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J Palliat Med. 2011 Oct;14(10):1149-66. doi: 10.1089/jpm.2010.0507. Epub 2011 Aug 23.

Cancer symptom clusters: clinical and research methodology.

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  • 1Taussig Cancer Institute, Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, Ohio 44195, USA.

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.

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.

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.

PMID:
21861613
[PubMed - indexed for MEDLINE]
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