Symptom groups and network analysis of HAIC liver cancer patients
HAIC肝癌患者的症状群及其网络分析

摘要

目的 探讨HAIC治疗原发性肝癌患者的症状群构成及其相关性，明确群内核心症状，以期为临床症状策略管理提供参考。方法 采取便利抽样原则选取130例HAIC治疗患者为研究对象。采用一般资料调查表、中文版安德森症状测评量表及原发性肝癌特异性症状模块对患者进行问卷调查。症状群的提取采用探索性因子分析，以JASP软件绘制网络分析图及各症状中心指标图，分析群内症状之间关系。结果 强度中心性相关性结果表明，乏力（r=0.92）、腹痛（r=0.88）、恶心（r=0.85）。准确性检验结果表明，网络结构的边权重与原始数据相关系数（r=0.93，P<0.001）。以具体的症状详情中取样五个症状群（疼痛、胃肠道、睡眠情绪、病感、HAIC）。通过分析后可见，密度与强度最高的是行走，睡眠障碍的中介度最高。结论 HAIC治疗期间患者存在多个症状群，疼痛是HAIC患者症状群的核心症状，情绪睡眠症状群起桥梁作用，通过减弱核心症状对患者身心健康的影响来改善周围其他症状，从症状群的角度对患者进行症状管理，从而提高患者的生命质量。

Abstract

Objective To explore the composition and correlation of symptom clusters in patients with primary liver cancer treated with HAIC, clarify the core symptoms within the cluster, and provide reference for clinical symptom strategy management.
Methods Convenience sampling was used to select 130 HAIC treated patients as the research subjects. A questionnaire survey was conducted on patients using a general information questionnaire, the Chinese version of the Anderson Symptom Assessment Scale, and a primary liver cancer specific symptom module. Exploratory factor analysis was used to extract symptom clusters, and JASP software was used to draw network analysis graphs and indicator graphs of each symptom center to analyze the relationships between symptoms within the cluster.
Results The results of the intensity centrality correlation showed fatigue (r=0.92), abdominal pain (r=0.88), and nausea (r=0.85). The accuracy test results showed that the correlation coefficient between the edge weights of the network structure and the original data was r=0.93, P<0.001. Sample five symptom clusters based on specific symptom details (pain, gastrointestinal tract, sleep mood, symptoms, HAIC). After analysis, it can be seen that walking has the highest density and intensity, and the mediation degree of sleep disorders is the highest.
Conclusion   During HAIC treatment, patients have multiple symptom clusters, with pain being the core symptom of the HAIC symptom cluster. The emotional sleep symptom cluster serves as a bridge, reducing the impact of the core symptom on the patient's physical and mental health to improve other surrounding symptoms. From the perspective of symptom clusters, symptom management can be implemented to improve the patient's quality of life.