Symptom Cluster Patterns During the First Year After Diagnosis with Cancer

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1 Vol. 39 No. 5 May 2010 Journal of Pain and Symptom Management 847 Original Article Symptom Cluster Patterns During the First Year After Diagnosis with Cancer Alex Molassiotis, RN, PhD, Yvonne Wengström, OCN, PhD, and Nora Kearney, RN, MSc School of Nursing (A.M.), University of Manchester, Manchester; Cancer Care Research Centre, University of Stirling (Y.W., N.K.), Stirling, United Kingdom; and Division of Nursing (Y.W.), Karolinska Institutet, Stockholm, Sweden Abstract Context. Research about clusters of symptoms in oncology is an emerging field of study. However, there is still conceptual confusion about clusters of symptoms and little agreement across studies. Objectives. The aim of the present study was to explore clusters of symptoms over time in a large heterogeneous group of patients with cancer and thereby contribute to the conceptual and methodological debate in this research area. Methods. A longitudinal design was used to assess symptoms in cancer patients over four time points during the first year after diagnosis using the Memorial Symptom Assessment Scale. The study recruited 143 patients from five U.K. cancer centers and provided 504 symptom assessments at the beginning of treatment and 3, 6, and 12 months later. Results. Six symptom clusters were identified at the first assessment, which were maintained across the assessment points with slight variations. These included gastrointestinal, hand/foot, body image, respiratory, nutritional, and emotional symptom clusters. The behavior of the clusters over time highlighted the complexities of symptom cluster assessment and the dynamic relationships between symptoms. Frequency, severity, and distress from symptoms were significantly higher (up to 75% higher) in patients who experienced a cluster of symptoms than in the overall sample, suggesting that symptom assessments in unselected patients underestimate the symptom burden in subgroups of patients. Conclusion. We propose attention to symptom clusters that are stable across time and include core or defining symptoms within the cluster, and we further discuss the usefulness and applicability of conceptual and methodological criteria used in this study for future symptom cluster research. J Pain Symptom Manage 2010;39:847e858. Ó 2010 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Key Words Symptom cluster, symptoms, oncology, Memorial Symptom Assessment Scale The projects from which data were drawn were funded by the Christie Hospital Charitable Trust and The Scottish Government Health Department. Address correspondence to: Professor Alex Molassiotis, RN, PhD, School of Nursing, University of Ó 2010 U.S. Cancer Pain Relief Committee Published by Elsevier Inc. All rights reserved. Manchester, Jean McFarlane Building, University Place, Manchester M13 9PL, United Kingdom. alex.molassiotis@manchester.ac.uk Accepted for publication: October 10, /$esee front matter doi: /j.jpainsymman

2 848 Molassiotis et al. Vol. 39 No. 5 May 2010 Introduction Research about clusters of symptoms in cancer care and palliative care has been a rapidly increasing area of focus over the past few years, particularly since the work published by Dodd et al. 1 in 2001 and the key article by Miaskowski et al. 2 in This interest came from an attempt to move away from a reductionist model of symptom research that focuses on single symptoms (or rather, to promote parallel work in the reductionist and the multiple-symptom models as they offer different insights into symptoms), recognizing that patients with cancer experience multiple concurrent symptoms that interact with each other and affect patient outcomes in a different way from that of single symptoms. An analysis of the symptom cluster has identified the ambiguous nature of the concept, its attributes, antecedents and consequences, and the variation in its measurement and assessment. 3 A review of the research about cancer symptom clusters published two years ago identified seven individual studies and a group of five studies validating the M. D. Anderson Symptom Inventory. 4 This review showed the lack of common clusters in breast and lung cancer patients, although a gastrointestinal cluster consisting of nausea and vomiting was a common cluster. It also highlighted a number of methodological disparities across published articles regarding the assessment scales used to extract clusters, statistical analyses, and the populations under study (mostly mixed cancer samples or breast and lung cancer populations). An analysis of data from 282 breast cancer patients has shown two distinct clusters, one being a psychoneurological cluster and the other a gastrointestinal cluster, which were generally stable across the treatment trajectory. 5 Gleason et al. 6 focused on brain tumor patients (n ¼ 66) and identified a language cluster and gastrointestinal cluster through exploratory factor analysis. Chen and Tseng 7 used data from a mixed cancer group of 151 patients and identified a threefactor model, including a sickness symptom cluster, a gastrointestinal symptom cluster, and an emotional symptom cluster. This clustering of symptoms was further validated with a sample of 321 patients and focused on nine symptoms, demonstrating satisfactory fit between the data and the prespecified threefactor model. 8 Although this type of exploratory work in symptom clusters is appropriate at this early stage of the development of the field, many questions remain unanswered. Furthermore, the limited clarity in conceptual and methodological issues makes this area of research complex. Some of the key questions for which there is no consensus currently include the following: How many symptoms need to be present together to form a cluster (minimum of two symptoms or three and above)? 1e3 What is the most appropriate statistical method for identifying symptom clusters? 9e13 What cut-off points for symptom severity would need to be to qualify a symptom for inclusion in a cluster? 2 Is it more appropriate to have homogeneous samples (providing more diseasespecific clusters but with lower variance) or heterogeneous samples (providing broader and common clusters across diagnostic groups, increasing variance at the cost of bringing into the model more confounding variables)? What criteria should be used to evaluate the relationships among the symptoms of a cluster? What elements of relationship are required before a group of symptoms can be classified as a cluster? Should presence, frequency, and/or distress from symptoms be examined as the variables to identify clusters? What scales should be used to identify clusters (i.e., generic vs. disease-specific; range and number of items in each scale)? Do symptom clusters need to be examined for stability across time or are crosssectional designs at a given time of the disease- and/or treatment-related trajectory adequate? What are the criteria for linking a statistically derived cluster to a clinically meaningful and valid cluster? 10e13 As the research arena in symptom clusters is developing, it is necessary to elucidate some of the above issues through empirical research

3 Vol. 39 No. 5 May 2010 Symptom Cluster Patterns the First Year After Diagnosis with Cancer 849 findings to move the field forward to new areas of exploration, such as impact of symptom clusters on patient outcomes, measurement of symptom clusters, and inclusion of such clusters as primary outcomes in the testing of symptom management interventions. Hence, the aim of the present study was to contribute to the debate in symptom cluster research and clarify some of the unresolved methodological issues that prohibit the further development of the field, taking into consideration the conceptual progress that has been made so far. More specifically, it was aimed to explore the patterns of clusters over time, their stability, the statistical strength of any given clusters, and the symptom experience of patients who reported symptoms in a cluster, using these results as examples of how to address more fully the methodological and conceptual questions raised earlier in this section. Methods Design A prospective longitudinal design was adopted for the purposes of the present study. This recognizes that the timing of measurement is an important factor because of the dynamic nature of symptoms and the changes expected to occur over time. 14 A time frame of 12 months from diagnosis was chosen to explore the development and change of symptom clusters over a long period of time, as the (limited) cluster research using prospective evaluations has focused on short time frames (i.e., from beginning of treatment to third treatment 5 or up to one month after radiotherapy 15 ). Sample and Settings Patients were recruited from a large specialist oncology hospital in the northwest region of the United Kingdom, which receives referrals from around the country, and four cancer centers in Scotland. Patients were recruited shortly after diagnosis, through consecutive sampling, and were followed up for one year from peridiagnosis to three months, six months, and 12 months after diagnosis. Patients were recruited if they were diagnosed with breast, gynecological, prostate, gastrointestinal, lung, and head and neck cancers to elicit a wider view of the symptom clustering not confined to symptoms unique to a specific group of cancer patients. The sample included patients with early or metastatic disease and those receiving chemotherapy or radiotherapy. Patients with cognitive impairment, metastasis with central nervous system involvement, life expectancy of less than six months at recruitment, or inability to complete the study questionnaire were excluded. Procedures Patients were recruited two to three weeks after diagnosis when they were coming to receive their first treatment. Patients were provided with information about the study, and written consent was obtained. If patients agreed to take part in the study, they completed the study scales at home toward the end of the first month after diagnosis. Subsequent assessments were carried out at three months, six months, and 12 months after diagnosis. These broad timings were selected to represent key milestones in the disease trajectory, including baseline data on the symptom experience just before starting treatments/the beginning of treatment, completion or close to completion of initial treatment plan (three-month point), free from treatments and waiting to see how well treatments have worked (six-month point), and looking at the future/survivorship (12-month point). Completed questionnaires were given back to the researchers directly or through self-addressed prepaid envelopes. The study was approved by the research and ethics committees of the participating hospitals and the university ethical boards. The Symptom Scale The symptom scale used in this study was the Memorial Symptom Assessment Scale (MSAS). 16 This scale evaluates 32 physical and psychological symptoms, considered to be a wide range of symptoms for assessment (compared with other popular scales in the literature such as the M. D. Anderson Symptom Inventory that has been frequently used in symptom cluster research but includes a short number of items). It also has been used in similar research more recently. 15 Finally, this measure has shown high correlation with quality of life and clinical status. 16 Internal

4 850 Molassiotis et al. Vol. 39 No. 5 May 2010 consistency of the scale is high, with Cronbach alphas ranging from 0.83 to 0.88, albeit with moderate alpha for the low-prevalence physical symptom subscale (0.58). Information about sociodemographic characteristics including age, gender, and marital status as well as disease- and treatmentrelated information (diagnosis and treatment received) were obtained from the patients in accordance with the requirements of the research ethics approval for the study. Data Analysis Descriptive statistics were used to summarize sociodemographic and treatment data and identify the number of subjects who experienced a given factor. Exploratory factor analysis with principal components (rotated component matrix with oblique rotation) and initial eigenvalues (the amount of variance in the variables accounted for by a component) of more than one were used to identify symptoms clustering together. This was followed from a scree plot analysis of eigenvalues against the number of factors to have an initial indication of how many factors account for the majority of the variance to avoid over- or underextraction of variables. Communalities were used to observe the level of shared variance between items. The Kaiser-Meyer-Olkin (KMO) measure was calculated to assess sampling adequacy (scores >0.50 indicate adequate sample size for the analysis). Cronbach alpha, as a measure of internal consistency among factor items, was used for each identified factor at each time point. Derived factors were discussed and interpreted between the research team, and final factor structure included factors with clinically contextual relevance. Results Sample Characteristics The sample consisted of 143 patients diagnosed with cancer, with an age range from 30 to 93 years. Sample size in subsequent assessments was 125 at T2, 123 at T3, and 113 at T4 (attrition 20.1%, total number of assessments ¼ 504). Table 1 shows other sociodemographic and treatment characteristics. Symptom Clusters and Their Pattern Over Time Six symptom clusters were identified at baseline. These clusters were interpreted as a gastrointestinal symptom cluster (core symptoms of nausea, vomiting, and feeling bloated); a hand/foot cluster (symptoms of numbness/ tingling of hand/feet and swelling of arms/ legs); a body image cluster (symptoms of hair loss, skin changes, and item I do not like myself ); a respiratory symptom cluster (symptoms of shortness of breath and cough); a nutritional symptom cluster (core symptoms of weight loss, difficulty swallowing, and lack of appetite); and an emotional symptom cluster (with a number of psychological symptoms present). In subsequent assessments, we did not run confirmatory analysis in the predefined clusters identified at T1, but rather we have carried out exploratory factor analysis as in the first time to assess if the same factors would be derived again with this method. With the exception of the respiratory cluster, which accounted for 25.2% of the sample at baseline, all other clusters concerned a small number of patients. The KMO statistic for this analysis was Communalities were high (0.60e0.72) in 21 items, and only one item had a communality of <0.40. Interfactor correlations allowed us to observe core symptoms in the cluster, that is, symptoms that define the cluster based on the symptom(s) with highest coefficient (i.e., weight loss in the nutritional cluster or feeling sad in the emotional cluster). Internal consistency in some of the clusters was moderate at this stage (Table 2). At the three-month assessment point, a more complex picture of the clusters was observed. Although the T2 clusters were largely the same as at T1 and the core symptoms were maintained, the T2 clusters showed a different pattern of behavior, with some new symptoms entering the cluster and reshaping the initial cluster. This led to high internal consistency within each cluster, accounting this time for 10%e34% of the patients experience. The KMO statistic in this analysis was 0.87, whereas communalities were also high (0.60e0.76) in 26 of the 32 items of the MSAS. Interfactor coefficients significantly increased compared with T1 (Table 2). Similar

5 Vol. 39 No. 5 May 2010 Symptom Cluster Patterns the First Year After Diagnosis with Cancer 851 Table 1 Sample Characteristics Characteristics n (%) Age group 30e60 years 65 (45.7) 61e84 years 75 (52.2) $85 years 3 (2.1) Gender Female 65 (45.7) Male 78 (54.3) Marital status Single 16 (11.5) Married 102 (70.4) Divorced/separated 16 (11.5) Widowed 9 (6.6) Diagnosis Gastrointestinal cancer 34 (23.8) Lung cancer 31 (21.7) Breast cancer 22 (15.4) Prostate cancer 20 (14.0) Gynecological cancer 20 (14.0) Head and neck cancer 16 (11.2) Treatment received Chemotherapy surgery 42 (29.3) Radiotherapy surgery 36 (25.1) Chemotherapy þ radiotherapy surgery 35 (24.4) Radiotherapy hormone 14 (9.7) treatment surgery Surgery alone 7 (4.8) Hormone treatment surgery 4 (2.7) Chemotherapy hormone 3 (2.0) treatment surgery Active monitoring 3 (2.0) cluster behaviors were observed at T3, although clusters accounted for a small proportion of the patients experience (up to 11.8%). The KMO statistic for this analysis was 0.94, communalities were high, with 29 of the 32 MSAS items having a value of 0.60e0.90, and there was high internal consistency of the factors (Table 2). At 12 months (T4), clusters decreased in the number of items that they included while maintaining their core/defining symptoms and accounted for a significant proportion of the patients experiencing each cluster (from 24.8% to 42.5%). The KMO statistic for this analysis was 0.91, communalities were high (0.60e0.87) in 30 of the 32 MSAS items, and there was high internal consistency of each cluster (Table 2). Clusters of Symptoms at Only One Assessment. There were several other symptoms clustering together with high coefficients, but these were only evident at one time point and not stable across time points. For example, at T1, mouth sores (coefficient ¼ 0.68), dry mouth (0.84) and changes in taste of food (0.39) (forming an oral cluster) were grouped together, but at subsequent times these symptoms became part of different clusters. At T2, a cluster that included having sweats (0.81) and feeling sad (0.59) appeared, forming a menopausal cluster. Although these clusters may be important in the evaluation of treatments, they were not maintained at other time points and hence were treatment specific only. Clusters with No Clinical Relevance. After interpretation of the (lack of) clinical relevance of some other clustering of symptoms that took place, several statistically strong clusters were excluded from the presentation. Examples of such symptoms included the symptoms of constipation (0.38) and feeling drowsy (0.39) at T1; feeling irritable (0.35), diarrhea (0.39), and having sweats (0.27) at T2; feeling bloated (0.45), difficulty swallowing (0.51), constipation (0.54), and itching (0.70) at T3, or difficulty sleeping (0.79) and problems with sexual interest (0.59) at T3; and dizziness (0.78) and pain (0.70), or changes in skin (0.72) and problems with urination (0.68), or cough (0.46), taste changes (0.84), and dry mouth (0.40) at T4. Symptom Experience in the Whole Sample and in the Subgroup of Patients Reporting Symptom Clusters. The analysis of frequency, severity, and distress scores in the whole sample and in the subsample of patients who reported as present all the symptoms within the identified clusters showed that there were significant differences between symptom experience in the whole sample and in the subsamples. The latter (smaller) group of patients had significantly higher scores in all three parameters of the symptom experience (frequency, severity, and distress). For example, distress from hair loss at T1 was 60% higher in the patients of the body image cluster than the overall sample. Similarly, distress from cough and shortness of breath was 25% higher in the patients who were part of the respiratory cluster at T1 compared with the whole sample of patients. Also, the scores were up to 75% higher in the patients of the cluster (gastrointestinal cluster). These differences were more profound at T1eT3, whereas at T4, scores were

6 Table 2 Symptom Cluster Structure, Interfactor Correlations, Cronbach Alpha, and Cluster Frequencies Over Time T1 Clusters (Soon After Diagnosis) T2 Clusters (Three Months After Diagnosis) T3 Clusters (Six Months After Diagnosis) T4 Clusters (12 Months After Diagnosis) Items Interfactor Correlations %of Patients Items Interfactor Correlations %of Patients Items Interfactor Correlations %of Patients Items Interfactor Correlations Nausea Nausea Nausea Nausea Vomiting 0.29 Vomiting 0.54 Vomiting 0.54 Vomiting 0.84 Feeling bloated 0.49 Feeling bloated 0.51 Feeling bloated 0.51 Lack of appetite 0.42 a ¼ 0.61 Taste changes 0.42 Taste changes 0.50 Difficulty 0.77 swallowing a ¼ 0.70 a ¼ 0.88 a ¼ 0.93 Numbness/tingling in hand/feet Swelling of arms/legs Numbness/tingling in hand/feet Numbness/tingling in hand/feet Numbness/tingling in hand/feet Swelling of arms/ 0.49 Swelling of arms/ 0.60 Swelling of arms/ 0.75 legs legs legs a ¼ 0.28 a ¼ 0.43 a ¼ 0.77 a ¼ 0.73 Hair loss Hair loss Hair loss d Do not like 0.28 Do not like myself 0.59 Do not like 0.59 myself myself a ¼ 0.50 Skin changes 0.57 Skin changes 0.85 a ¼ 0.73 a ¼ 0.91 Shortness of Shortness of Shortness of Shortness of breath breath breath breath Cough 0.21 Cough 0.57 Cough 0.41 Cough 0.48 a ¼ 0.51 a ¼ 0.62 Lack of energy 0.31 a ¼ 0.77 a ¼ 0.75 Difficulty Difficulty Difficulty Weight loss swallowing swallowing swallowing Weight loss 0.59 Weight loss 0.84 Weight loss 0.88 Diarrhea 0.81 Lack of appetite 0.32 Lack of appetite 0.70 Lack of appetite 0.83 a ¼ 0.83 Vomiting 0.33 Mouth sores 0.60 Dry mouth 0.51 Pain 0.34 Diarrhea 0.45 a ¼ 0.93 a ¼ 0.69 Vomiting 0.62 a ¼ 0.85 Feeling sad Feeling sad Feeling sad Feeling sad Worrying 0.60 Worrying 0.71 Worrying 0.79 Worrying 0.77 Feeling nervous 0.52 Feeling nervous 0.64 Feeling nervous 0.86 Feeling nervous 0.80 Difficulty 0.52 Difficulty 0.59 Difficulty 0.55 Difficulty 0.81 concentrating concentrating concentrating concentrating Dizziness 0.62 Dizziness 0.67 Dizziness 0.50 Lack of energy 0.46 Feeling drowsy 0.50 Feeling drowsy 0.55 Difficulty sleeping 0.41 Difficulty 0.61 sleeping Feeling irritable 0.49 Feeling irritable 0.67 a ¼ 0.90 a ¼ 0.88 Lack of energy 0.49 Lack of energy 0.55 a ¼ 0.78 Difficulty sleeping 0.51 a ¼ 0.87 The core or defining symptoms in each cluster containing more than two symptoms are shown in boldface. %of Patients 852 Molassiotis et al. Vol. 39 No. 5 May 2010

7 Vol. 39 No. 5 May 2010 Symptom Cluster Patterns the First Year After Diagnosis with Cancer 853 similar between the symptom cluster patients and the whole sample (Table 3). Discussion Conceptual Issues The present study is the first study to date to explore clusters of symptoms in cancer patients over the first 12 months after diagnosis, reporting cluster patterns, structure, and factor coefficients. It identified six relatively stable clusters over time. The gastrointestinal symptom cluster has commonly been identified as a cluster in the literature and includes always the items of nausea and vomiting 7,8,17e19 and often lack of appetite or taste changes. 5,20,21 In the only qualitative study exploring the patients perceptions of concurrent and associated symptoms related to nausea during chemotherapy, nausea, vomiting, feeling bloated, loss of appetite, and taste changes were commonly reported together. 22 Also, the emotional symptom cluster is another commonly identified cluster that includes a variety of affective items depending on the scale used to elicit the symptom reports and can include patients feeling distress, sadness, lack of appetite, sleeping difficulties, irritability, anxiety, and depression. 7,17,18,23,24 Interestingly, these symptoms seem to cluster together in both early and advanced cancer populations. It is also interesting that, whereas some studies consistently report a sickness behavior cluster of symptoms 7,8,15 that includes some of the above items, this study showed no such clear cluster and relevant items were all clustered under the emotional symptom cluster. Although theoretically such a cluster may exist, many components of the model behind the (cytokine-induced) sickness behavior are unsubstantiated and based on animal models to date. More basic science work in the future will confirm whether the model s assertions are correct and what is the link between sickness behavior and emotional symptoms, if any. The identified respiratory symptom cluster has also been described elsewhere, 18,21 alone or with other items (i.e., hoarseness of voice), deriving from lung cancer populations or patients with advanced disease. The hand/foot cluster is of interest as it initially affected a small proportion of the patients and had a low alpha coefficient for T1 and T2 but remained stable across times, increasing steadily in both the proportion of patients affected and its alpha coefficient, while interfactor correlations also increased over time. At T4, this cluster affected nearly 30% of the sample, indicating that it may be a cluster of symptoms reflecting more late effects. This had also had moderate loadings with the body image symptom cluster at T1; after T2, it had no loading with this cluster, suggesting some initial link between the hand/ foot cluster and body image cluster. The body image cluster was common mainly during treatment (when hair loss is common), whereas it affected a small number of patients at T3 and was not evident at T4. The nutritional symptom cluster has similar items to that discussed by Gift et al., 20 although the latter seemed to be a combination of our gastrointestinal and nutritional clusters. It is possible that these two clusters have common biological mechanisms or affect each other in a synergistic way. The fact that both clusters have items of the other cluster as their core items in a given time suggests the close link between these two clusters. There was a relative stability of the clusters across time, with some secondary symptoms changing over time. Stability of clusters in this study has been based on the presence of core or defining symptoms in the cluster across time. It is unreasonable to expect that, with different assessment times, different populations, and different assessment methods, exactly the same symptom cluster items are formed. Clusters are dynamic constructs reflecting complex relationships and, as such, need a degree of flexibility and inclusiveness in their definition and construction. We propose that stable clusters are those that have similar core symptoms across times and populations. For example, nausea and vomiting are present in all the gastrointestinal symptom clusters described in the literature and across times. Cough and breathlessness seem to cooccur across assessment points, as do the hand/foot and body image clusters. The more symptoms are part of a cluster, the less likely that the cluster will be maintained the same across times and populations. Hence, the core symptoms may define a cluster, and this can be based on the symptom(s) within a cluster with the highest interfactor correlation(s), that is, weight loss for the

8 Items Table 3 Mean Scores for Frequency, Severity, and Distress in the Whole Sample vs. the Sample Within Each Symptom Cluster Mean Score of Frequency/Severity/Distress at T1 Mean Score of Frequency/Severity/Distress at T2 All Patients Cluster Patients Items All Patients Cluster Patients Nausea 0.86/0.65/0.74 a 1.46/1.00/1.00 a Nausea 0.54/0.44/ /0.77/0.42 Vomiting 0.25/0.26/0.25 a 0.92/0.85/0.77 a Vomiting 0.17/0.16/0.19 a 0.62/0.62/0.67 a Feeling bloated 0.58/0.45/0.47 a 1.38/1.15/1.08 a Feeling bloated 0.55/0.41/ /0.17/0.25 Taste changes d/0.80/0.72 d/0.62/0.69 Numbness/tingling in 0.70/0.50/0.46 a 1.67/1.00/1.00 a Numbness/tingling in hand/feet 0.83/0.59/ /1.00/1.00 hand/feet Swelling of arms/legs d/0.05/0.06 a d/0.50/0.33 a Swelling of arms/legs d/0.13/0.16 a d/0.60/0.67 a Hair loss d/0.26/0.29 a d/2.22/2.67 a Hair loss d/0.41/0.43 a d/0.92/1.36 a Do not like myself d/0.32/0.38 a d/1.78/2.33 a Do not like myself d/0.57/0.71 a d/1.36/1.72 a Skin changes d/0.51/0.49 d/0.92/0.96 Cough 0.89/0.65/0.61 a 2.00/1.59/1.57 a Cough 0.87/0.64/0.66 a 1.86/1.45/1.47 a Shortness of breath 0.90/0.69/0.76 a 2.26/1.76/1.82 a Shortness of breath 0.94/0.77/0.89 a 1.82/1.59/1.82 a Difficulty swallowing 0.47/0.40/0.44 a 1.30/1.00/1.20 a Difficulty swallowing 0.38/0.35/ /0.18/0.18 Weight loss d/0.42/0.29 a d/1.30/0.80 a Weight loss d/1.74/0.32 d/0.45/0.73 Lack of appetite 0.70/0.57/0.50 a 1.90/1.40/1.40 a Lack of appetite 0.46/0.34/ /0.36/0.73 Vomiting 0.25/0.26/0.25 a 1.10/1.00/1.00 a Mouth sores d/0.34/0.33 d/0.27/0.36 Pain 1.49/1.22/ /1.50/1.60 Diarrhea 0.40/0.36/ /0.18/0.27 Vomiting 0.17/0.16/ /0.27/0.55 Feeling sad 0.88/0.82/0.96 a 1.71/1.57/1.71 a Feeling sad 0.79/0.66/ /0.60/1.00 Worrying 0.99/0.81/0.90 a 2.29/2.00/2.00 a Worrying 0.83/0.60/ /0.40/0.73 Feeling nervous 0.72/0.53/0.61 a 2.00/1.64/1.86 a Feeling nervous 0.68/0.54/ /0.87/0.67 Difficulty concentrating 1.10/0.76/0.89 a 2.36/1.64/1.43 a Difficulty concentrating 0.91/0.71/ /0.53/0.73 Dizziness 0.36/0.29/0.29 a 1.29/0.93/0.79 a Dizziness 0.37/0.30/ /0.40/0.67 Feeling drowsy 1.42/1.05/0.89 a 2.57/1.86/1.36 a Feeling drowsy 1.23/0.96/ /0.80/0.67 Feeling irritable 0.93/0.79/0.82 a 1.93/1.57/1.36 a Feeling irritable 0.83/0.69/ /0.60/1.13 Lack of energy 1.93/1.51/1.47 a 3.07/2.29/2.14 a Lack of energy 1.74/1.35/ /1.00/1.00 Difficulty sleeping 1.32/1.00/ /0.73/1.07 Mean Score of Frequency/Severity/Distress at T3 Mean Score of Frequency/Severity/Distress at T4 Nausea 0.89/0.26/0.25 a 3.00/3.00/4.00 a Nausea 0.21/0.17/ /0.25/0.29 Vomiting 0.87/0.02/0.03 a 2.00/3.00/4.00 a Vomiting 0.09/0.12/ /0.25/0.25 Feeling bloated 1.13/0.37/ /1.00/0 Lack of appetite 0.32/0.25/ /0.32/0.29 Taste changes d/1.06/0.34 d/1.00/1.00 Difficulty swallowing 0.19/0.12/ /0.25/0.29 Numbness/tingling 1.34/0.66/0.64 a 2.67/1.63/1.78 a Numbness/tingling in hand/feet 0.68/0.44/ /0.31/0.31 in hand/feet Swelling of arms/legs d/0.90/0.15 a d/1.78/1.44 a Swelling of arms/legs d/0.19/0.22 d/0.44/0.53 Hair loss d/0.93/0.25 a d/3.00/3.25 a d d d Do not like myself d/1.01/0.40 a d/3.00/3.25 a Skin changes d/0.94/0.26 a d/2.75/3.25 a Cough 1.22/0.56/0.52 a 2.45/2.10/2.30 a Cough 0.62/0.43/ /0.77/ Molassiotis et al. Vol. 39 No. 5 May 2010

9 Vol. 39 No. 5 May 2010 Symptom Cluster Patterns the First Year After Diagnosis with Cancer 855 Shortness of breath 1.11/0.61/0.56 a 2.80/2.30/3.33 a Shortness of breath 0.72/0.55/ /0.92/1.04 Lack of energy 1.36/1.42/1.34 a 2.82/2.30/3.20 a Difficulty swallowing 1.06/0.23/0.24 a 2.33/2.00/3.00 a Weight loss d/0.11/0.13 d/0.20/0.17 Weight loss d/0.95/0.20 a d/2.00/2.33 a Diarrhea 0.18/0.15/ /0.37/0.23 Lack of appetite 0.91/0.21/0.20 a 3.00/2.67/3.00 a Dry mouth 1.28/0.56/0.48 a 2.33/1.50/2.00 a Feeling sad 1.05/0.45/0.53 a 2.33/1.67/2.33 a Feeling sad 0.52/0.42/ /0.68/0.68 Worrying 1.09/0.52/0.59 a 3.00/2.00/3.00 a Worrying 0.71/0.53/ /0.66/0.71 Feeling nervous 0.96/0.38/0.43 a 3.00/1.67/3.00 a Feeling nervous 0.65/0.50/ /0.63/0.66 Difficulty concentrating 0.79/0.60/0.63 a 2.33/2.00/1.67 a Difficulty concentrating 0.69/0.49/ /0.61/0.68 Dizziness 0.92/0.22/0.27 a 2.00/1.33/1.67 a Lack of energy 1.35/0.98/ /0.79/0.84 Difficulty sleeping 1.26/0.83/0.82 a 3.00/2.00/2.00 a Difficulty sleeping 0.87/0.67/ /0.74/0.79 a Significant differences (P < 0.05) in symptom frequency, severity, and distress between the group of All Patients and those patients who were part of the given symptom cluster. nutritional cluster and feeling sad (and additionally worrying) for the emotional cluster. Such a stance will help future research in planning appropriate assessments for symptom clusters (and subsequently interventions), without the current confusion as to which symptoms are part of the cluster and which are not. There is emerging evidence from the limited literature in the field highlighting some of the potentially core or defining symptoms. These defining symptoms also could be set a priori based on biological mechanisms or past literature and assessed in a confirmatory statistical analysis model. Also, the fact that some symptoms are in and out in a given cluster may reflect presence of confounding variables. At this stage in the development of the field, it is preferable to use symptom clusters with fewer symptoms than multiple symptoms for the development of assessments and/or interventions and use other symptoms that occasionally are part of a given cluster as secondary and hypothesis-generating outcomes. It is interesting to note that symptom clusters accounted for a small fraction of the patients at T3 (six months after diagnosis). This is a time that the vast majority of patients have completed treatments, at which time symptom burden and symptom experience seem to be very low. T4 (12 months after diagnosis) is, on the other hand, a time with a significant symptom burden in at least onequarter to one-third of the patients. This symptom burden has implications for survivorship and the patient quality of life, as patients are attempting to live with their cancer and manage several symptoms that are becoming more chronic in nature and affecting their lives. There were significant differences in the frequency, severity, and distress from symptoms between those patients presenting with all the symptoms in the cluster and the whole sample, suggesting that the experience of symptoms is higher in those patients who have symptoms of a cluster together. This finding has implications for generic symptom assessment, as it seems that generic symptom assessments may significantly underestimate the symptom experience in the subgroup of patients who experience multiple symptoms

10 856 Molassiotis et al. Vol. 39 No. 5 May 2010 that are part of the same cluster of symptoms. Indeed, research has shown in the past that the presence of multiple symptoms is associated with poor functioning in cancer patients. 1,7,25,26 Furthermore, identification of this particular subgroup of patients with higher symptom experience scores may be a more appropriate target for symptom management interventions, and this may have more clinical, cost, and statistical advantages in symptom management research than targeting broader groups of patients with symptoms that are not interrelated with other symptoms as in the clustering model. Methodological Issues There is no consensus in the literature as to whether a symptom cluster should have a minimum of two symptoms, or three or more symptoms. 1,3 Based on the many clinicallymeaningful symptom clusters that include two symptoms (i.e., gastrointestinal cluster of nausea and vomiting, 7,19 or the respiratory and hand/foot cluster in the present study), it seems inappropriate not to consider the role of symptoms when they cluster only in pairs. Our study findings support the definition of the symptom cluster proposed by Kim et al. 3 that. consists of two or more symptoms that are related to each other and that occur together. Factor analysis has been a helpful method of analyzing complex relationships in a large sample. It is a data reduction method that is looking for associations among variables, assessing covariance. Factor analysis identifies the structures or dimensions that may exist in data sets and their variables, and if two or more symptoms are found to share a commonality with one or more factors, we can infer that these symptoms form a symptom cluster. However, the analysis does not tell us whether the factor model of a potential cluster is true. In simpler approaches to the identification of associations, for example, correlations, the covariance is not taken into consideration, and results may have little value. For example, in our study, for the variable shortness of breath, there were moderate-to-high Spearman correlations with another 24 variables (of the 32 variables of the MSAS). However, factor analysis has reduced these associations down to only two or three items that provide a different understanding of the symptom links. It is not a perfect method, however, as it depends on the number of variables included in the analysis (and essentially the number of symptoms included). It also may be affected by other extraneous variables that may cause symptoms to form clusters. 12 Finally, as with many other advanced statistical methods, factor analysis involves a sequence of often subjective decisions that may affect the symptom clusters derived. 12 In the future, efforts to explore symptom clusters should test the use of other methods, such as cluster analytic methods; this approach strives for mutual exclusiveness in its classification and the determination of which symptom forms a cluster and uses similarity measures, including correlation coefficients, distance measures, or association coefficients for binary variables. Cluster analysis can group symptoms that have similar patterns across patients and find clinical subgroups based on symptom experience and are useful in categorizing patients according to their similarity in symptom experience. 11 However, the application of traditional oneway clustering techniques for data analysis and exploring patterns poses significant problems. One-way clustering methods give a global view, whereas biclustering algorithms give a local view of the hidden relationships in the data. When one-way cluster is used, each record in a given cluster is evaluated by using all of its attributes; this may actually distort the cluster because all the attributes might not contribute towards that cluster together. At the same time, each cluster is characterized by all the activity of the records, which may not always be true. By using bicluster analysis, each record in a bicluster is selected using only a subset of the attributes, as well as a subset of the records to be included in the cluster. 27 Hence, neither factor analysis nor cluster analysis is without problems but can be complementary methods of analysis and be used with a clear understanding of their limitations, until new approaches to analyzing complex data sets are developed. The heterogeneity of the sample used may be a concern. However, such a sample allows for broad clusters to be formed and is applicable to a variety of cancer patients, rather than being disease specific. Disease-specific symptom clusters are appropriate too, but cannot be used generally for all cancer populations,

11 Vol. 39 No. 5 May 2010 Symptom Cluster Patterns the First Year After Diagnosis with Cancer 857 limiting their use. Also, differences between broad symptom clusters and disease- or treatment-specific clusters may highlight common cancer symptom experience vs. symptom experience that is related to each diagnostic and treatment group, providing a different and complementary insight into the patients symptom experience. Besides clinically meaningful symptoms, we have used two criteria in this study to evaluate the relationships among the symptoms of a cluster. These included Cronbach alpha reliability coefficients, representing the internal consistency of the items within each cluster (this should be a minimum of 0.70), and interfactor coefficients, representing the shared variance of symptoms with the latent factor (this should be a minimum of 0.30 representing an almost 10% variance, although many studies have selected in the past 0.40 as the minimum criterion). We have looked at the increasing interfactor coefficients and alpha for those clusters that did not initially meet one of the above two statistical criteria to assess the possibility of retaining these clusters. The latter approach provided the opportunity to include clinically meaningful symptoms meeting one of the two criteria but not linking well in the initial period of symptomatology, perhaps because of the influence of uncontrolled variables or overlapping clusters. In parallel to it, we have also looked at the difference of frequency, severity, and distress of the symptoms in the cluster patients vs. the whole sample; if this difference exceeded 10%, it indicated an important clinical difference in the symptom experience. Another observation in this study was that some symptoms were present in more than one cluster at the same time (i.e., vomiting in the gastrointestinal and nutritional clusters). There is no consensus to date as to whether a symptom needs to be exclusively part of only one cluster or can be linked with other clusters. 11 The current findings suggest that presence of a symptom in more than one cluster is clinically meaningful, contributes to increases in the internal consistency of the cluster, and, potentially, can be the biological outcome or other link between the two clusters, although it also may be that the factor analysis did not provide a clear distinction between the clusters. For example, the link between the gastrointestinal and nutritional factor is quite striking, as symptoms in the nutritional factor (difficulty swallowing and lack of appetite) become part of the gastrointestinal factor over time. Also, lack of energy has been clustered more commonly with the emotional cluster, reflecting its affective dimension, but was also present at T3 in the respiratory cluster, reflecting its physical dimension, possibly resulting from low oxygen saturation in people with breathlessness. As a result of the previous discussion, we propose a refinement of the definition of a symptom cluster 1,3 to be two or more symptoms that are clinically meaningful together, relate to each other at a given time, and share a significant variance with their cluster. The cluster should be stable across time, at least with respect to the core or defining symptom(s). Noncore or nondefining symptoms can be part of the cluster at different time points and in different populations as associated symptoms of the cluster. Symptoms of a cluster can also be part of more than one symptom cluster concurrently, linking the two clusters together in some way. There is a need to clarify the clustering of symptoms and the membership of symptoms to clusters, to design appropriate outcome measures and interventions targeting symptom clusters rather than individual symptoms, and a data bank of the increasing cluster-related information is necessary. Also, the identification of clusters will need to be supported by work related to the biological mechanisms behind the clusters (or other mechanisms responsible for clustering of symptoms); explain the complex relationships between symptoms of a cluster; identify appropriate or design new statistical approaches necessary to capture changes in clusters of symptoms; and focus on the effects the symptom clusters have on the patients functional status and quality of life and how patients can be better supported when they experience a cluster of symptoms to improve their quality of life and symptom experience. References 1. Dodd MJ, Miaskowski C, Paul SM. Symptom clusters and their effect on the functional status of patients with cancer. Oncol Nurs Forum 2001;28: 465e470.

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