Abstract 4142110: Coronary Artery Calcium Scans Powered by Artificial Intelligence (AI-CAC) Predicts Atrial Fibrillation and Stroke Comparably to Cardiac Magnetic Resonance Imaging: The Multi-Ethnic Study of Atherosclerosis (MESA)

Background: Coronary artery calcium (CAC) scans contain more actionable information than the Agatston CAC score. We have previously shown in the Multi-Ethnic Study of Atherosclerosis (MESA) that AI-enabled left atrial (LA) volumetry in CAC scans (AI-CAC) enabled prediction of atrial fibrillation (AF) as early as one year. Furthermore, we have recently shown adding AI-CAC LA volumetry to CHA 2 DS 2 -VASc risk score improved stroke prediction in MESA. In this study we evaluated the performance of AI-CAC LA volumetry versus LA measured by human experts using cardiac magnetic resonance imaging (CMRI) for predicting AF and stroke, and compared them with CHARGE-AF risk score, Agatston score, and NT-proBNP. Methods: We used 15-year outcomes data from 3552 asymptomatic individuals (52.2% women, age 61.7±10.2 years) who underwent both CAC scans and CMRI in the MESA baseline examination. We have applied the AutoChamber TM (HeartLung.AI, Houston, TX) component of AI-CAC to 3552 CAC scans. CMRI LA volume was previously measured by human experts. Data on NT-proBNP, CHARGE-AF risk score and the Agatston score were obtained from MESA. Discrimination was assessed using the time-dependent area under the curve (AUC). Results: Over 15 years follow-up, 562 cases of AF and 140 cases of stroke accrued. The AUC for 15-year AF prediction by AI-CAC LA volume (0.801) was comparable to CMRI LA volume (0.797) and significantly higher than Agatston CAC Score (0.687) and NT-proBNP (0.704). Similarly, the AUC for 15-year stroke prediction for AI-CAC volumetry (0.761) was comparable to CMRI volumetry (0.751) and significantly higher than NT-proBNP (0.631) and Agatston CAC Score (0.646). AI-CAC LA volume outperformed CHARGE AF over 1-3 years for incident AF (p<0.02), but not for subsequent years. AI-CAC significantly improved the continuous Net Reclassification Index (NRI) for prediction of AF and stroke when added to CHARGE-AF risk score (0.28, 0.21), NT-proBNP (0.43, 0.37), and Agatston score (0.69, 0.41) respectively (p for all<0.0001). Conclusion: LA volumetry measured by the AutoChamber component of AI-CAC and CMRI LA volume measured by human experts similarly predicted incident AF and stroke over 15 years, and outperformed NT-proBNP and Agatston CAC Score. AI-CAC LA volumetry outperformed CHARGE-AF and NT-proBNP for short-term (1-3 years) AF prediction. Further studies to investigate the clinical utility of AI-CAC LA volumetry for AF and stroke prediction are warranted.