Symptom networks in glioma patients: understanding the multidimensionality of symptoms and quality of life

Abstract

Objectives: To comprehend the complex relationship between symptoms such as fatigue and depression, and health-related quality of life (HRQoL) in patients with diffuse glioma, we estimated symptom networks to identify patterns of associations amongst a set of patient-reported outcome measures (PROMs). Additionally, we aimed to compare symptom networks of subgroups based on disease characteristics and fatigue status.Methods: We analyzed PROMs on fatigue, depression, cognitive functioning, brain tumor-related symptoms, and HRQoL from 256 patients. We stratified the sample based on disease status (preoperative vs. postoperative), tumor grade (grade II vs. III/IV), and fatigue status (non-fatigued vs. fatigued). We constructed group-level symptom networks with 21 nodes, wherein a node represented a subscale or item of a questionnaire, and an edge between two nodes signified a partial correlation between the two. We statistically compared global strength (GS, i.e., how strongly all nodes in a network are connected) between networks.Results: Across the networks, fatigue severity, depression, and social functioning were the most connected nodes. We found no differences in GS between the networks based on disease characteristics. However, GS was lower in the non-fatigued network compared to the fatigued network (GS=5.51 vs. GS=7.49, p<0.001).Conclusions: Symptom network analysis is a novel approach to understand and quantify the interrelatedness of symptoms in glioma. Interestingly, global network strength did not differ between networks based on disease-specific characteristics, but PROMs were more tightly intercorrelated in fatigued patients compared to non-fatigued patients. This underlines the need for integrative symptom management targeting fatigue.

A network approach to investigating the inter-relationship between health-related quality of life dimensions and depression in 1735 Chinese patients with heterogeneous cancers

“…In addition, the network analysis can be used to assess the complex interactions between symptoms of comorbid psychiatric disorders (e.g., depression and anxiety) or systems (e.g., quality of life and pain) and identifies the bridge symptoms between them, which are priority targets for clinical intervention (14)(15)(16). Network analysis was also applied in psychometric analysis of HRQOL data in previous studies (17)(18)(19).…”