Streur Megan M, Ratcliffe Sarah J, Callans David J, Shoemaker M Benjamin, Riegel Barbara J
School of Nursing, University of Washington, Seattle, WA, USA.
Department of Biostatistics & Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Pacing Clin Electrophysiol. 2018 Jul;41(7):741-749. doi: 10.1111/pace.13356. Epub 2018 May 15.
Symptoms drive healthcare use among adults with atrial fibrillation, but limited data are available regarding which symptoms are most problematic and which patients are most at-risk. The purpose of this study was to: (1) identify clusters of patients with similar symptom profiles, (2) characterize the individuals within each cluster, and (3) determine whether specific symptom profiles are associated with healthcare utilization.
We conducted a cross-sectional secondary data analysis of 1,501 adults from the Vanderbilt Atrial Fibrillation Registry. Participants were recruited from Vanderbilt cardiology clinics, emergency department, and in-patient services. Subjects included in our analysis had clinically verified atrial fibrillation and a completed symptom survey. Symptom and healthcare utilization data were collected with the University of Toronto Atrial Fibrillation Severity Scale. Latent class regression analysis was used to identify symptom clusters, with clinical and demographic variables included as covariates. We used Poisson regression to examine the association between latent class membership and healthcare utilization.
Participants were predominantly male (67%) with a mean age of 58.4 years (±11.9). Four latent classes were evident, including an Asymptomatic cluster (N = 487, 38%), Highly Symptomatic cluster (N = 142, 11%), With Activity cluster (N = 326, 25%), and Mild Diffuse cluster (N = 336, 26%). Highly Symptomatic membership was associated with the greatest rate of emergency department visits and hospitalizations (incident rate ratio 2.4, P < 0.001).
Clinically meaningful atrial fibrillation symptom profiles were identified that were associated with increased rates of emergency department visits and hospitalizations.
症状促使患有心房颤动的成年人寻求医疗服务，但关于哪些症状问题最大以及哪些患者风险最高的数据有限。本研究的目的是：（1）识别具有相似症状特征的患者群体，（2）描述每个群体中的个体特征，以及（3）确定特定的症状特征是否与医疗服务利用相关。
我们对范德比尔特心房颤动登记处的1501名成年人进行了横断面二次数据分析。参与者从范德比尔特心脏病诊所、急诊科和住院服务部门招募。纳入我们分析的受试者临床确诊为心房颤动且完成了症状调查。症状和医疗服务利用数据通过多伦多大学心房颤动严重程度量表收集。使用潜在类别回归分析来识别症状群，将临床和人口统计学变量作为协变量纳入。我们使用泊松回归来检验潜在类别成员与医疗服务利用之间的关联。
参与者主要为男性（67%），平均年龄58.4岁（±11.9）。明显存在四个潜在类别，包括无症状群（N = 487，38%）、高度症状群（N = 142，11%）、活动相关群（N = 326，25%）和轻度弥漫群（N = 336，26%）。高度症状群成员的急诊科就诊率和住院率最高（发病率比2.4，P < 0.001）。
识别出了具有临床意义的心房颤动症状特征，这些特征与急诊科就诊率和住院率的增加相关。
