This gap in knowledge prevents the identification of key symptom(s) that exert an influence on other co-occurring symptoms or symptom clusters that may be potential target(s) for therapeutic interventions. While progress is being made in symptom clusters research 4, one of the major gaps in knowledge using standard statistical approaches is that the nature of the relationships among individual symptoms and symptom clusters have not been evaluated. One of the underlying assumptions of this research is that symptoms that cluster together may share underlying mechanisms that are potential targets for therapeutic interventions. In order to advance symptom management science and gain a better understanding of oncology patients’ symptom experiences, research has focused on the evaluation of symptom clusters using techniques such as exploratory factor analysis or cluster analysis 4, 5, 6. Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress 1, 2, 3. Our findings can be used to guide the development of symptom management interventions based on the identification of core symptoms and symptom clusters within a network. Based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. Findings from this study provide the first direct evidence that the connections between and among symptoms differ depending on the symptom dimension used to create the network. Using two different models of Pairwise Markov Random Fields (PMRF), we examined the nature and structure of interactions for three different dimensions of patients’ symptom experience (i.e., occurrence, severity, distress). We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing chemotherapy.

Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress.

Symptom network analysis definition