JPM Review December 2011

1. JPM Review December 2011 NW Arkansas Palliative Care Collaborative

2. Cancer Symptom Clusters:Clinical and Research Methodology • JordankaKirkova, M.D., AynurAktas, M.D.,Declan Walsh, M.Sc., FACP, FRCP (Edin), and Mellar P. Davis, M.D., FCCP • Taussig Cancer Institute, Department of Solid Tumor Oncology • The Harry R Horvitz Center for Palliative Medicine, Cleveland Clinic, Cleveland, Ohio.

3. Abstract Introduction: Patients with cancer experience multiple symptoms that frequently appear in groups or clusters. We conducted a comprehensive clinical review of cancer symptom cluster studies to identify common symptom clusters (SC), explore their clinical relevance, and examine their research importance. Methods: Published studies and review articles on cancer SC were obtained through a literature search. We identified 65 reports. These varied in assessment instruments, outcomes, design, population characteristics, and study methods.

4. Results: • Two main approaches to symptom cluster identification were found: clinical and statistical. • Clinically determined SC were based upon observations of symptom co-occurrence, associations, or interrelations. These included fatigue-pain, fatigue-insomnia, fatigue-insomnia-pain, depression-fatigue, and depression-pain. They were analyzed by multivariate analysis. They had low to moderate statistical correlations. • Disease- or treatment related SC were influenced by primary cancer site, disease stage, or antitumor treatment. • SC determined by statistical analysis were identified by factor and cluster analysis through nonrandom symptom distribution. • Nausea-vomiting, anxiety-depression, fatigue-drowsiness, and pain-constipation consistently clustered by either or both of these statistical methods. • The individual symptoms of pain, insomnia, and fatigue often appeared in different clusters. A consensus about standard criteria and methodological techniques for cluster analysis should be established.

5. Conclusions: Several important cancer SC have been identified. Nausea-vomiting, anxiety-depression, and dyspnea-cough clusters were consistently reported. The techniques of symptom cluster identification remain a research tool, but one with considerable potential clinical importance. Further research should validate our analytical techniques, and expand our knowledge about SC and their clinical importance.

6. Consistent Symptom Clusters Fatigue was found to be the most frequent dependant symptom variable. Despite differences in population and design, fatigue consistently clustered with pain. Nausea and vomiting clustered consistently Dyspnea consistently clusters with cough but also with dysphagiaand muscle weakness. Anxiety and depression cluster together consistently.

7. Further Research Presumably a common mechanism for some SC may exist, e.g., cytokine-induced symptoms. Relationships between neuroimmunologicresponses, proinflammatorycytokines, and SC need to be explored. Any relationship between cancer prognosis and SC also needs to be explored further.

8. Subgroups of Advanced Cancer Patients Clusteredby Their Symptom Profiles: Quality-of-Life Outcomes • Amna Husain, M.D., M.P.H., Jeff Myers, M.D., MSEd, Debbie Selby, M.D., Barbara Thomson, M.Sc., and Edward Chow, MBBS, M.Sc., Ph.D. • Centre for Palliative Care, Mount Sinai Hospital, Toronto, Ontario, Canada. • Department of Palliative Medicine, Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

9. Abstract Background: Symptom cluster analysis is a new frontier of research in symptom management. This study clustered patients by their symptom profiles to identify subgroups that may be at higher risk for poor quality of life (QOL) and that may, therefore, benefit most from targeted interventions.

10. Methods: Longitudinal study of metastatic cancer patients using the Edmonton Symptom Assessment Scale (ESAS). We generated two-, three-, and four-cluster subgroups and examined the relationship of cluster membership with patient outcomes. To address the problem of missing longitudinal data, we developed a novel outcome variable (QualTime) that measures both QOL and time in study.

11. Results: • Two hundred and twenty-one patients with a mean Palliative Performance Scale (PPS) of 59.1 were enrolled. • The three-cluster model was chosen for further analysis. • The low-burden subgroup had all low severity symptom scores. • The intermediate subgroup separates from the low-burden group on the ‘‘debility’’ profile of fatigue, drowsiness, appetite, and well-being. • The high-burden group separates from the intermediate-burden group on pain, depression, and anxiety. • At baseline, PPS ( p = 0.0003) and cluster membership ( p < 0.0001) contributed significantly to global QOL. • In univariate analysis, cluster membership was related to the longitudinal outcome, QualTime. • In a multivariate model, the relationship of PPS to QualTime was still significant ( p = 0.0002), but subgroup membership was no longer significant ( p = 0.1009).

12. Conclusion: PPS is a stronger predictor of the longitudinal variable than cluster subgroups; however, cluster subgroups provide a target for clinical interventions that may improve QOL.

13. Discussion: • The study found that cluster subgroup membership is associated with QOL over the period of the study, but that relationship is no longer significant when performance status is added to the model. • That is, baseline performance status is a stronger predictor of the longitudinal QOL variable than cluster subgroups. • This finding begs the question, ‘‘Why study cluster subgroups?’’ • Unlike performance status, cluster subgroups provide a target for clinical interventions that may improve QOL. • Therefore, in addition to performance status, optimal assessment should include an inventory of symptoms in order to identify subgroups of patients with higher-risk symptom profiles.