Symptom clusters and symptom network analysis during immunotherapy in lung cancer patients

This study analyzes symptoms in lung cancer patients undergoing immunotherapy to identify core symptom clusters through network analysis and lay a foundation for effective symptom management programs.

The sample comprised 240 lung cancer patients receiving immunotherapy. Participants were assessed using the Memorial Symptom Assessment Scale. Exploratory factor analysis was used to extract symptom clusters, and network analysis using JASP 0.17.3 was performed to explore the centrality indices and density of the symptom network.

Five symptom clusters were identified, i.e., emotion-related, lung cancer-related, physical, skin, and neural symptom clusters, with a cumulative variance contribution rate of 55.819%. Network analysis revealed that sadness was the most intense symptom (r_s = 2.189), dizziness was the most central symptom (r_c = 1.388), and fatigue was the most significant bridging symptom (r_b = 2.575).

This study identified five symptom clusters and a symptom network among lung cancer patients during immunotherapy. The network analysis’s centrality indices and network density results can assist healthcare professionals in devising more precise symptom management strategies.