Zhejiang Chinese Medical University, Hangzhou, 310053, China.
Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China.
Support Care Cancer. 2024 Oct 9;32(11):717. doi: 10.1007/s00520-024-08918-0.
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 = 2.189), dizziness was the most central symptom (r = 1.388), and fatigue was the most significant bridging symptom (r = 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.
本研究通过网络分析分析肺癌患者接受免疫治疗时的症状，确定核心症状群，为制定有效的症状管理方案奠定基础。
样本包括 240 名接受免疫治疗的肺癌患者。参与者使用 Memorial 症状评估量表进行评估。采用探索性因子分析提取症状群，采用 JASP 0.17.3 进行网络分析，探讨症状网络的中心性指数和密度。
确定了五个症状群，即情绪相关、肺癌相关、身体、皮肤和神经症状群，累积方差贡献率为 55.819%。网络分析显示，悲伤是最强烈的症状（r=2.189），头晕是最中心的症状（r=1.388），疲劳是最重要的桥接症状（r=2.575）。
本研究在肺癌患者接受免疫治疗期间确定了五个症状群和一个症状网络。网络分析的中心性指数和网络密度结果可以帮助医疗保健专业人员制定更精确的症状管理策略。
