Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial
Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial
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DOI：
10.1038/s41591-021-01335-4
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发表时间：
2021-05-06
期刊：
影响因子：
82.9
通讯作者：
Noseworthy, Peter A.
中科院分区：
文献类型：
--
作者：
Yao, Xiaoxi;Rushlow, David R.;Noseworthy, Peter A.
我们进行了一项实用的临床试验，旨在评估基于心电图（ECG）的人工智能（AI）驱动的临床决策支持工具是否能够早期诊断低射血分数（EF），这是一种诊断不足但可治疗的疾病。在这项试验中，来自45家诊所或医院的120个初级保健团队被随机分组到干预组（获得AI结果; 181名临床医生）或对照组（常规护理; 177名临床医生）。作为常规护理的一部分，从总共22，641名既往无心力衰竭的成人（N = 11，573干预; N = 11，068对照）中获得ECG。主要结局是新诊断的低EF（
We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial (), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (