International evaluation of an artificial intelligence-powered ecg model detecting acute coronary occlusion myocardial infarction

R. Herman, H. P. Meyers, Stephen W Smith, D. Bertolone, A. Leone, Konstantinos Bermpeis, M. M. Viscusi, M. Belmonte, A. Demolder, V. Boza, B. Vavrik, V. Kresnakova, Andrej Iring, M. Martonak, Jakub Bahyl, Timea Kisova, D. Schelfaut, M. Vanderheyden, L. Perl, Emre Aslanger, R. Hatala, Wojtek Wojakowski, J. Bartunek, Emanuele Barbato
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Abstract

Majority of acute coronary syndromes (ACS) present without typical ST-elevation. One third of Non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery (occlusion myocardial infarction [OMI]), leading to poor outcomes due to delayed identification and invasive management. We sought to develop a versatile artificial intelligence (AI)-model detecting acute OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art diagnostic criteria. An AI model was developed using 18,616 ECGs from 10,543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. Primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3,254 ECGs from 2,222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve (AUC) of 0.938 (95% CI: 0.924-0.951) in identifying the primary OMI outcome, with superior performance (accuracy 90.9% [95% CI: 89.7-92.0], sensitivity 80.6% [95% CI: 76.8-84.0], specificity 93.7 [95% CI: 92.6-94.8]) compared to STEMI criteria (accuracy 83.6% [95% CI: 82.1-85.1], sensitivity 32.5% [95% CI: 28.4-36.6], specificity 97.7% [95% CI: 97.0-98.3]) and similar performance compared to ECG experts (accuracy 90.8% [95% CI: 89.5-91.9], sensitivity 73.0% [95% CI: 68.7-77.0], specificity 95.7% [95% CI: 94.7-96.6]). The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared to the STEMI criteria. This suggests its potential to improve ACS triage ensuring appropriate and timely referral for immediate revascularization.
检测急性冠状动脉闭塞性心肌梗死的人工智能驱动心电图模型的国际评估
大多数急性冠状动脉综合征(ACS)没有典型的ST段抬高。三分之一的非 ST 段抬高型心肌梗死(NSTEMI)患者的冠状动脉有急性闭塞(闭塞性心肌梗死 [OMI]),由于延迟识别和有创治疗,导致预后不良。我们试图开发一种多功能人工智能(AI)模型,在单张标准 12 导联心电图(ECG)上检测急性 OMI,并将其性能与现有的最先进诊断标准进行比较。 我们利用一个国际数据库中 10,543 名疑似 ACS 患者的 18,616 张心电图开发了一个人工智能模型,并对其结果进行了临床验证。该模型在国际队列中进行了评估,并在检测 OMI 方面与 STEMI 标准和心电图专家进行了比较。OMI 的主要结果是需要紧急血管重建的急性闭塞或血流受限的罪魁祸首动脉。 在来自 2,222 名患者(年龄 62 ± 14 岁,67% 为男性,21.6% 为 OMI)的 3,254 张心电图的总体测试集中,人工智能模型在识别主要 OMI 结果方面的曲线下面积(AUC)为 0.938(95% CI:0.924-0.951),性能优越(准确性 90.9% [95% CI:89.7-92.0],灵敏度 80.6% [95% CI:76.8-84.0],特异性 93.7 [95% CI:92.0])。7[95%CI:92.6-94.8])相比(准确率 83.6% [95% CI:82.1-85.1],灵敏度 32.5% [95% CI:28.4-36.6],特异性 97.7% [95% CI:97.0-98.3]),表现相似。3]),与心电图专家的表现相似(准确率 90.8% [95% CI: 89.5-91.9],灵敏度 73.0% [95% CI: 68.7-77.0],特异性 95.7% [95% CI: 94.7-96.6])。 与 STEMI 标准相比,本新型心电图 AI 模型检测急性 OMI 的准确性更高。这表明该模型具有改善 ACS 分诊的潜力,可确保适当、及时地转诊以立即进行血管重建。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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