Symptom Clusters and Symptom Network of Glioma Patients Undergoing Chemotherapy: A cross-sectional network analysis
Abstract
Purposes: This study aimed to explore symptom clustering patterns and interconnections in glioma chemotherapy patients, and identify
central and bridging symptoms for targeted interventions. Methods: A convenience sample of 330 patients was evaluated via the Chinese version of
the Anderson Brain Tumor Symptom Scale. Exploratory factor analysis was used to identify symptom clusters, and network analysis was performed
with R 4.4.2 to assess network centrality. Results: Five symptom clusters (emotion-related, neurocognitive, gastrointestinal, pain-related, other) were
identified, with a cumulative variance contribution rate of 70.41%. Network analysis showed that sad had the highest intensity (rs=1.4406), while
numbness was the most central (rc=0.0046) and bridging symptom (rb=164). Conclusion: Symptom clusters in these patients have complex interdependencies. Proactive monitoring of core and bridging symptoms (e.g., numbness, sadness) is crucial. Combining network centrality metrics with
cluster analysis can facilitate precision interventions to disrupt symptom networks, reduce symptom burden, and improve patient outcomes.
central and bridging symptoms for targeted interventions. Methods: A convenience sample of 330 patients was evaluated via the Chinese version of
the Anderson Brain Tumor Symptom Scale. Exploratory factor analysis was used to identify symptom clusters, and network analysis was performed
with R 4.4.2 to assess network centrality. Results: Five symptom clusters (emotion-related, neurocognitive, gastrointestinal, pain-related, other) were
identified, with a cumulative variance contribution rate of 70.41%. Network analysis showed that sad had the highest intensity (rs=1.4406), while
numbness was the most central (rc=0.0046) and bridging symptom (rb=164). Conclusion: Symptom clusters in these patients have complex interdependencies. Proactive monitoring of core and bridging symptoms (e.g., numbness, sadness) is crucial. Combining network centrality metrics with
cluster analysis can facilitate precision interventions to disrupt symptom networks, reduce symptom burden, and improve patient outcomes.
Keywords
Core symptoms; Glioma; Network analysis; Symptom clusters; Symptom network
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DOI: http://dx.doi.org/10.70711/pmr.v3i4.8808
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