目的 通过网络分析探索急性脑卒中患者疲劳相关症状群的网络特征，识别症状网络中的核心症状和桥梁症状，进而发掘症状干预的关键靶点。 方法 本研究为横断面研究设计，采用便利抽样法选取2023年12月至2024年5月在银川市某三级甲等医院神经内科住院的580例脑卒中患者为调查对象，采用一般资料调查表、疲劳严重度量表、医院焦虑抑郁量表、阿森斯失眠量表、疼痛数字评分表进行调查，构建症状网络并分析中心性指标。 结果 患者疲劳严重度量表得分为（3.35±1.02）分，阿森斯失眠量表得分4.00（3.00,7.00）分，医院焦虑抑郁量表抑郁亚量表得分为6.00（4.00,7.75）分。症状网络分析显示，各症状之间相互关联，“感到容易疲劳”的强度中心性、紧密中心性和预期影响均最高，“睡眠时间”的桥梁强度最高。经检验，网络的准确性和稳定性较好，网络模型较为可靠。 结论 本研究运用网络分析探究急性脑卒中患者疲劳症状群的网络结构，其中疲劳为核心症状，睡眠障碍为桥梁症状，建议未来研究以疲劳和睡眠障碍为干预靶点，实施精准管理，提升症状管理效率。

Objective To explore the network characteristics of fatigue-related symptom clusters in patients with acute stroke through network analysis.Identify core and bridge symptoms within the symptom network,and then explore key targets for symptom intervention.Methods A cross-sectional study recruited 580 stroke patients from a tertiary A hospital’s neurology department in Yinchuan from December 2023 to May 2024 using convenience sampling.Data included general information questionnaire,Fatigue Severity Scale (FSS),Hospital Anxiety and Depression Scale (HADS),Athens Insomnia Scale(AIS）,and Numerical Rating Scale (NRS).Symptom networks and centrality indices were analyzed.Results The FSS score was (3.35±1.02) points,the AIS was 4.00(3.00,7.00) points, and the HADS-D score was 6.00(4.00,7.75) points.Network analysis revealed symptom interconnections,with “feeling easily fatigued” having highest strength,closeness and expected influence.“Sleep duration” showed highest bridging strength.The accuracy and stability of the network were validated,and the network model is reliable.Conclusions The study used network analysis to explore the network structure of fatigue-related symptom clusters in patients with acute stroke,in which fatigue is the core symptom and sleep disorder is the bridge symptom.Future research should take fatigue and sleep disorder as intervention targets to implement accurate management and improve the efficiency of symptom management.

R473.74

2024年宁夏医科大学护理学院一流学科孵育项目(NYHLYB202401)