, researchers had different opinion on the least number of and relationships among symptoms in a cluster. Four theoretical frameworks were used, but none of them were specific to guide research in symptom clusters for general cancer population. Most-common symptom approach and all-possible symptom approach had their own characteristics and methods for cluster identification. Functional status and quality of life were major outcomes that were negatively associated with the number or severity of symptom clusters. Interventions with multiple or central symptoms in clusters were two potential ways to improve patients symptom experience. Conclusions: Despite advances in understanding of symptom clusters, further research is needed to define clusters operationally, and to develop appropriate theoretical frameworks. Methods of cluster identification need further comparison to see which offers the best understanding of symptom clusters. More studies with cross-sectional or longitudinal designs are necessary to explore influences of symptom clusters on patient outcomes, and interventions on symptom clusters. Published by Elsevier Ltd. Introduction Symptoms are among the most-common reasons that patients seek healthcare (Rutledge and McGuire, 2004). Clinical experiences and studies have shown that cancer patients often experience multiple concurrent symptoms during disease trajectories (Dodd et al., 2001a; Given et al., 2001a; Patrick et al., 2004). Symptom clusters occur, when these multiple concurrent symptoms are related to each other. Compared with single symptoms, symptom clusters have more complicated and synergetic detrimental influences on patient outcomes (Dodd et al., 2001a; Gift et al., 2004; Given et al., 2001a). The number of studies investigating symptom clusters has greatly increased since the first paper regarding the effect of symptom clusters on oncology patients functional status, published in 2001 (Dodd et al., 2001a). The purpose of this paper is to provide an integrative review of the literature on cancer symptom clusters. The paper will discuss the E-mail address: canhuax@nursing.upenn.edu definition, theoretical framework, measurement, outcome, and intervention for symptom clusters. Suggestions for further research in this field conclude the review. Methods The literature search was conducted in MEDLINE, CINAHL, PsycINFO, Sociological Abstracts and Cochrane Database, using key words: symptom clusters, multiple symptoms, concurrent symptoms, or constellation of symptoms, which were combined with cancer, oncology, neoplasm or tumor. The search was limited to articles published in English, within the timeframe from 1950 to January 2010. Reference lists and bibliographies from other published articles were used to find additional articles to review. A total of 426 abstracts were identified for initial review. Selection was based on the following inclusion and exclusion criteria. Any data-based reports addressing measurements, outcomes, or interventions of multiple symptoms and relationships between symptoms in oncology patients were included in the review. Theoretical articles about the concept of 1462-3889/$ e see front matter Published by Elsevier Ltd. doi:10.1016/j.ejon.2010.05.011

418 C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 symptom clusters were also included as it is an important issue in symptom cluster research. Articles that did not examine the relationships between and among multiple symptoms in oncology patients were excluded from the review, along with duplicate articles. A total of 61 articles were identified, of which 57 were databased and 4 were theoretical. The publication dates of these articles ranged from 1999 to 2010. Among the 57 data-based articles, seventeen were secondary data analyses, and the others were not secondary data analyses; twenty-two were longitudinal research designs, and the others were cross-sectional research designs. Study populations varied. Twenty-six studies included patients with at least two types of cancer; fourteen studies included only patients; eight studies included only lung cancer patients; two studies included only patients with brain tumors; two studies only enrolled patients with prostate cancer; another five studies contained patients with ovarian, colorectal, pancreatic cancer, or malignant hematological disorders, or adult survivors of childhood cancer respectively. Review Definition of symptom clusters Conceptual clarification is the foundation for building knowledge about symptom clusters in cancer (Dodd et al., 2004). Although most researchers agree that symptoms in a cluster are correlated with each other and coexistent (Barsevick, 2007; Dodd et al., 2001a; Kim et al., 2005), there is still disagreement about some essential elements in the definition of symptom clusters. For instance, different researchers have different understandings of the relationship between symptoms in a cluster. Some have identified the relationship by the correlation between and among symptoms (Gaston-Johansson et al., 1999; Gift et al., 2003). Others have measured the relationship based on the effect of symptoms on outcomes (Fox et al., 2007). Miaskowski et al. (2004) have also suggested that symptoms can be related to each other through a common mechanism or etiology. In addition, researchers disagree about whether a symptom can be shared by several different clusters. Most studies put a symptom exclusively in one cluster, but two studies were found to allow a symptom shared by several clusters (Aprile et al., 2008; Francoeur, 2005). Clarifying the meaning of relationships between and among symptoms in a cluster will be necessary to define the concept of symptom clusters. Another discrepancy in the definition of symptom clusters is the minimum number of symptoms constituting a cluster. Dodd et al. (2001a) suggested that at least three symptoms constitute a cluster, but Kim et al. (2005) recommended a minimum requirement of only two symptoms. Many data-based studies have since shown that two symptoms clustered have negative influences on patient s quality of life or functional status (Chen and Lin, 2007; Chow et al., 2007; Fox and Lyon, 2006, 2007; Given et al., 2001b; Walke et al., 2007), while others have supported at least three symptoms in a cluster (Bender et al., 2005; Chan et al., 2005). Additionally, it is also not well understood whether all symptoms in a cluster should be presented at the same time (Kim et al., 2008; Molassiotis et al., 2010). These discrepancies reflect different understandings of the concept of symptom clusters. Variations in study designs, cluster identification methods, and characteristics of study samples could also contribute to these discrepancies. Determining the clinical and theoretical significance of symptom clusters might clarify these issues in the definition of symptom clusters. Theoretical frameworks The theory of unpleasant symptoms The theory of unpleasant symptoms (TOUSs) has been utilized frequently in symptom cluster research (Chan et al., 2005; Fox and Lyon, 2006, 2007; Fox et al., 2007; Gift et al., 2004, 2003; Hoffman et al., 2007). TOUS has three main reciprocal components: symptoms, influential factors, and performance (Lenz et al., 1997). According to the theory, each symptom has four dimensions: intensity, timing, level of distress perceived, and quality. Factors influencing symptoms include physiological, psychological, and situational antecedents. Performance is the consequence of the symptom experience, which includes functional and cognitive activities. This model provides a theoretical framework for research on symptom clusters by indicating multiplicative effects of multiple concurrent symptoms. A potential criticism of TOUS model is its focus on physical rather than psychological symptoms. This limitation is reflected in the studies using this model as their theoretical framework. Three studies strictly following this model only included physical symptoms in cluster analyses (Gift et al., 2004, 2003; Hoffman et al., 2007). By discounting the role of psychological symptoms, TOUS offers a less comprehensive understanding of the nature of symptom clusters than theories that take psychological symptoms into account. The symptom management model Symptom management model (SMM) (recently renamed as symptom management theory) is based on the premise that effective management of any given symptom or group of symptoms should consider all three components (Dodd et al., 2001b; Humphreys et al., 2008). These three components are: symptom experience, symptom management strategies, and outcomes. Symptom experience comprises perception, evaluation, and response to symptoms. Symptom management involves dealing with negative outcomes through biomedical, professional and selfcare strategies. Patient outcomes are the results of symptom experience and management, including functional status, quality of life, costs, and morbidity. Each component can be affected by the others. Although the concept of symptom clusters has been recently introduced into the model, the relationships among these multiple symptoms in a cluster are not addressed completely. This restriction is also manifested in the utilization rate of the model: only three studies found in the review adopted this model as the theoretical framework to guide research (Dodd et al., 2010, 2001a; So et al., 2009). The Symptom Cluster in Children and Adolescents with Cancer The Symptom Cluster in Children and Adolescents with Cancer is the only theoretical framework to specifically address symptom clusters. This model has three components: antecedent, symptom cluster, and outcome (Hockenberry and Hooke, 2007). Personal, environmental, and disease factors are the antecedents that can influence children s symptom experience. Three most common and related symptoms of pain, sleep, and fatigue are the essential components of this cluster. The consequences of this cluster are physical performance and behavioral changes. This framework might be successful in guiding research into symptom clusters of pain, sleep, and fatigue in children and adolescents with cancer. However, restricting the theoretical framework to one symptom cluster with three specific symptoms limits its usefulness for broader symptom cluster research, as there are many other symptom clusters in children with cancer (Yeh et al., 2008). In addition, concentration on children and adolescents impedes utilization of this framework in adult and senior populations.

C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 419 Cytokine-induced sickness behavior Cytokine-induced sickness behavior has been proposed by some authors as a possible explanation for the biological mechanism of symptom clusters (Chen and Tseng, 2006; Cleeland et al., 2003; Francoeur, 2005). Sickness behaviors refer to physiological and behavioral responses that can be induced in animal models after the administration of infectious or inflammatory agents (Hart, 1987, 1991; Watkins and Maier, 2000; Yirmiya, 1996). Physical changes include fever, pain, and increased activity in the hypothalamicepituitaryeadrenal axis and the autonomic nervous system (Watkins and Maier, 2000). Observed behavior changes consist of decreased activity, appetite loss, somnolence, and cognitive impairment (Yirmiya, 1996). Although sickness behavior represents a potential mechanism to explain some symptom clusters in cancer patients, there are still some limitations in the model as a guide for research. For instance, this model cannot explain many other symptoms that are not included in sickness behaviors (Aprile et al., 2008; Bender et al., 2005; Dodd et al., 2001b). In addition, it is difficult to use this model to guide the research in cases where sickness behaviors are separated into several different clusters (Chow et al., 2007; Molassiotis et al., 2010; Walke et al., 2007; Yeh et al., 2008), because it might be assumed that symptoms caused by the same underlying biomedical mechanism would be grouped in the same cluster. All of these four theoretical frameworks identify the importance of symptoms in disease trajectories. Each framework has its own strengths and limitations in regard to symptom clusters. As general symptom theories, TOUS and SMM focus on multiple symptoms, but the relationships among these symptoms are not clearly addressed. While Symptom Cluster in Children and Adolescents with Cancer is specifically a symptom cluster theory, its specificity limits its use in a broad range of research. Cytokine-Induced Sickness Behavior provides a possible way to understand the underlying biological mechanism for certain symptom clusters. With the limitation of each theoretical framework, comprehensive theoretical models specifically focusing on cancer symptom clusters are still needed to guide further clinical research. Measurement of symptom clusters Most-common symptom approach There are four main characteristics in the most-common symptom approach (see Table 1). First, researchers often select several most-common symptoms in cluster identification, such as pain, fatigue, insomnia and depression. Second, researchers assume that these most-common symptoms be grouped together as a cluster before empirical studies. Third, generally, symptoms selected in cluster identification constitute a single cluster as the results of analytic techniques used to identify clusters. Fourth, the number of symptoms in a cluster is small, with most having 2e3 symptoms. Although the most-common symptom approach presents a way to understand symptom clusters, the main limitation to this approach is whether it is sufficient to select only the mostcommon symptoms in cluster identification. Since symptoms selected in cluster identification directly determine cluster results, adding or deleting any symptom could change the cluster result. If there is no sound theoretical foundation for selecting only the most-common symptoms in cluster identification, results from this approach might be neither reasonable nor reliable. Table 2 gives an overview of the 20 studies that have used a most-common symptom approach. The cluster identification method used most in this approach is clustering by correlations between symptoms (Chan et al., 2005; Dodd et al., 2001a; Fox and Lyon, 2006, 2007; Fox et al., 2007; Gaston-Johansson et al., 1999; Hoffman et al., 2007; Miaskowski and Lee, 1999; So et al., 2009). The correlation is usually calculated by correlation coefficients. For example, Fox and Lyon (2007) explored the relationship between pain, fatigue, and depression in 76 patients with ovarian cancer, and found fatigue and depression were grouped as a cluster because these two symptoms were correlated significantly with each other. The majority of these studies further supported symptom clusters by showing the influence of these clusters on patient outcomes, such as QOL and functional status (Dodd et al., 2001a; Fox and Lyon, 2006, 2007; Fox et al., 2007; Gaston-Johansson et al., 1999; So et al., 2009). Although most studies identified a single symptom cluster, symptoms involved in these single clusters varied at different studies (Chan et al., 2005; Fox and Lyon, 2006; Gaston-Johansson et al., 1999; Hoffman et al., 2007; So et al., 2009). The main reason for this might be that researchers chose different most-common symptoms according to different types of cancer and treatment. In addition, small sample sizes (Chan et al., 2005; Fox and Lyon, 2006) and using individual items from QOL instruments as proxy measures for patients symptoms (Chan et al., 2005; Dodd et al., 2001a; Fox and Lyon, 2006, 2007) may also decrease the evidence of significance from this method. Yet another method of symptom cluster identification, clustering by concurrent multiple symptoms, was used in five studies (Given et al., 2001a,b; Liu et al., 2009; Reyes-Gibby et al., 2006; Wilmoth et al., 2009). By this way, the identification of clusters does not need any statistical analyses, but only needs the cooccurrence of selected related symptoms. Three of these studies further identified the synergistic effect of multiple concurrent symptoms on patient outcomes (Given et al., 2001a,b; Liu et al., 2009). Compared with patients who reported no pain, fatigue or insomnia, those reporting two or three symptoms had a higher risk of lower functional status (Given et al., 2001a). Patients with more symptoms also experienced more additional symptoms or Table 1 Comparison of most-common symptom approach and all-possible symptom approach. Most-common symptom approach All-possible symptom approach Symptoms selected in cluster identification Most-common symptoms All-possible symptoms Assumption Selected symptoms are assumed to be in a cluster before empirical studies No assumption about the potential clusters before empirical studies Number of clusters Usually one cluster identified Usually more than one cluster identified Number of symptoms under a cluster Usually 2 or 3 symptoms Usually more than 4 symptoms Methods of symptom cluster identification By correlations between symptoms By underlying factors or components By concurrent symptoms By temporal patterns of symptoms over time By mediation effect among symptoms By central symptoms By interaction effect among symptoms By causal connection among symptoms By subgroups of patients with similar symptom profiles By subgroups of patient with similar symptom profiles Limitations Need sound theoretical foundations for selecting only the most-common symptoms in cluster identification Need explanation with clinical meaning for symptom clusters identified by statistical methods

420 Table 2 Studies identifying symptom clusters in cancer patients by most-common symptom approach. Author, year Primary aim Sample Design Indictor Analytic technique Main result Barsevick et al., 2006 To test mediation hypothesis about direct and indirect relationships between fatigue and depressive symptoms through functional status 295 patients with cancer Depressive symptoms Functional status multiple regression and depressive symptoms. Previously significant relationship between fatigue and depressive symptoms was reduced after functional status was controlled. Beck et al., 2005 To test whether sleep disturbance mediates the effect of pain on fatigue 84 patients with cancer having pain Sleep disturbance Correlation Multistage linear regression influences fatigue directly as well as indirectly by its effect on sleep Chan et al., 2005 Dodd et al., 2010 Dodd et al., 2001a Fox and Lyon, 2007 To test the existence of a symptom cluster involving breathlessness, fatigue and anxiety To identify subgroups of outpatients based on a specific symptom cluster and the differences of these subgroups on outcomes To determine the effect of the symptom cluster on functional status during chemotherapy To examine symptom clusters and its relationship to QOL 27 patients with lung cancer undergoing palliative radiation 112 women with breast caner 92 patients with cancer 76 patients with ovarian cancer Breathlessness Anxiety Sleep disturbances Functional status Quality of life (QOL) Sleep insufficiency Functional status QOL Correlation Cluster analysis Correlation, multiple regression Correlation Regression Three symptoms were moderately correlated at T1 and T2 and had high internal consistency across T0eT2 At baseline and the end of treatment: all low, mild, moderate, and all high. At one year after the start of treatment: mild, moderate, and all high. Subgroups with high severity levels of all four symptoms had poorer functional status and QOL at each time point than other subgroups. Group membership changed over time., sleep insufficiency and fatigue and fatigue explained 10.7% and 7.3% of the change in functional status. and fatigue and fatigue explained 41% of the variance in QOL. C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 Fox et al., 2007 To explore symptom clusters based on the relationship between symptoms, QOL, and functional status 73 patients with high-grade glioma Sleep disturbance Cognitive impairment QOL Functional status Correlation Regression QOL cluster (without pain) Functional status cluster (all of the symptoms) QOL cluster explained 29% of the variance in QOL. Functional status cluster explained 62% of the variance in functional status. Fox and Lyon, 2006 To explore the relationship between symptom clusters and QOL 51 patients with lung cancer QOL Correlation Regression and depression The cluster explained 29% of the variance in QOL.

Francoeur, 2005 To identify the relationship of cancer symptom clusters to depressive affect 268 cancer patients with recurrent disease initiating palliative radiation for bone pain Change in bowel habits Fever Nausea/vomiting Poor appetite Shortness of breath Sleep problems Weight loss Depressive affect Curvilinear and moderated regression analyses and weight loss and fatigue Pan and fever Sleep and fever and weight loss Nausea and fever Breath and appetite and sleep Breath and fatigue and sleep HTN a and fatigue and breath and sleep Gaston-Johansson et al., 1999 To determine the influence of fatigue, pain, and depression on health status in breast cancer patients 127 women with stages II, III and IV Health status Correlation regression, depression, and fatigue correlated with each other. and depression had an impact on health status (64%), and depression and fatigue had influence on perceived health status (42%). Given et al., 2001a Given et al., 2001b Hoffman et al., 2007 Liu et al., 2009 To test how symptom clusters and other factors can explain changes in physical function prior to following diagnosis To identify the predictor of pain and fatigue in the year following diagnosis among elderly cancer patients To examine the relationships among pain, fatigue, insomnia, and gender in lung cancer patients To explore the associations between pretreatment cluster categories and longitudinal profiles of these same symptoms during chemotherapy 826 patients with cancer 841 patients with cancer 80 patients with lung cancer 76 patients with Insomnia Physical function Insomnia Gender Sleep disturbances Concurrent symptoms Polytomous logistic regression Concurrent symptoms Regression Multinomial log-linear modeling Concurrent symptoms All of three symptoms Any two of them Any one of them None of them Compared with no pain, fatigue, or insomnia, patients with two or three symptoms had higher risk of lower physical functioning. and fatigue Compared with baseline, patients were less likely to report both pain and fatigue at the 12 month observation. and fatigue were the independent predictors of the numbers of other symptoms patients experienced., insomnia and fatigue No symptoms 1e2 symptoms All three symptoms Those with more symptoms pre-treatment continued to experience worse symptoms during treatment compared with those who began with fewer symptoms. (continued on next page) C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 421

Table 2 (continued) Author, year Primary aim Sample Design Indictor Analytic technique Main result Miaskowski et al., 2006 To identify subgroup of patients with cancer and the relationship with functional status and QOL 191 patients with cancer Cluster analysis Sleep disturbance Functional status QOL High fatigue and low pain Low fatigue and high pain All low All high The subgroup of patients who reported low levels of all four symptoms reported the best functional status and QOL. 422 Miaskowski and Lee, 1999 Pud et al., 2008 Reyes-Gibby et al., 2006 So et al., 2009 To describe pain, fatigue, and sleep disturbances in cancer patient and their selfcare strategies To identify subgroup of patients with cancer and the relationship with functional status and QOL To examine the pain, depression, and fatigue in adults with and without a history of cancer To examine the symptom cluster of fatigue, pain, anxiety, and depression and its effect on QOL 24 patients with cancer receiving radiation therapy for bone metastases 228 oncology outpatients 2161 adults with cancer and 17 210 adults without cancer 215 patients with Sleep disturbances Sleep disturbance Functional status QOL,, Anxiety QOL Correlation Cluster analysis Concurrent symptoms logistic regression Correlation Structural equation modeling and sleep disturbances were correlated with each other. Morning fatigue was related to pain for the evening and morning Higher scores for depressive symptoms were positively correlated with fatigue in the evening and morning. High fatigue and low pain Moderate fatigue and high pain All low All high The subgroup of patients who reported high levels of all four symptoms reported the worst functional status and poorest QOL. and fatigue and depression and depression All of three Symptom clusters were more prevalent among those with a history of cancer., pain, anxiety, and depression Participants experiencing higher levels of symptoms were more likely to have a poorer QOL. C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 Wilmoth et al., 2009 To provide initial validation of a symptom cluster 15 patients with Pilot Weight gain Psychological distress Altered sexuality Concurrent symptoms, weight gain, psychological distress and altered sexuality Clustering of all the symptoms was observed in 7 of the subjects. Clustering of three of the symptoms occurred in 7 subjects. a Hypertension.

C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 423 more severe symptoms than those who reported only one or neither symptom (Given et al., 2001b; Liu et al., 2009). However, as this method identifies a cluster only by the concurrent characteristic of selected symptoms, it is difficult to exclude the possibility that some unselected concurrent symptoms may be related to these selected symptoms, and thus should also be included in the cluster. Some studies further explored the nature of symptom clusters or how symptoms were related to each other in a cluster by mediation effects and interaction effects. Two studies investigated the mediation effect between symptoms. The mediation effect indicates that the effect of one symptom on another can be adjusted by a mediator (Baron and Kenny, 1986). In a study of 84 cancer patients with pain, Beck et al. (2005) showed that sleep disturbance was a mediator between pain and fatigue, and that a 35% effect of pain on fatigue was accounted by sleep disturbance. Another study of two symptoms of fatigue and depression used functional status as a mediator, and found fatigue had a direct and an indirect influence on depression by the mediator of functional status (Barsevick et al., 2006). However, both of the studies were crosssectional, and one used secondary data. With these study designs, it is difficult to assume the causal relationships of the mediation model, especially when there is not a strong theoretical framework to guide analyses (Polit and Beck, 2004). In addition, neither of these studies mentioned the control of other variables, such as age, co-morbidities, or other symptoms. If these variables had an influence on the relationship between symptoms, the mediation effect would change. Three other studies explored interaction effects within symptom clusters. The interaction effect postulates that the differing effect of one independent variable on the dependent variable depends on the particular level of another independent variable (Cozby, 1997). In a study of 268 cancer patients in the initial phase of palliative radiation, Francoeur (2005) identified interaction effects in a cluster of pain, fatigue, and depressive affect. When pain and fatigue were lower, the depressive affect was lower. When pain was high, even lower fatigue could yield a higher depressive affect. Two other studies examined a same symptom cluster of pain, fatigue and insomnia. However, one study found a three-way interaction effect (Hoffman et al., 2007), while the other did not find significant interactions (Dodd et al., 2001a). The main reason for the inconsistency could come from the different dependent variables in both regression models, with one being gender, and another functional status. In addition, different study populations: one study focused on lung cancer patients, the other on general cancer patients, might also explain the inconsistency. Another method is to identify subgroups of patients with similar symptom experience based on a specific symptom cluster. Unlike the previous presented methods, this method clusters patients together instead of symptoms. Miaskowski et al. (2006) studied 191 patients with cancer, and identified four subgroups of patients using cluster analysis: high fatigue and low pain, low fatigue and high pain, all symptoms low, and all symptoms high. This study further found that the subgroup of patients who reported low levels of all symptoms reported the best functional status and QOL. The findings from this study were further confirmed by two recent investigations either cross-sectionally (Pud et al., 2008) or longitudinally (Dodd et al., 2010). Although several distinct subgroups of patients with similar symptom experience were identified in these studies, the differences in most demographic and clinical characteristics among these subgroups have not yet been demonstrated (Dodd et al., 2010; Miaskowski et al., 2006; Pud et al., 2008). However, the findings might still benefit clinical practice by giving subgroups of patients different interventions based on their diverse symptom experiences. In the most-common symptom approach, researchers assume several most-common symptoms might be clustered together prior to empirical studies. Methods of cluster identification include clustering by correlations between symptoms, by concurrent symptoms, by mediation effect, by interaction effect, and by subgroups of patients with similar symptom cluster profiles. Among them, mediation effect and interaction effect can also be used to explore the nature of symptom clusters. Identifying subgroups of patients based on a cluster may provide an easy way to translate the results from this method to clinical practice because patients with similar symptom profiles are identified. Since the results of symptom clusters are based mainly on the symptoms selected into cluster identification, extra effort should be paid to the rationale of including and excluding symptoms in identification process. All-possible symptom approach Compared with the most-common symptom approach, more studies recently have used the all-possible symptom approach to explore cancer symptom clusters. The main characteristics of this approach are opposite to those of the most-common symptom approach (see Table 1). First, researchers often target all potential symptoms that cancer patients might experience to identify clusters rather than simply select the most-common symptoms. Second, researchers obtain results of symptom clusters after statistical analysis, instead of assuming clusters before empirical studies. Third, the number and type of symptom clusters are more than those in the most-common symptom approach. For instance, researchers found not only the fatigue cluster, which is the focus in the most-common symptom approach, but also emotional clusters, gastrointestinal clusters and others (Bender et al., 2008; Chen and Lin, 2007). Fourth, the number of symptoms in a cluster is greater than that in the common-symptoms approach, with more than 4 clusters in many studies. These distinct characteristics in the allpossible symptom approach make the methods of cluster identification also different from those in the most-common symptom approach. Table 3 provides a summary of a total of 37 studies that have used an all-possible symptom approach. In the all-possible symptom approach, the method used most to identify symptom clusters is clustering symptoms by underlying factors or components. Literature in this review indicates that, principle component analysis (PCA), and cluster analysis are among the most likely to cluster symptoms (see Table 3). Each of these methods can reduce the number of symptoms by putting several related symptoms under one group that is relatively independent of the other groups. For instance, Gleason et al. (2007) identified two symptom clusters: language and mood, from 12 symptoms in 66 patients with newly diagnosed primary or metastatic brain tumors. However, various cluster results have existed across studies because of different study populations, questionnaires, and statistical methods (see Table 3). In addition, it is possible that symptom clusters found in this way might not have a rational explanation because cluster results are based on factors or components from statistical procedures. In order to prevent this disadvantage, researchers have to adjust cluster results until they have clinical significance. This adjustment, although still conducted by statistical methods, is based on researchers understanding of symptom clusters, which might be a threat to the objectivity of the results and thus also cause discrepancies in cluster results. Clustering by a temporal pattern of symptoms provides a way to understand changes in symptom severity over time. Wang et al. (2006) conducted a longitudinal study of 64 patients who had locally advanced lung cancer and had undergone concurrent chemoradiation therapy (CXRT). Four symptom cluster patterns appeared during CXRT: steady increase: pain and sore throat; early

424 Table 3 Studies identifying symptom clusters in cancer patients by all-possible symptom approach. Author, year Primary aim Sample Design Questionnaire Analytic technique Main result Aprile et al., 2008 To identify association between toxicities 300 patients with NCI CTC a and strengths of these relations colorectal cancer Distance matrix by Bayesian analytical approach Six main hubs: fever, dehydration, fatigue, anorexia, pain, and weight loss Bender et al., 2008 To identify and compare symptom clusters in individuals with chronic health problems with cancer versus without cancer Bender et al., 2005 To describe symptom clusters across 3 phases of the disease Breen et al., 2009 To explore the presence of symptom clusters and investigate their relationships with anxiety and depression 154 subjects with cancer 892 subjects without cancer 154 women with 192 patients with breast or gastrointestinal cancers or lymphoma Comorbidity questionnaire POMS b Symptom checklist Daily symptom diary The Kupperman index Menopausal QOL scale Anemia/fatigue scale in FACT c Hospital Anxiety and Scale Chemotherapy Symptom Assessment Scale factor analyses cluster analysis factor analysis Skin rash, itching, night sweats, constipation, dizziness standing, abdominal pain, back pain, nausea, diarrhea, generalized pain, sleeping problems Leaking urine, frequent ruination, walking problems, balance problems Weight gain, overeating, shortness of breath, chest palpitations, joint pain Three symptom clusters were identified corresponding 3 different phases of the experience. Each cluster was composed of symptoms related to fatigue, perceived cognitive impairment and mood problems. Gastrointestinal: nausea, vomiting, pain General malaise: tiredness, feeling weak, headaches Emotional: feeling depressed, feeling anxious Nutritional: changes to appetite, weight loss or gain General physical: mouth/throat problems, shortness of breath Malaise, nutritional and gastrointestinal factors were independent predictors of depression. C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 Capp et al., 2009 To identify radiation-induced rectal toxicity symptom clusters 766 patients with prostate cancer Litwin self-assessment questionnaire Integrated visualization and clustering approach Seven well-defined clusters of rectal symptoms were present prior to treatment, 25 were seen immediately following radiation and 7 at years 1, 2 and 3 following radiation. Chen and Lin, 2007 To validate the three-factor symptom structure by using Confirmatory factor analysis in a larger sample of cancer patients 329 patients with cancer MDASI d -Taiwanese KPS e Confirmatory Sickness: pain, fatigue, disturbed sleep, lack of appetite and drowsiness Gastrointestinal: nausea and vomiting Emotional: distress and sadness Functional status was negatively associated with all three clusters.

Chen and Tseng, 2006 To understand cancer-related symptoms cluster 151 patients with cancer MDASI-Chinese HADS-D f KPS Sickness: pain, fatigue, disturbed sleep, lack of appetite and drowsiness Gastrointestinal: nausea and vomiting Emotional: distress and sadness Functional status was negatively associated with the sickness cluster. Cheung et al., 2009 To explore symptom clusters among outpatients with different advanced cancers 1366 patients with cancer ESAS g Principal component analysis Cluster 1: fatigue, drowsiness, nausea, decreased appetite, and dyspnea Cluster 2: anxiety and depression Symptom clusters varied in different cancer sites. Chow et al., 2007 Ferreira et al., 2008 Finnegan et al., 2009 Gift et al., 2004 To explore whether bone pain clusters with other symptoms in patients with bone metastases To identify the impact of multiple symptoms on health-related QOL dimensions and performance status To generate subgroups of survivors based on symptoms To examine factors predicting subgroup membership and change of QOL among different subgroups To identify symptom clusters experienced by patients 518 patients with bone metastases 115 outpatients with cancer 100 adult survivors of childhood cancers (ACC) 220 patients newly diagnosed with lung cancer ESAS EORTC QOL-C30 h The Beck Inventory The Brief Inventory KPS Memorial Symptom Assessment Scale Physical symptom experience Physical dimension of SF-36 i Principal component analysis TwoStep Cluster component with log-likelihood distance measure Latent variable mixture modeling, pain, drowsiness, and poor sense of well-being; Anxiety and depression; Shortness of breath, nausea, and poor appetite Symptom clusters changed during postradiation. Multiple and severe symptom subgroup Less symptoms and with lower severity subgroup Multiple and severe symptoms had worse PS, role functioning, and physical, emotional, cognitive, social, and overall HRQOL than less and lower severity subgroup. Three subgroups of patients: high symptoms (HS), moderate symptoms (MS), and low symptoms (LS) ACC-survivors who reported at least one chronic health condition were six times as likely to be classified in the HS subgroup as compared with the LS subgroup. Mean health-promoting lifestyle scores were lowest in the HS subgroup and highest in the LS subgroup. Differences in QOL among the subgroups were statistically significant., nausea, weakness, appetite loss, weight loss, altered taste, and vomiting The number and severity of symptoms in a cluster was significantly related to physical function. (continued on next page) C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 425

Table 3 (continued) Author, year Primary aim Sample Design Questionnaire Analytic technique Main result 426 Gift et al., 2003 To identify a cluster of symptoms over time in patients 112 patients newly diagnosed with lung cancer Physical symptom experience, nausea, weakness, appetite loss, weight loss, altered taste, and vomiting The cluster had internal consistency that remained at 3 and 6 months. Death 6 to 19 months after diagnosis was predicted by symptom severity at 6 months. Glaus et al., 2006 Gleason et al., 2007 Gwede et al., 2008 Hadi et al., 2008 To explore the occurrence and frequency of menopausal symptoms in women with To explore symptom clusters in patients with newly diagnosed brain tumors To identify distinct subgroups of patients and assessed whether the subgroups were associated with deleterious QOL outcomes To explore how patients worst pain clustered together with functional interference items. To determine whether symptom clusters change with palliative radiotherapy (RT) 373 women with 66 patients with newly diagnosed primary or metastatic brain tumors 133 women with 348 patients with bone metastases C-PET r IBCSG Linear Analogue Scales Items representing symptoms from FACT, FACT-brain subscale, CESD j MSAS k SF-36 Brief Inventory (BPI) cluster analysis cluster analysis Principal component analysis Hot flashes, weight gain, tiredness, decreased sexual interest and vaginal dryness. There were significant differences between the fatigued and the non-fatigued population regarding the intensity of menopausal symptoms, emotional irritability and general coping. Language cluster: difficulty reading, writing, and finding the right words Mood cluster: feeling of sadness, anxiety, and depressed mood The two symptom clusters were consistent over time. High-symptom prevalence group Low-symptom group The high-symptom burden group was more likely to report greater symptom prevalence and poorer QOL after chemotherapy. Cluster 1: walking ability, general activity, normal work, enjoyment of life and worst pain Cluster 2: relations with others, mood and sleep The two symptom clusters disintegrated at 4, 8, and 12 weeks post-rt. C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 Jarden et al., 2009 To explore the longitudinal effect of a interventions on treatment-related symptoms 42 patients with malignant hematological disorders Randomized, clinically controlled trial Stem Cell Transplantation Symptom Assessment Scale Principal component analysis Mucositis Cognitive Gastrointestinal Affective Functional In the intervention group, there was a significant reduction in symptom intensity over time for all clusters except the affective cluster. The intensity reduction in control group was not significant.

Kenefick, 2006 To describe patterns of symptom distress over time and to examine the relationship of selected patient and clinical characteristics to symptom distress 57 patients with The Symptom Distress Scale Correlations Each of the 13 symptoms was correlated with several other symptoms. The number of symptoms decreased during the period of the study. Kenne Sarenmalm et al., 2007 To explore predictors of HRQOL in postmenopausal women diagnosed with recurrent 56 women with recurrent breast cancer MSAS HADS l EORTC QOL-C30 IBCSG m QOL Core Questionnaire () Correlations Several symptoms yield strong significant correlations (worrying and feeling nervous, worrying and feeling sad, nausea and lack of appetite, and et al). Women who experience multiple symptoms also report higher levels of symptom distress. Kenne Sarenmalm et al., 2008 Kim et al., 2009a Kim et al., 2009b Kim et al., 2008 To explore the symptom experience and predictors of distress and quality of life over time To determine the number and types of symptom clusters at the middle, end, and 1 month after the completion of RT To evaluate for changes over time in these symptom clusters To identify and compare symptom clusters by frequency and by severity scores To compare the identified clusters by severity between patients with breast and prostate cancer To investigate treatment-related symptom clusters and the influence of demographic/ clinical variables on symptom clusters 56 women with recurrent breast cancer 160 patients underwent RT for breast or prostate cancer 78 patients with 82 patients with prostate cancer 282 patients with MSAS HADS EORTC QOL-C30 Memorial Symptom Assessment Scale (MSAS) Memorial Symptom Assessment Scale (MSAS) General Scale and confusion from the Profile of Mood States-Short Form Pittsburgh Sleep Quality Index Side Effect Checklist Correlations Highly significant association was identified between fatigue and depression, fatigue and pain, and pain and depression., pain and depression significantly explained 68% of the variance in distress. Mood-cognitive cluster Sickness-behavior cluster Treatment-related, or pain cluster Although the symptoms within each cluster were not identical across the three time points, the three clusters were identified. Mood-cognitive cluster Sickness-behavior cluster Treatment-related, or pain cluster The factor solution derived using the severity ratings fit the data better. Patients with had higher symptom cluster severity scores than the patients with prostate cancer. Psychoneurological cluster Upper gastrointestinal cluster The clustering of symptoms was generally stable, but weaker across the treatment trajectory. Demographic and clinical variables did not significantly influence symptom clustering. (continued on next page) C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 427

Table 3 (continued) Author, year Primary aim Sample Design Questionnaire Analytic technique Main result 428 Kuo and Ma, 2002 To understand the correlation of symptom distresses and coping strategies of patients with lung cancer 73 patients with non-small-cell lung cancer Symptom Distress Scale The Coping Strategies Scale Correlations Clear correlations were seen between some symptom distresses, especially for fatigue, lack of appetite, insomnia, increased sputum, and difficulty breathing. Participants with higher physical symptom distress had higher psychological distress and emotion-focused coping strategy frequency. Maliski et al., 2008 To identify symptom clusters that include urinary and erectile dysfunction 402 patients with prostate cancer Molassiotis et al., 2010 To explore clusters of symptoms over time 143 patients with cancer Olson et al., 2008 To develop a causal model of the relationships between symptoms To investigate the changing associations among the symptoms 82 cancer patients from an existing palliative care database Urinary, sexual, and bowel function from Prostate Cancer Index Short Form (PCI -SF), pain, and emotional distress from SF-36 MSAS ESAS Number and co-occurrence of symptoms Correlation factor analysis Cluster analysis Structural equation model Cluster 1: fatigue and emotional Distress Cluster 2: sexual dysfunction, bowel dysfunction, and pain When clusters occured, fatigue and emotional distress often were included Gastrointestinal cluster Hand/foot cluster Body image cluster Respiratory cluster Nutritional cluster Emotional symptom cluster Symptom clusters identified at the first assessment maintained across the assessment points with slight variations. Exogenous variables:, anxiety, nausea, shortness of breath and drowsiness Endogenous variables: Appetite, tiredness (fatigue), depression, and well-being The model fit acceptably. Drowsiness displayed consistent effects on appetite, tiredness and well-being. Anxiety s effect on well-being shifted importantly. C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 Reyes-Gibby et al., 2007 To determine the prevalence, and cooccurrence, of symptoms and to identify the extent to which symptoms interfered with function 48 patients with pancreatic cancer treated with chemoradiation on a Phase I protocol MDASI cluster analysis Over the course of the study, fatigue and lack of appetite formed a distinct grouping. The proportion of patients reporting moderate to severe symptoms was increased during chemoradiation and decreased after chemoradiation at 94 days follow-up. Ridner, 2005 To describe QOL and a symptom cluster associated with treatmentrelated lymphedema 128 patients with Symptom checklist Skin/arm condition CESD POMS-SF n FACT with FACT-B o ULL 27 p WCLS q e Alteration in limb sensation, loss of confidence in body, decreased physical activity, fatigue, and psychological distress Women with lymphedema scored lower on QOL.

Suwisith et al., 2008 To explore symptom clusters across two symptom dimensions and their influences on functional status 320 patients with e By severity: emotion related, GI and fatigue related, image related cutaneous symptoms, and pain related. By distress: emotions and pain related, GI and fatigue related, and image related cutaneous symptoms. The clusters identified by severity and distress explained 19.8% and 17.4% of the variance in the functional status respectively. Walke et al., 2007 Walsh and Rybicki, 2006 Wang et al., 2008 Wang et al., 2006 To determine the association of a range of symptoms with QOL, self-rated health, and functional status among chronically ill adults To determine if symptom clusters could be identified To explore the symptom clusters and relationships to symptom interference with daily life in Taiwan lung cancer patients To establish a profile of different symptoms over the time of therapy and to examine symptom-related functional interference in patients 226 Participants with chronic ill (79 Cancer) 922 patients with advanced cancer 108 patients with lung cancer 64 patients with locally advanced unresectable non-small-cell lung cancer (NSCLC) ESAS Quality of life with a single-item Self-rated health Activities of daily living Eastern Cooperative Oncology Group performance status and symptom severity MDASI-Taiwanese MDASI Principal component analysis cluster analysis cluster analysis Mixed effect growth-curve model Physical: physical discomfort, fatigue, problems with appetite, and pain Affective: feelings of depression and anxiety Shortness of breath The Physical and Affective components were associated with poorer quality of life. : anorexia-cachexia: fatigue, weakness, anorexia, lack of energy, dry mouth, early satiety, weight loss, taste changes Neuropsychological: sleep problems, depression, anxiety Upper gastrointestinal: dizzy spells, dyspepsia, belching, bloating Nausea and vomiting Aerodigestive: dysphagia, dyspnea, cough, hoarseness : pain, constipation General: fatigue, sleep disturbance, pain, drowsiness, lack of appetite, shortness of breath, numbness, difficulty remembering, dry mouth, distress, and sadness Gastrointestinal: nausea and vomiting General symptom cluster could predict symptom interference. Steady increase: pain and sore throat Early increase: nausea and vomiting Early/late increase: fatigue, lack of appetite, drowsiness, sleep disturbance, dry mouth, and distress Minimal change: sadness, difficulty remembering, and others Early/late increase symptom cluster had the highest predictive value for total interference. (continued on next page) C. Xiao / European Journal of Oncology Nursing 14 (2010) 417e434 429