Improved Detection of Acute Coronary Occlusion Myocardial Infarction by an Artificial Intelligence Electrocardiogram Model in Swedish Emergency Departments

Abstract

Objectives The Queen of Hearts (QoH) ECG artificial intelligence model has demonstrated improved sensitivity for detecting occlusion myocardial infarction (OMI) compared with STEMI criteria, but further validation is needed. We aimed to evaluate QoH's diagnostic performance in patients with chest pain at Swedish emergency departments (EDs). Methods This retrospective analysis included consecutive patients with chest pain at the ED from the ESC-TROP study (2017-2018). Patients transferred directly from the prehospital setting to the coronary care unit were not included. OMI classification was based on angiographic data and expert adjudication. QoH, conventional STEMI criteria, and the Glasgow ECG Analysis Algorithm were applied to all cases. In addition, extended STEMI criteria incorporating additional ECG leads (-V1, -V2, -V3, -aVL, -aVR, and -III) and OMI criteria for left bundle branch block (modified Sgarbossa criteria) and left ventricular hypertrophy (ST elevation V1-V3 ≥0.25 of R and S) were applied. Results Among 24,511 patients (mean age 59 ± 19 years, 52% male), 467 (1.9%) had OMI. QoH achieved higher sensitivity than STEMI criteria (52% [47 to 57] vs 23% [19 to 27]), similar specificity (99% [99 to 99] vs 98% [98 to 98]), higher positive predictive value (51% [47 to 54] vs 17% [15 to 20]), and similar negative predictive value (99% [99 to 99] vs 98 [98 to 99]). The Glasgow algorithm obtained 32% (28 to 37) sensitivity, 98% (98 to 98) specificity, 26% (23 to 30) PPV, and 99% (99 to 99) NPV, and corresponding number for the extended criteria were 41% (36 to 45), 95% (95 to 96), 14% (12 to 15), and 99% (99 to 99). Conclusion In ED patients with chest pain, QoH improved sensitivity in OMI detection compared with currently available ECG criteria, with similar specificity.