Advances in Conceptual and Methodological Issues in Symptom Cluster Research: A 20-Year Perspective

Abstract

Two conceptual approaches are used to evaluate symptom clusters: "clustering" symptoms (ie, variable-centered analytic approach) and "clustering" patients (ie, person-centered analytic approach). However, these methods are not used consistently and conceptual clarity is needed. Given the emergence of novel methods to evaluate symptom clusters, a review of the conceptual basis for older and newer analytic methods is warranted. Therefore, this article will review the conceptual basis for symptom cluster research; compare and contrast the conceptual basis for using variable-centered versus patient-centered analytic approaches in symptom cluster research; review their strengths and weaknesses; and compare their applications in symptom cluster research.

Figure 1.

Two conceptual approaches to symptom cluster research. A) Illustrates the identification of symptom clusters using a variable-centered approach. B) Illustrates the identification of subgroups of patients based on their experience with a pre-specified symptom cluster (e.g., pain, fatigue, sleep disturbance, depression). Adapted from Miaskowski C, Aouizerat BE, Dodd M, Cooper B. Conceptual issues in symptom clusters research and their implications for quality-of-life assessment in patients with cancer. J Natl Cancer Inst Monogr. 2007;(37):39–46. doi:10.1093/jncimonographs/lgm003. Reprinted with permission from the Journal of the National Cancer Institute Monographs.

Figure 2.

A) An undirected graphical model with seven nodes. Each node represents a symptom. The presence of an edge between two nodes indicates a relationship between them. B) This figure represents the estimated network of 38 cancer symptoms across the “distress” symptom dimension. In this figure, the node size corresponds to the symptom distress scores and the strength of the relationship between nodes is illustrated by the thickness of the edges. Green edges indicate positive relationships and red edges indicate negative relationships. Symptom clusters were identified using a community detection algorithm and are identified by the color of the symptoms within each cluster. Adapted from Papachristou N, Barnaghi P, Cooper B, et al. Network analysis of the multidimensional symptom experience of oncology. Article. Sci Rep. 2019;9(1):2258. doi:10.1038/s41598-018-36973-1.