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心力衰竭患者住院前症状群的研究 被引量:27

Analyzing of heart failure symptom clusters before hospitalization to indicate patients get timly treatment
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摘要 目的探讨对心功能失代偿起到预测作用的症状和症状群,以提示患者及时就医。方法对参与"心力衰竭患者症状经历的相关研究"的200例住院患者的数据进行二次分析,采用中文版Memorial心力衰竭症状评估量表,评估患者住院前1周症状的发生情况及其严重程度。结果心力衰竭患者在住院前发生频率最高的5个症状分别为气短(85.5%)、疲乏(74.0%)、出汗(71.0%)、口干(64.0%)、平躺时呼吸困难(62.8%)。运用主成分分析对发生率≥30%的症状(18个)进行分析,根据症状的严重程度得出6个症状群,分别为消化-排泄症状群(食欲缺乏、腹胀、排尿异常、恶心),淤血症状群(气短、夜间睡眠憋醒、水肿),缺血症状群(头晕、胸痛),抑制状态症状群(疲乏、出汗、易困),兴奋状态症状群(口干、心悸、易激惹),休息相关症状群(平躺时呼吸困难、咳嗽、睡眠困难),方差贡献率分别为17.3%、9.7%、7.6%、7.3%、6.6%、5.9%,Cronbach'sα系数分别为0.545、0.427、0.499、0.414、0.394、0.344。结论认识心力衰竭患者住院前的症状群,可帮助患者尽早意识到病情的恶化,及时就医,从而使其得到及时的救治。 Objective To explore heart failure symptoms and symptom clusters among in-hospitalized heart failure patients. Methods The data of 200 in-hospitalized patients recruited in the "study of symptom experience in patients with heart failure" was re-analyzed in this research. Symptoms happened in a week before hospitalization and the severity of these symptoms were assessed by the Chinese version of Memorial Symptom Assessment Scale-Heart Failure. Results Before hospitalization,the most prevalent symptoms happened on HF patients were shortness of breath,lack of energy,sweats,dry mouth and difficulty breathing when lying flat. 18 symptoms were included in the principal component analysis,and 6 symptom clusters had been found: the digestion and excretion cluster,the congestion cluster,the ischemia cluster,the active cluster,the relaxation-related cluster. The variation contribution rates of each cluster were 17.3%,9.7%,7.6%,7.3%,6.6%,5.9%,and the Cronbach's α coefficients of each cluster were 0.545,0.427,0.499,0.414,0.394,0.344 respectively. Conclusion Patients with heart failure would experience different symptoms at the same time. HF symptom clusters can be used to help patients realize the deterioration of the disease,and to get timely treatments.
出处 《中华护理杂志》 CSCD 北大核心 2015年第3期292-295,共4页 Chinese Journal of Nursing
关键词 心力衰竭 体征和症状 主成分分析 Heart Failure Signs and Symptoms Principal Component Analysis
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