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Improved Detection of Acute Coronary Occlusion Myocardial Infarction by an Artificial Intelligence Electrocardiogram
Thomas Lindow1,2, Axel Nyström3, Jakob Lundager Forberg4,5
1Respiratory Medicine, Allergology, and Palliative Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
Insights
The Queen of Hearts (QoH) AI model significantly improves detection of occlusion myocardial infarction (OMI) in chest pain patients. QoH shows higher sensitivity and positive predictive value than STEMI criteria, aiding faster diagnosis.
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Emergency Medicine
Background:
- Occlusion myocardial infarction (OMI) detection is critical in emergency departments (EDs).
- Current ST-elevation myocardial infarction (STEMI) criteria have limitations in diagnosing OMI.
- The Queen of Hearts (QoH) artificial intelligence (AI) model shows promise for improved OMI detection.
Purpose of the Study:
- To evaluate the diagnostic performance of the QoH AI model for OMI detection.
- To compare QoH's performance against conventional STEMI criteria and other algorithms in chest pain patients.
- To validate QoH in a real-world Swedish emergency department setting.
Main Methods:
- Retrospective analysis of 24,511 consecutive chest pain patients from the ESC-TROP study (2017-2018).
- OMI classification based on angiographic data and expert adjudication.
- Comparison of QoH, conventional STEMI, extended STEMI criteria, and Glasgow ECG Analysis Algorithm performance.
Main Results:
- 467 patients (1.9%) had OMI.
- QoH demonstrated significantly higher sensitivity (52% vs. 23%) and positive predictive value (51% vs. 17%) compared to STEMI criteria.
- QoH achieved similar specificity (99%) and negative predictive value (99%) as STEMI criteria.
Conclusions:
- The QoH AI model significantly improves OMI detection sensitivity in emergency department patients with chest pain.
- QoH offers comparable specificity to existing criteria, making it a valuable tool for OMI diagnosis.
- Further validation supports QoH as a potentially superior AI-driven ECG analysis tool for acute cardiac events.
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.
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