Network analysis of the multidimensional symptom experience of oncology

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

LanguageEnglish
Article number2258
Number of pages11
JournalScientific Reports
Volume9
DOIs
Publication statusPublished - 19 Feb 2019

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Oncology
Electric network analysis
Chemotherapy
Nausea
Drug Therapy
Neoplasms
Therapeutics

Keywords

  • oncology patients
  • network analysis
  • core symptoms
  • Pairwise Markov Random Fields (PMRF)

Cite this

Papachristou, N., Barnaghi, P., Cooper, B., Kober, K. M., Maguire, R., Paul, S. M., ... Miaskowski, C. (2019). Network analysis of the multidimensional symptom experience of oncology. Scientific Reports, 9, [2258]. https://doi.org/10.1038/s41598-018-36973-1
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. ; Miaskowski, Christine. / Network analysis of the multidimensional symptom experience of oncology. In: Scientific Reports. 2019 ; Vol. 9.
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Papachristou, N, Barnaghi, P, Cooper, B, Kober, KM, Maguire, R, Paul, SM, Hammer, M, Wright, F, Armes, J, Furlong, EP, McCann, L, Conley, YP, Patiraki, E, Katsaragakis, S, Levine, JD & Miaskowski, C 2019, 'Network analysis of the multidimensional symptom experience of oncology' Scientific Reports, vol. 9, 2258. https://doi.org/10.1038/s41598-018-36973-1

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.; Miaskowski, Christine.

In: Scientific Reports, Vol. 9, 2258, 19.02.2019.

Research output: Contribution to journalArticle

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T1 - Network analysis of the multidimensional symptom experience of oncology

AU - Papachristou, Nikolaos

AU - Barnaghi, Payam

AU - Cooper, Bruce

AU - Kober, Kord M.

AU - Maguire, Roma

AU - Paul, Steven M.

AU - Hammer, Marilyn

AU - Wright, Fay

AU - Armes, Jo

AU - Furlong, Eileen P.

AU - McCann, Lisa

AU - Conley, Yvette P.

AU - Patiraki, Elisabeth

AU - Katsaragakis, Stylianos

AU - Levine, Jon D.

AU - Miaskowski, Christine

PY - 2019/2/19

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N2 - 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.

AB - 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.

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