目的 构建脑卒中患者症状群网络分析模型，探究其核心症状及桥梁症状，为症状的精准干预与管理提供依据。方法 2024年3—9月，便利抽样法选取滨州市某三级甲等医院642例脑卒中住院患者为研究对象，采用脑卒中患者症状群评估量表对其进行调查，应用R 4.4.2软件进行脑卒中患者症状群严重程度的网络分析，并分析中心性指标。结果 脑卒中患者最常见和最严重的症状是行走困难；在网络分析中，痛觉异常和温度敏感度降低、呛咳/误吸和吞咽困难、记忆力减退和反应迟钝症状之间关联性最强，正则化偏相关系数分别是0.60、0.53、0.48；温度敏感度降低可预测值（77.7%）最高；行走困难的强度（2.104）、节点预期影响（2.117）均最高，为症状群的核心症状；瘫痪桥梁预期影响值最高（1.798），为症状群的桥梁症状。结论 临床护理人员在干预过程中要针对性地识别强关联及高可预测症状，提高干预效率；应将行走困难和瘫痪作为重点干预的靶点，拟定科学的症状管理方案。

Objective To construct a symptom cluster network analysis model for stroke patients, explore its core symptoms and bridge symptoms, and provide a basis for precise symptom intervention and management. Methods From March to September 2024, 642 hospitalized stroke patients from a tertiary A hospital in Binzhou were selected by convenience sampling, and then surveyed with the stroke symptom cluster assessment scale. R 4.4.2 software was used to construct a network analysis of symptom cluster severity in them and analyze centrality indicators. Results The most common and severe symptom in stroke patients was difficulty in walking. In the network analysis, the strongest associations were found between dysesthesia and reduced temperature sensitivity, choking/aspiration and dysphagia, and memory decline and slow response, with regularized partial correlation coefficients of 0.60, 0.53, and 0.48, respectively. Reduced temperature sensitivity had the highest predictability value (77.7%). Difficulty in walking had the highest strength (2.104) and node expected influence (2.117), making it the core symptom of the symptom cluster. Paralysis had the highest bridge expected influence value(1.798), making it the bridge symptom of the symptom cluster. Conclusions Clinical nurses should identify strongly associated and highly predictable symptoms in a targeted manner during the intervention process to improve efficiency. Difficulty in walking and paralysis should be prioritized as key intervention targets, and scientific symptom management plans should be developed.

R473.74

山东省医药卫生科技项目(202314010490)