Abstract
Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. 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. 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. 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). 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. Based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. 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.
Highlights
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 study is the first to use Network Analysis (NA) to evaluate the relationships among symptoms and symptom clusters using ratings of symptom occurrence, severity, and distress, in a sample of oncology patients undergoing chemotherapy (CTX; n = 1328)
The use of NA to understand the symptom experience of oncology patients has the potential to increase our knowledge of the structural relationships among co-occurring symptoms and symptom clusters; the core symptoms driving associations between and among symptoms, and how co-occurring symptoms and symptom clusters change based on the dimension of the symptom experience that is used to create the network
Summary
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]. 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 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. We explore the complex organisation and interconnectedness of cancer symptoms and associated clusters by using two different models of Pairwise Markov Random Fields (PMRF)[27,28,29] on binary symptom occurrence and ordinal symptom severity and distress data. Our study is the first to use NA to evaluate the relationships among symptoms and symptom clusters using ratings of symptom occurrence, severity, and distress, in a sample of oncology patients undergoing chemotherapy (CTX; n = 1328). The interrelationships among symptoms inside and outside of a symptom cluster are described
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