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

Author:

Herman Robert123ORCID,Meyers Harvey Pendell4,Smith Stephen W56,Bertolone Dario T12,Leone Attilio12,Bermpeis Konstantinos12,Viscusi Michele M12,Belmonte Marta12,Demolder Anthony3,Boza Vladimir37,Vavrik Boris3,Kresnakova Viera38,Iring Andrej3,Martonak Michal3,Bahyl Jakub3,Kisova Timea39,Schelfaut Dan2ORCID,Vanderheyden Marc2ORCID,Perl Leor10ORCID,Aslanger Emre K11,Hatala Robert12,Wojakowski Wojtek13,Bartunek Jozef2,Barbato Emanuele14ORCID

Affiliation:

1. Department of Advanced Biomedical Sciences, University of Naples Federico II , C.so Umberto I, 40, 80138 Naples , Italy

2. Cardiovascular Centre Aalst, OLV Hospital , Moorselbaan 164 , Aalst 9300, Belgium

3. Powerful Medical , Bratislavska 81/37, 931 01 Samorin , Slovakia

4. Department of Emergency Medicine, Carolinas Medical Center , Charlotte, NC , USA

5. Department of Emergency Medicine, University of Minnesota , Minneapolis, MN , USA

6. Department of Emergency Medicine, Hennepin Healthcare , Minneapolis, MN , USA

7. Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava , Bratislava , Slovakia

8. Department of Cybernetics and Artificial Intelligence, Technical University of Kosice , Kosice , Slovakia

9. Faculty of Medicine and Dentistry, Barts and The London School of Medicine and Dentistry , London , UK

10. Department of Cardiology, Rabin Medical Center , Petah Tikvah , Israel

11. Department of Cardiology, Basaksehir Cam and Sakura City Hospital , Istanbul , Turkey

12. Department of Arrhythmia and Pacing, National Institute of Cardiovascular Diseases , Bratislava , Slovakia

13. Department of Cardiology and Structural Heart Diseases, Medical University of Silesia , Katowice , Poland

14. Department of Clinical and Molecular Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome , Rome , Italy

Abstract

Abstract 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.

Funder

CardioPaTh PhD Programme

Publisher

Oxford University Press (OUP)

Subject

Energy Engineering and Power Technology,Fuel Technology

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