Coronary artery calcium scans powered by artificial intelligence (AI-CAC) predicts atrial fibrillation and stroke comparably to cardiac magnetic resonance imaging: MESA

Abstract Background AI-CAC provides more actionable information than the Agatston coronary artery calcium (CAC) score. We have recently shown in the Multi-Ethnic Study of Atherosclerosis (MESA) that AI-CAC automated left atrial (LA) volumetry enabled prediction of atrial fibrillation (AF) as early as one year. 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. CMRI LA volume was previously measured by human experts. Data on BNP, 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 AI-CAC versus CMRI for AF and stroke were not significantly different (0.802 vs. 0.798 and 0.762 vs. 0.751 respectively, p=0.60). 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 AI-CAC automated LA volumetry and CMRI LA volume measured by human experts similarly predicted incident AF and stroke over 15 years. Further studies to investigate the clinical utility of AI-CAC for AF and stroke prediction are warranted.