小柯机器人

美国梅奥诊所心血管内科Xiaoxi Yao研究组取得最新进展。他们进行了一项实用的随机临床试验，发现具有人工智能（AI）功能的心电图（ECG），可识别出低射血分数（EF）的患者。相关论文于2021年5月7日发表于国际顶尖学术期刊《自然-医学》杂志上。

他们进行了一实用临床试验，旨在评估基于ECG，AI的临床决策支持工具能否对低EF进行早期诊断，这种疾病被诊断不足但可以治疗。在该试验中（NCT04000087），将来自45个诊所或医院的120个初级护理团队按簇随机分配到干预部门（获得AI结果的181位临床医生）或对照组（普通护理； 177位临床医生）。从例行护理中共从22,641名成年人中获得了心电图（N = 11573例干预； N = 11068例对照），而没有心力衰竭。

主要结果是在心电图90天内重新诊断出低EF（≤50％）。该试验达到了预先设定的主要终点，表明该干预增加了对整个队列中低EF的诊断（对照组为1.6％，干预组为2.1％，优势比（OR）为1.32（1.01-1.61），P = 0.007），并且在那些被确认具有极低EF可能性的人中（即AI-ECG阳性，占整个队列的6％）（对照组为14.5％，干预组为19.5％，或1.43） （1.08–1.91），P = 0.01）。

在整个队列中，两组的超声心动图利用率相似（对照组为18.2％，干预组为19.2％，P = 0.17）。对于AI-ECG阳性的患者，与对照组相比，干预组获得的超声心动图更多（对照组为38.1％，干预组为49.6％，P <0.001）。这些结果表明，使用基于ECG的AI算法可以在常规初级保健环境中早期诊断出患者的EF低。

附：英文原文

Title: Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial

Author: Xiaoxi Yao, David R. Rushlow, Jonathan W. Inselman, Rozalina G. McCoy, Thomas D. Thacher, Emma M. Behnken, Matthew E. Bernard, Steven L. Rosas, Abdulla Akfaly, Artika Misra, Paul E. Molling, Joseph S. Krien, Randy M. Foss, Barbara A. Barry, Konstantinos C. Siontis, Suraj Kapa, Patricia A. Pellikka, Francisco Lopez-Jimenez, Zachi I. Attia, Nilay D. Shah, Paul A. Friedman, Peter A. Noseworthy

Issue&Volume: 2021-05-06

Abstract: 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 (NCT04000087), 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 (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01–1.61), P=0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08–1.91), P=0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P=0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P<0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.

DOI: 10.1038/s41591-021-01335-4

Source: https://www.nature.com/articles/s41591-021-01335-4

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