心房颤动
医学
内科学
心房扑动
队列
心脏病学
人口
前瞻性队列研究
队列研究
纤颤
环境卫生
作者
Nikolas Nozica,Anna Lam,Eleni Goulouti,Elena Elchinova,Alessandro Spirito,Mattia Branca,Helge Servatius,Fabian Noti,Jens Seiler,Samuel H. Baldinger,Andreas Haeberlin,Stefano F. de Marchi,Babken Asatryan,Nicolas Rodondi,Jacques Donzé,Drahomir Aujesky,Hildegard Tanner,Tobias Reichlin,Peter Jüni,Laurent Roten
出处
期刊:Schweizerische Medizinische Wochenschrift
日期:2021-02-20
卷期号:151 (0708): w20421-w20421
被引量:3
标识
DOI:10.4414/smw.2021.20421
摘要
Anticoagulation of patients with screen-detected atrial fibrillation may prevent ischaemic strokes. The STAR-FIB study programme aims to determine the age- and sex-specific prevalence of silent atrial fibrillation and to develop a clinical prediction model to identify patients at risk of undiagnosed atrial fibrillation in a hospitalised patient population.The STAR-FIB study programme includes a prospective cohort study and a case-control study of hospitalised patients aged 65–84 years, evenly distributed for both age and sex. We recruited 795 patients without atrial fibrillation for the cohort study (49.2% females; median age 74.8 years). All patients had three serial 7-day Holter ECGs to screen for silent atrial fibrillation. The primary endpoint will be any episode of atrial fibrillation or atrial flutter of ≥30 seconds duration. The age- and sex-specific prevalence of newly diagnosed atrial fibrillation will be estimated. For the case-control study, 120 patients with paroxysmal atrial fibrillation were recruited as cases (41.7% females; median age 74.6 years); controls will be randomly selected from the cohort study in a 2:1 ratio. All participants in the cohort study and all cases were prospectively evaluated including clinical, laboratory, echocardiographic and electrical parameters. A clinical prediction model for undiagnosed atrial fibrillation will be derived in the case-control study and externally validated in the cohort study.The STAR-FIB study programme will estimate the age- and sex-specific prevalence of silent atrial fibrillation in a hospitalised patient population, and develop and validate a clinical prediction model to identify patients at risk of silent atrial fibrillation.
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