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

被引:25
作者
Herman, Robert [1 ,2 ,3 ]
Meyers, Harvey Pendell [4 ]
Smith, Stephen W. [5 ,6 ]
Bertolone, Dario T. [1 ,2 ]
Leone, Attilio [1 ,2 ]
Bermpeis, Konstantinos [1 ,2 ]
Viscusi, Michele M. [1 ,2 ]
Belmonte, Marta [1 ,2 ]
Demolder, Anthony [3 ]
Boza, Vladimir [3 ,7 ]
Vavrik, Boris [3 ]
Kresnakova, Viera [3 ,8 ]
Iring, Andrej [3 ]
Martonak, Michal [3 ]
Bahyl, Jakub [3 ]
Kisova, Timea [3 ,9 ]
Schelfaut, Dan [2 ]
Vanderheyden, Marc [2 ]
Perl, Leor [10 ]
Aslanger, Emre K. [11 ]
Hatala, Robert [12 ]
Wojakowski, Wojtek [13 ]
Bartunek, Jozef [2 ]
Barbato, Emanuele [14 ]
机构
[1] Univ Naples Federico II, Dept Adv Biomed Sci, Cso Umberto I 40, I-80138 Naples, Italy
[2] Onze Lieve Vrouw Hosp, Cardiovasc Ctr Aalst, Moorselbaan 164, B-9300 Aalst, Belgium
[3] Powerful Med, Bratislavska 81-37, Samorin 93101, Slovakia
[4] Carolinas Med Ctr, Dept Emergency Med, Charlotte, NC USA
[5] Univ Minnesota, Dept Emergency Med, Minneapolis, MN USA
[6] Hennepin Healthcare, Dept Emergency Med, Minneapolis, MN USA
[7] Comenius Univ, Fac Math Phys & Informat, Bratislava, Slovakia
[8] Tech Univ Kosice, Dept Cybernet & Artificial Intelligence, Kosice, Slovakia
[9] Barts & London Queen Marys Sch Med & Dent, Fac Med & Dent, London, England
[10] Rabin Med Ctr, Dept Cardiol, Petah Tiqwa, Israel
[11] Basaksehir Cam & Sakura City Hosp, Dept Cardiol, Istanbul, Turkiye
[12] Natl Inst Cardiovasc Dis, Dept Arrhythmia & Pacing, Bratislava, Slovakia
[13] Med Univ Silesia, Dept Cardiol & Struct Heart Dis, Katowice, Poland
[14] Sapienza Univ Rome, Fac Med & Psychol, Dept Clin & Mol Med, Rome, Italy
来源
EUROPEAN HEART JOURNAL - DIGITAL HEALTH | 2024年 / 5卷 / 02期
关键词
Electrocardiogram; Artificial intelligence; Acute coronary syndrome; Myocardial infarction; Occlusion myocardial infarction; NSTEMI; ST-SEGMENT ELEVATION; BUNDLE-BRANCH BLOCK; LOGISTIC-REGRESSION; EMERGENCY; INTERVENTION; VALIDATION; CARDIOLOGY; DIAGNOSIS; SOCIETY; ARTERY;
D O I
10.1093/ehjdh/ztad074
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Aims A 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. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria. Methods and results 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. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 +/- 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (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), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)]. Conclusion The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.
引用
收藏
页码:123 / 133
页数:11
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