Purpose/Objectives: To provide an integration and synthesis of literature on the definition and importance of the symptom cluster, theoretical frameworks to explain it, analysis strategies to identify it, interventions to alleviate it, and suggestions for future research.
Data Sources: A literature review from 1995-2007 was conducted using MEDLINE®. Clinical guidelines, descriptive research, intervention studies of multiple symptoms, and theoretical and conceptual articles were examined. Articles were reviewed if at least two of the four symptoms of interest were examined in relation to one or more other symptoms. Conceptual models were included if they explained or allowed for the notion of a symptom cluster.
Data Synthesis: Four symptoms were examined as a candidate symptom cluster for this analysis: fatigue, insomnia, pain, and depression. Symptom clusters were identified by expert opinion, group comparisons, shared variance among symptoms (including factor analysis and mediation analysis), identification of subgroups, influence of symptoms on patient outcomes, or the identification of a common underlying mechanism. Regardless of the method chosen for identifying a symptom cluster, the substantial evidence showed that various combinations of the target symptoms formed a symptom cluster.
Conclusions: Although the findings suggest that fatigue, insomnia, pain, and depression constitute a viable cluster for further study, more research is needed to define the cluster and describe its underlying mechanisms. Addressing multiple symptoms is beneficial in reducing negative patient outcomes; however, more work needs to be done to understand the efficacy of intervention for symptom clusters.
Implications for Nursing: When conducting symptom assessment, healthcare providers should address the four symptoms (fatigue, insomnia, pain, and depression) targeted in this review because evidence of clustering exists. Guidelines provided by the National Comprehensive Cancer Network for fatigue and distress provide algorithms and decision trees for assessment and management.