Symptom clusters and network analysis in patients with gynecologic cancer undergoing chemotherapy: A cross-sectional study
Jiajia Zhang, Shan Wang, Yuan Wang, Jiaru Zhuang, Ling Hang, Yibo Wu, Dewu Xu, Chunyan Huang
Abstract
Objective: This study aimed to explore the complex relationships among symptoms and symptom clusters in patients with gynecologic cancer receiving chemotherapy using symptom network analysis, and to identify core symptoms and core symptom clusters. Methods: A cross-sectional study was conducted at the Affiliated Hospital of Jiangnan University from December 2023 to June 2024, including 221 patients with gynecologic tumors. Participants completed demographic and clinical information questionnaires and the Chinese version of the MD Anderson Symptom Inventory (MDASI-C). Univariate analysis and multiple linear regression were used to screen covariates, exploratory factor analysis to determine symptom clusters, and network analysis to identify core symptoms and core symptom clusters. Results: = 192, 86.9%) were the most common symptoms, while fatigue (mean = 4.17, SD = 2.07) and distress (mean = 3.43, SD = 2.20) were the most severe symptoms. Several distinct symptom clusters were identified: sickness behavior, gastrointestinal, psychological, and side-effect clusters. In the constructed network, fatigue emerged as the most central symptom (rs = 1.28), while the sickness behavior cluster was identified as the most central symptom cluster (rs = 1.11). Conclusions: Patients with gynecologic cancer undergoing chemotherapy commonly experience a range of symptoms. Our findings suggest that targeted interventions focusing on the sickness behavior symptom cluster may help reduce the overall symptom burden and assist caregivers in developing more effective symptom management strategies.