中华护理杂志 ›› 2020, Vol. 55 ›› Issue (8): 1189-1196.DOI: 10.3761/j.issn.0254-1769.2020.08.015

• 专科实践与研究 • 上一篇    下一篇

心房颤动患者症状群及其影响因素研究

葛红玥(),林梅(),许志英   

  1. 300052 天津市 天津医科大学总医院护理部
  • 收稿日期:2019-08-20 出版日期:2020-08-15 发布日期:2020-08-06
  • 通讯作者: 林梅
  • 作者简介:葛红玥:女,本科(硕士在读),E-mail: <email>982312343@qq.com</email>

Analysis of characteristics of patients with atrial fibrillation distributed by different symptom clusters

GE Hongyue(),LIN Mei(),XU Zhiying   

  • Received:2019-08-20 Online:2020-08-15 Published:2020-08-06
  • Contact: Mei LIN

摘要:

目的 探讨心房颤动患者的症状群特征,并分析影响症状群的主要因素。方法 采用社会人口学及临床特征问卷、多伦多心房颤动严重程度量表的症状分量表对天津市某三级甲等综合医院心律失常门诊的203例心房颤动患者进行横断面调查,采用系统聚类分析提取症状群,并通过卡方检验和Kruskal-Wallis H秩和检验分析社会人口学因素及疾病特征对症状群分布情况的影响。结果 该调查共回收有效问卷197份。心房颤动患者存在3个症状群:心脏症状群(心悸、胸痛)、疲劳症状群(休息时呼吸困难、疲乏、眩晕)和运动症状群(活动时呼吸困难、运动无耐力)。性别、心房颤动类型、BMI、血栓危险度评分、合并高血压、冠心病、心力衰竭是影响心房颤动患者症状群分布的主要因素。结论 护士应加强女性、永久性心房颤动、合并疾病的心房颤动患者的症状识别,提供症状管理措施,改善疾病预后。

关键词: 心房颤动, 症状群, 症状管理, 聚类分析, 护理

Abstract:

Objective To identify the composition of symptom cluster in patients with atrial fibrillation and to explore the differences in the distribution of symptom clusters on sociodemographic variables and disease characteristics. Methods A cross-sectional survey was conducted in 197 patients with atrial fibrillation in the arrhythmia clinic of a tertiary general hospital in Tianjin,using the general information questionnaire and the symptom subscale of the University of Toronto AF Severity Scale(AFSS) as data collection instruments. Symptom clusters were extracted by systematic cluster analysis,and the effects of sociodemographic variables and disease characteristics on the distribution of symptom clusters were determined by chi-square test,and Kruskal-Wallis H rank sum test. Results There were 3 symptom clusters in atrial fibrillation patients,including cardiac symptom cluster(palpitation,chest pain),fatigue symptom cluster(dyspnea at rest,fatigue,vertigo) and exertional symptom cluster(dyspnea during activity,exercise intolerance). Gender,type of atrial fibrillation,body mass index,CHA2DS2-VASC score,combined hypertension,coronary heart disease,and heart failure were the main factors affecting the composition of the symptom clusters. Conclusion Symptom clusters can be helpful for patients with atrial fibrillation to detect symptoms early and seek medical help in time,as well as to provide theoretical support for clinical staff to implement more accurate and comprehensive symptom management for atrial fibrillation patients.

Key words: Atrial Fibrillation, Symptom Cluster, Symptom Management, Cluster Analysis, Nursing Care