Can an artificial intelligence electrocardiogram algorithm improve diagnostic accuracy for acute coronary occlusion
Brandon S Friedman1, Rosa Malloy-Post1, Stephen W Smith2
1Department of Emergency Medicine, Carolinas Medical Center, 1000 Blythe Blvd, Charlotte, NC 28203, United States of America.
Insights
An artificial intelligence (AI) algorithm significantly improved the detection of acute coronary occlusion myocardial infarction (OMI) in canceled ST-segment elevation myocardial infarction (STEMI) activations. This AI tool shows promise for enhancing diagnostic accuracy and inter-team reliability in identifying OMI.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- ST-segment elevation myocardial infarction (STEMI) and acute coronary occlusion myocardial infarction (OMI) are diagnosed via electrocardiogram (ECG).
- Discrepancies in ECG interpretation are common between Emergency Medicine and Cardiology.
Purpose of the Study:
- To evaluate an artificial intelligence (AI) algorithm's effectiveness in improving diagnostic accuracy for OMI.
- Focus on challenging cases of canceled STEMI activations.
Main Methods:
- Retrospective review of 17 months of STEMI activations.
- Included canceled STEMI activations with ECGs not meeting STEMI criteria.
- Defined OMI as angiographic culprit lesion with TIMI 0-1 flow; assessed ECGs with AI and standard STEMI criteria.
Main Results:
- 185 canceled STEMI activations were reviewed, with 17 meeting OMI criteria.
- AI algorithm showed significantly higher sensitivity (94.1%) for OMI than STEMI criteria (47.1%, p=0.005).
- AI demonstrated higher positive (3.51) and negative (0.08) likelihood ratios for OMI detection.
Conclusions:
- The AI algorithm may enhance interrater reliability between Emergency Medicine and Cardiology for OMI identification.
- AI can serve as a valuable adjunct in diagnosing OMI, particularly in ambiguous cases.
- Further prospective studies are recommended to validate AI utility in clinical practice.
Background:
ST-segment elevation myocardial infarction (STEMI) and its equivalents describe the electrocardiogram (ECG) findings of acute coronary occlusion myocardial infarction (OMI). Discordance in ECG interpretation between Emergency Medicine and Cardiology teams is common.
Objectives:
We examined the utility of an artificial intelligence (AI) algorithm to improve diagnostic accuracy for OMI in the difficult subset of canceled STEMI activations.
Methods:
We conducted a retrospective review of STEMI activations over 17 months. We included cases that were canceled with the rationale of "ECG not meeting STEMI criteria." We excluded sustained activations, cancellations with alternative rationales, and incomplete records. OMI was defined as an angiographic culprit lesion with TIMI 0 or 1 flow. ECGs were reviewed by the AI algorithm and assessed for STEMI criteria.
Results:
Of 1224 STEMI activations, 185 cancellations (15.1%) were included, with 17 patients meeting the study definition of OMI. STEMI criteria demonstrated lower sensitivity for OMI as compared to the AI algorithm (47.1% vs 94.1%, p = 0.005), and a non-significantly lower specificity (66.1% vs 73.2%, p = 0.090). The AI algorithm also demonstrated higher positive and negative likelihood ratios for OMI identification (3.51 and 0.08, respectively) than STEMI criteria (1.39 and 0.80, respectively).
Conclusions:
Our data suggests that the AI algorithm may serve as a clinical adjunct to improve interrater reliability between Emergency Medicine and Cardiology teams in OMI identification. Further prospective studies may help evaluate its utility in clinical practice.
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