AI-enhanced recognition of occlusions in acute coronary syndrome (AERO-ACS): a retrospective study

Abstract

BACKGROUND: Artificial intelligence (AI) augmentation of ECG assessment has significant potential to improve patient outcomes in acute coronary syndrome. OBJECTIVE: We sought to evaluate the performance of a novel AI device (PMCardio) in assessing angiographic occlusion myocardial infarction (OMI) and predicting clinical outcomes. METHODS: We used a 1-year retrospective cohort of angiographic data from patients presenting with ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI). The device analyzed precatheterization ECGs to identify OMI, defined as a culprit vessel with thrombolysis In myocardial infarction (TIMI) 0-2 flow or TIMI 3 flow and peak cardiac troponin I > 10.0 ng/ml. RESULTS: A total of 217 patients were included: 72 STEMI (32%) and 145 NSTEMI (65%). Angiographic OMI was confirmed in 60 (83%) STEMI and 51 (35%) NSTEMI cases. The AI model achieved a sensitivity of 86.5%, specificity of 82.2%, and an area under the curve of 0.84. Traditional STEMI criteria had a sensitivity of 54.1% and a specificity of 88.7%. The AI model was 100% sensitive in detecting STEMI-OMI. The odds ratio for mortality in AI-detected OMI patients was 12.44 (1.56-98.98), unplanned readmissions 1.15 (0.53-2.51), and reduced ejection fraction at 1 year 0.24 (0.26-2.16). CONCLUSIONS: The AI model demonstrated higher sensitivity and similar specificity compared with traditional STEMI criteria, improving OMI detection while reducing false positives. These findings suggest potential benefits in triage accuracy and resource utilization, but further prospective validation is needed to determine its clinical impact.