缺血性卒中患者心房颤动的表型决定因素：一项回顾性研究
本研究为荷兰Radboud大学医学中心2019年1月至2024年10月间的回顾性队列研究，纳入1836例缺血性卒中患者，中位年龄73岁，44.1%为女性。结果显示30.5%的患者合并心房颤动，其中10.8%为卒中后新检出。房颤相关因素包括年龄、卒中严重程度、既往卒中、心力衰竭、二尖瓣反流等，并通过机器学习识别出高危表型（房颤患病率47.9%）。
要点
- 缺血性卒中患者中房颤总患病率30.5%，新检出占10.8%。
- 机器学习识别高危表型，房颤患病率达47.9%。
- 重点筛查高龄、重症卒中、心脏负荷重及左房重构患者。
核心结论
- 缺血性卒中患者中房颤患病率较高（30.5%），其中10.8%为卒中后新检出，房颤相关因素包括年龄增长、卒中严重程度、既往缺血性卒中、心力衰竭、二尖瓣反流、左房容积增大及E波峰值流速升高，提示针对高危表型的定向筛查可能优化监测策略。
研究设计与样本
- 本回顾性队列研究纳入荷兰Radboud大学医学中心2019年1月至2024年10月期间所有缺血性卒中患者，共1836例（中位年龄73岁，IQR 64–80，44.1%为女性），采用描述性统计、logistic回归分析和生成拓扑映射（GTM）概率机器学习方法进行聚类和数据可视化。
主要发现
- 研究中30.5%的患者有房颤，其中19.7%为已知房颤，10.8%为卒中后新检出。
- 与房颤最显著相关的因素包括年龄较大、卒中严重程度增加、既往缺血性卒中史、心力衰竭、二尖瓣反流、左房容积增大及E波峰值流速升高。
- GTM识别出一表型特征为年龄较大、卒中严重程度高、既往缺血性卒中史、高血压、糖尿病、心脏负荷重（心肌梗死史、心脏瓣膜异常、心力衰竭）、肾功能受损、房性及室性早搏负荷高、左房重构明显，该表型房颤患病率高达47.9%。
关键数值与推荐
- 房颤患病率30.5%，已知房颤19.7%，新检出10.8%。
- GTM识别的高危表型房颤患病率为47.9%。
- 相关因素包括年龄较大、卒中严重程度增加、既往缺血性卒中、心力衰竭、二尖瓣反流、左房容积增大、E波峰值流速升高。
临床怎么用
- 建议对高龄、卒中严重程度高、既往卒中史、心脏负荷重及左房重构的患者加强房颤监测，可能有助于提高卒中二级预防的房颤检出率。
局限性
- 本研究为单中心回顾性研究，可能存在选择偏倚和信息偏倚。
- 未提供房颤检测方法、监测持续时间及房颤定义的具体细节。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
原文
Background Atrial fibrillation (AF) is a common cause of ischaemic stroke and a major global health burden, but is often undetected in routine clinical practice. Detection of AF is essential for secondary stroke prevention, but it remains unclear which patients who had a stroke are at highest risk of previously unrecognised or newly detected AF, limiting targeted detection strategies. The aim of this study was to determine the prevalence of AF and risk factors that are associated with AF among patients who had an ischaemic stroke. Methods In this retrospective cohort study, all patients who had an ischaemic stroke at the Radboud University Medical Center (Nijmegen, the Netherlands) from January 2019 to October 2024 were included. We applied descriptive statistics, logistic regression analyses and generative topographic mapping (GTM), a probabilistic machine learning method for clustering and data visualisation. Results Of 1836 patients (median age 73 years (IQR 64–80), 44.1% female), 30.5% had AF, of which 19.7% were previously known and 10.8% were newly detected after stroke. The most significant factors associated with AF were older age, increased stroke severity, history of ischaemic stroke and heart failure, mitral regurgitation, increased left atrial volume and elevated peak E wave velocity. GTM identified a phenotype characterised by older age, severe strokes, high prevalence of history of ischaemic stroke, hypertension, diabetes, high cardiac burden with history of myocardial infarction, heart valve abnormalities and heart failure, impaired kidney function, high premature atrial and ventricular contraction burden, and signs of severe left atrial remodelling to be most associated with AF (prevalence 47.9%). Conclusions The prevalence of AF and stroke severity among AF patients in this study highlights the relevance of optimising monitoring strategies. Stroke severity, history of ischaemic stroke, high cardiac burden and left atrial remodelling signs were most strongly associated with AF. These findings suggest potential areas for targeted screening in high-risk clinical phenotypes.
引用
- Open Heart：Phenotypic determinants of atrial fibrillation in ischaemic stroke: a retrospective study
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。