Symptom networks of multidimensional symptom experiences in breast cancer survivors: A network analysis

Objectives We aimed to construct a symptom network for breast cancer patients, identify its core symptoms, and explore symptom clusters. This network approach may provide valuable insights for precise interventions to improve the overall quality of life in breast cancer patients. Methods A total of 462 eligible breast cancer patients were recruited. The severity of patients' symptoms was measured using the EORTC QLQ-C30 Chinese version scale and Zung Self-Rating Depression and Anxiety Scale. A regularized partial correlation network was established, and central symptoms were identified using Strength centrality. Results The strongest associations were observed between NV-AP (weight = 0.39), Dep-Anx (weight = 0.38), PA-DY (weight = 0.21), and Anx-SL (weight = 0.20). Fatigue was the most prevalent symptom among breast cancer patients, and fatigue was consistently the central symptom in the network, in addition to anxiety, appitie loss, and pain. DAG indicated that fatigue might influence overall symptoms in breast cancer patients. Three syomtom clusters were indentified: emotional symptoms (depression, anxiety, and insomnia), gastrointestinal symptoms (nausea/vomiting, diarrhea, and loss of appetite), and somatic symptoms (fatigue, pain, and dyspnea). Conclusions Fatigue, depression, and anxiety are highly prevalent and central symptoms in breast cancer patients. It is crucial to screen and provide early treatment for these symptoms to effectively manage them and enhance the overall quality of life for breast cancer patients. Future studies should focus on conducting longitudinal research to establish dynamic networks and investigate causal relationships between these symptoms.