Symptom Clusters and Symptom Network Analysis During Immunotherapy in Lung Cancer Patients

Abstract

Abstract Objective: This study aimed to analyze symptoms in lung cancer patients undergoing immunotherapy and to identify core symptom clusters through network analysis, thereby laying the groundwork for effective symptom management programs. Methods: The study involved 240 lung cancer patients receiving immunotherapy. Participants were assessed using the memory symptom scale. Exploratory factor analysis extracted symptoms, and network analysis using JASP 0.17.3 explored centrality indices and density in these symptom networks. Results: Five symptom clusters were identified: emotion-related, lung cancer-specific, perception, skin, and neurological symptom clusters, with a cumulative variance contribution rate of 55.819%. Network analysis revealed sadness as the most intense symptom (rs = 2.189), dizziness as the most central (rc = 1.388), and fatigue as the most significant bridging symptom (rB = 2.575). Conclusion: This study identified five symptom clusters and networks during the immunotherapy in lung cancer patients. The centrality indices and network density from the network analysis can assist healthcare professionals in devising more precise symptom management strategies.