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急性脑卒中患者疲乏相关症状群特征及影响因素研究 被引量:13

Study on the characters and predictors of fatigue related symptom cluster in patients with acute stroke
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摘要 目的:研究急性脑卒中患者疲乏相关症状群的内部特征,并分析具有高症状特征的预测指标。方法:于2020年7—12月调查郑州市某三级甲等医院660例急性脑卒中患者的一般资料、疲乏、疼痛、睡眠状况、焦虑抑郁、社会支持、自我效能和生活质量状况,运用Mplus 7.4软件拟合急性脑卒中患者症状群的潜在类别并分析各潜在类别的表现特征。结果:急性脑卒中患者疲乏相关症状群可分为高症状组、心理障碍组和低症状组。可用于区分和预测高症状组的指标为低年龄段(OR=1.505,P=0.029)、较低的慢性病自我效能得分(OR=0.728,P=0.010)和较低的自我感知社会支持(OR=0.643,P=0.016)。结论:具有多重症状困扰的脑卒中患者生活质量差,后续对于急性脑卒中患者疲乏相关症状群的管理应强调提高社会支持和自我效能。 Objective:To identify the internal characteristics of fatigue related symptom cluster in patients with acute stroke,and to analyze the indicators for patients with high symptoms.Methods:A cross-sectional study was used to investigate the general information,fatigue,pain,sleep status,anxiety and depression,social support,self-efficacy,and quality of life in 660 patients with acute stroke in a tertiary hospital in Zhengzhou City,and the Mplus7.4 software was used to fit the latent class of symptom clusters.Results:The fatigue related symptom cluster of patients with acute stroke were divided into three groups as"high symptom class","high psychological disorder class"and"low symptom class"according to the symptom characteristics.Patients in the"high symptom class"were characterized by significantly younger age(OR=1.505,P=0.029),lower level of self-efficacy(OR=0.728,P=0.010)and lower level of perceived social support(OR=0.643,P=0.016).Conclusion:The quality of life in patients with high-risk symptoms needs to be improved,and how to improve social support and self-efficacy are the key intervention target to improve the management of fatigue related symptom cluster in patients with acute stroke.
作者 董小方 王敏 郭园丽 吕培华 刘延锦 DONG Xiaofang;WANG Min;GUO Yuanli;LYU Peihua;LIU Yanjin(Neurology Department,the First Affiliated Hospital of Zhengzhou University,Zhengzhou,450052,China)
出处 《中国护理管理》 CSCD 2021年第10期1470-1475,共6页 Chinese Nursing Management
基金 2022年度河南省高等学校重点科研项目计划。
关键词 脑卒中 症状管理 潜在类别分析 预测指标 stroke symptom management Latent Class Analysis predictors
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