基于网络分析的痛风患者症状群的核心与桥梁症状及与生活质量关系的研究 [中文引用][英文引用]

目的  基于网络分析识别痛风患者的症状群，探究其核心与桥梁症状，并分析其对生活质量的影响，为临床制定精准化的症状管理策略提供依据。方法  采用便利抽样法，选取 2024 年 8 月—2025 年 2 月成都市某三级甲等综合医院的 300例痛风患者为研究对象，通过一般资料调查表，痛风患者症状群评估量表及痛风影响量表（gout impact scale，GIS）进行调查。采用探索性因子分析确定症状群，运用 JASP 0.17.1 基于 EBICglasso 函数和 Spearman 相关性分析来构建症状网络图；利用分层回归分析控制混杂因素后分析症状群对生活质量的影响。结果  300 例患者均完成研究。痛风患者最常见的症状为焦虑（73.3%）、全身乏力或疲倦（71.3%）、睡眠不佳（68.0%）、关节功能受限（67.7%）、关节发红（67.3%）；最严重的症状为关节肿胀（55.3%）、情绪低落（59.0%）、全身乏力或疲倦（71.3%）、焦虑（73.3%）；提取出 5 个症状群分别为关节症状群、活动受限 - 疲乏症状群、心理 - 睡眠症状群、全身表现症状群、泌尿系统症状群；在症状网络分析中，关节功能受限的强度中心性最强（rs=1.656），关节疼痛的紧密中心性（rc=2.089）及中介中心性均最高（rb=2.929）。除泌尿系统症状群外（P>0.05），其余 4 个症状群对患者生活质量均有负面影响，其中关节症状群对痛风患者生活质量影响最大，对模型的解释变异为 11.5％。 结论  医护人员应做好患者症状群管理，特别应关注其关节症状群的核心、桥梁症状的管理，提高症状管理效率，以期改善患者生活质量。

Objective To identify symptom clusters in patients with gout based on network analysis,explore the core and bridging symptoms of gout and analyse the impact on quality of life so as to inform precise symptom management strategies. Methods Convenience sampling was used to recruit 300 patients with gout from our hospital between August 2024 and February 2025. Data were collected with a general-information form,gout symptom cluster scale and gout impact scale（GIS）. Exploratory factor analysis extracted symptom clusters. A symptom network was constructed with JASP 0.17.1 using the EBICglasso algorithm and Spearman correlations. Hierarchical regression and adjusting for confounders examined the independent effect of each cluster on the quality of life. Results All 300 patients completed the study. The most prevalent symptoms were found of anxiety（73.3 %）,general fatigue（71.3 %）,poor sleep （68.0 %）,limited joint function（67.7 %）and joint redness（67.3 %）. The most severe symptoms were joint swelling（55.3 %）,poor mood （59.0 %）,general fatigue（71.3 %）and anxiety（73.3 %）. Five symptom clusters were identified：articular clusters,restricted mobility/ fatigue clusters,psycho-sleep clusters,systemic clusters and urological clusters. In the network,limited joint function showed the highest strength centrality（rs=1.656）,whereas joint pain exhibited the highest closeness（rc=2.089）and the highest was intermediary centrality （rb=2.929）. Except the urological cluster（P>0.05）,the other four symptom clusters negatively affected HRQoL. The articular cluster exerted the greatest impact,explaining 11.5 % of additional variance in the regression model. Conclusion Medical staff should adopt a cluster-based approach to symptom management,with particular emphasis on the articular cluster and its core/bridging symptoms,to improve management efficiency and ultimately enhance the quality of life of patients.