Abstract 4139994: Deep-learning Assisted ECG-based Emergent Cathlab Activation: First Prospective Implementation of a Smartphone-based System

Introduction: Standard ST-segment elevation myocardial infarction (STEMI) pathways misidentify up to 50% of patients with an acutely occluded culprit coronary artery (OMI) with false positive emergent cathlab activations in up to 35% of cases. Recently, an artificial intelligence (AI) electrocardiogram (ECG) model outperformed standard of care in detecting OMI in international retrospective cohorts. Hypothesis: We sought to prospectively evaluate the rule-in performance of an AI ECG model in a large tertiary care STEMI network compared to ECG machine readings of STEMI and AI-assisted emergency physicians. Methods: An AI model trained to detect acute coronary occlusion regardless of ST elevation was implemented using smartphones in a tertiary care STEMI network consisting of 1 hub hospital, 1 spoke center, and 2 emergency medical service crews (EMS) (Fig 1A). Outcomes of all patients presenting with atraumatic chest pain during a 10-week period between January and March 2024 were adjudicated using ECG, laboratory, and angiographic chart review and classified based on the presence of OMI. Results: A total of 731 consecutive patients (68% male) with atraumatic chest pain were included; 142 patients were hospitalized of whom 23 (16%) met the primary outcome of OMI. The AI model showed a significantly higher sensitivity detecting OMI as compared to ECG machine (95.7% vs. 47.8%, p<0.001, Fig 1B) at comparable specificity (95% vs. 96%, respectively) with overall superior predictive accuracy (Chi-squared=8.1; p=0.004). The AI model correctly identified 15 cases that the ECG machine misclassified (80% were false negatives by ECG machine). All AI false positives were patients post recent myocardial infarction or angioplasty. Sensitivity of AI-assisted emergency physicians interpreting OMI was 78.2% indicating potential instances of AI underutilization. Conclusions: This first prospective performance evaluation in a large all-comer atraumatic chest pain cohort indicates high accuracy of unbiased, AI-powered ECG detecting acute coronary occlusion. The findings suggest its potential to improve ACS patient outcomes through timely referral for invasive management in a real-world clinical setting.