Faculty, Staff and Student Publications
Publication Date
1-1-2023
Journal
Cancer Medicine
Abstract
Background: Symptom networks can provide empirical evidence for the development of personalized and precise symptom management strategies. However, few studies have explored the symptom networks of multidimensional symptom experiences in cancer survivors. The objectives of this study were to generate symptom networks of multidimensional symptom experiences in cancer survivors and explore the centrality indices and density in these symptom networks METHODS: Data from 1065 cancer survivors were obtained from the Shanghai CANcer Survivor (SCANS) Report. The MD Anderson Symptom Inventory was used to assess the prevalence and severity of 13 cancer-related symptoms. We constructed contemporaneous networks with all 13 symptoms after controlling for covariates.
Results: Distress (rs = 9.18, rc = 0.06), sadness (rs = 9.05, rc = 0.06), and lack of appetite (rs = 9.04, rc = 0.06) had the largest values for strength and closeness. The density of the "less than 5 years" network was significantly different from that of the "5-10 years" and "over 10 years" networks (p < 0.001). We found that while fatigue was the most severe symptom in cancer survivorship, the centrality of fatigue was lower than that of the majority of other symptoms.
Conclusion: Our study demonstrates the need for the assessment of centrality indices and network density as an essential component of cancer care, especially for survivors with < 5 years of survivorship. Future studies are warranted to develop dynamic symptom networks and trajectories of centrality indices in longitudinal data to explore causality among symptoms and markers of interventions.
Keywords
Humans, Cancer Survivors, China, Survivors, Survivorship, Fatigue, Neoplasms, cancer, network analysis, survivorship, symptom network
DOI
10.1002/cam4.4904
PMID
35651298
PMCID
PMC9844664
PubMedCentral® Posted Date
6-1-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons