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亚临床房颤:一种具有不确定影响的隐匿性威胁。

Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications.

作者信息

Kashou Anthony H, Adedinsewo Demilade A, Noseworthy Peter A

机构信息

Department of Medicine, Mayo Clinic, Rochester, Minnesota 55905, USA.

Department of Cardiovascular Medicine, Mayo Clinic, Jacksonville, Florida 32224, USA.

出版信息

Annu Rev Med. 2022 Jan 27;73:355-362. doi: 10.1146/annurev-med-042420-105906. Epub 2021 Nov 17.

Abstract

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.

摘要

心房颤动(AF)是最常见的心律失常之一。可植入和可穿戴心脏设备能够检测到无症状的房颤发作,即所谓的亚临床房颤(SCAF)。SCAF的患病率可能被严重低估,它与心血管疾病和全因死亡率增加以及显著的中风风险相关。机器学习的最新进展,即人工智能支持的心电图(AI-ECG),能够识别出发生SCAF可能性更高的患者。利用AI-ECG算法的功能来推动筛查方案,最终可能实现更早的检测和治疗,并有助于减轻与房颤相关的负担。

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