University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Network Analysis of the Multidimensional Symptom Experience of Oncology

Papachristou, Nikolaos, Barnaghi, Payam, Cooper, Bruce, Kober, Kord M, Maguire, Roma, Paul, Steven M, Hammer, Marilyn, Wright, Fay, Armes, Jo, Furlong, Eileen P , McCann, Lisa, Conley, Yvette P, Patiraki, Elisabeth, Katsaragakis, Stylianos, Levine, Jon D and Miaskowski, Christine (2019) Network Analysis of the Multidimensional Symptom Experience of Oncology Scientific Reports, 9, 2258(2019).

[img] Text
Network.Analysis.NSR.revision76922.pdf - Accepted version Manuscript
Restricted to Repository staff only

Download (3MB)
[img] Text
Network.Analysis.Appendix.NSR76921.pdf - Accepted version Manuscript
Restricted to Repository staff only

Download (1MB)
[img]
Preview
Text
Network_analysis.pdf - Version of Record

Download (1MB) | Preview

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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Papachristou, Nikolaosn.papachristou@surrey.ac.uk
Barnaghi, PayamP.Barnaghi@surrey.ac.uk
Cooper, Bruce
Kober, Kord M
Maguire, Romar.maguire@surrey.ac.uk
Paul, Steven M
Hammer, Marilyn
Wright, Fay
Armes, Jojo.armes@surrey.ac.uk
Furlong, Eileen P
McCann, Lisa
Conley, Yvette P
Patiraki, Elisabeth
Katsaragakis, Stylianos
Levine, Jon D
Miaskowski, Christine
Date : 19 February 2019
Funders : Horizon 2020
DOI : 10.1038/s41598-018-36973-1
Copyright Disclaimer : © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Uncontrolled Keywords : Oncology; Signs and symptoms
Depositing User : Melanie Hughes
Date Deposited : 23 Nov 2018 14:54
Last Modified : 18 Mar 2019 15:16
URI: http://epubs.surrey.ac.uk/id/eprint/849931

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800