Network Analysis of the Multidimensional Symptom Experience of Oncology View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Nikolaos Papachristou, Payam Barnaghi, Bruce Cooper, Kord M. Kober, Roma Maguire, Steven M. Paul, Marilyn Hammer, Fay Wright, Jo Armes, Eileen P. Furlong, Lisa McCann, Yvette P. Conley, Elisabeth Patiraki, Stylianos Katsaragakis, Jon D. Levine, Christine Miaskowski

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. More... »

PAGES

2258

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-36973-1

DOI

http://dx.doi.org/10.1038/s41598-018-36973-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112225971

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30783135


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