Stroke · 2026
AI-Derived LA Volume Index, LA/RA and LA/LV Volume Ratios From Coronary Artery Calcium Scans Predict Long-Term Atrial Fibrillation and Stroke.
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Azimi Amir, Atlas Kyle, Reeves Anthony P, Zhang Chenyu, Wasserthal Jakob, Mirjalili Seyed Reza, Mozafarybazargany Mohammadhossein, Hashemi Ali, Atlas Thomas, Henschke Claudia I, Yankelevitz David F, Zulueta Javier J, Branch Andrea D, Roy Sion K, Nasir Khurram, Molloi Sabee, Rana Jamal S, Fayad Zahi A, McConnell Michael V, Abela George S, Vliegenthart Rozemarijn, Maron David J, Narula Jagat, Williams Kim A, Shah Prediman K, Budoff Matthew J, Benjamin Emelia J, Mehran Roxana, Kloner Robert A, Wong Nathan D, Naghavi Morteza
Аннотация
BACKGROUND: The AI-CVD initiative aims to extract actionable insights from coronary artery calcium (CAC) scans beyond the traditional CAC score. Although AI-derived cardiac chamber volumes predict atrial fibrillation (AF) and stroke, the long-term prognostic value of chamber ratios is less established. We evaluated the predictive value of AI-derived left atrial volume index and related chamber ratios (left atrial [LA]/right atrial [RA], LA/left ventricular) from routine CAC scans for incident AF and stroke, and their incremental value beyond established risk scores.
METHODS: Pooled participant-level data from 2 prospective cohorts, the MESA (Multi-Ethnic Study of Atherosclerosis, 2000-2002, n=5670) and the FHS (Framingham Heart Study Offspring cohort, 1998-2001, n=1142), were analyzed. Primary outcomes were incident AF and incident stroke. AI-enabled volumetry (AutoChamber, AI-CVD platform) quantified cardiac chamber metrics from noncontrast CAC scans. Cox proportional hazards models, net reclassification improvement, time-dependent area under the curve, calibration metrics, and least absolute shrinkage and selection operator regression were applied to evaluate predictive performance.
RESULTS: Over a median 17-year follow-up, 1302 participants developed AF, and 365 experienced stroke events. Individuals in the ≥95th percentile of chamber metrics had a significantly increased risk. Adjusted hazard ratios for AF were 2.66 (95% CI, 2.23-3.17) for left atrial volume index, 2.04 (95% CI, 1.71-2.45) for LA/left ventricular (LV) ratio, and 1.87 (95% CI, 1.55-2.26) for LA/RA ratio. For stroke, corresponding hazard ratios were 1.96 (95% CI, 1.38-2.77), 1.64 (95% CI, 1.15-2.33), and 1.83 (95% CI, 1.29-2.59), respectively. AI-derived metrics improved reclassification beyond Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation risk score and Framingham Stroke Risk Profile, with greatest improvements for AF from left atrial volume index (net reclassification improvement, 0.48) and stroke from LA/RA ratio (net reclassification improvement, 0.39), driven mainly by nonevent classification. Although discrimination improvements (area under the curve ) were modest, chamber measurements substantially improved Framingham Stroke Risk Profile calibration (slope, 0.448 to 0.834-0.902). Among all chamber metrics (including volumes and ratios), the least absolute shrinkage and selection operator identified left atrial volume index as the strongest predictor for AF, and LA/RA ratio as the strongest for stroke.
CONCLUSIONS: AI-enabled left atrial volumetric and ratio-based metrics derived opportunistically from CAC scans provide incremental predictive value for AF and stroke prediction.
METHODS: Pooled participant-level data from 2 prospective cohorts, the MESA (Multi-Ethnic Study of Atherosclerosis, 2000-2002, n=5670) and the FHS (Framingham Heart Study Offspring cohort, 1998-2001, n=1142), were analyzed. Primary outcomes were incident AF and incident stroke. AI-enabled volumetry (AutoChamber, AI-CVD platform) quantified cardiac chamber metrics from noncontrast CAC scans. Cox proportional hazards models, net reclassification improvement, time-dependent area under the curve, calibration metrics, and least absolute shrinkage and selection operator regression were applied to evaluate predictive performance.
RESULTS: Over a median 17-year follow-up, 1302 participants developed AF, and 365 experienced stroke events. Individuals in the ≥95th percentile of chamber metrics had a significantly increased risk. Adjusted hazard ratios for AF were 2.66 (95% CI, 2.23-3.17) for left atrial volume index, 2.04 (95% CI, 1.71-2.45) for LA/left ventricular (LV) ratio, and 1.87 (95% CI, 1.55-2.26) for LA/RA ratio. For stroke, corresponding hazard ratios were 1.96 (95% CI, 1.38-2.77), 1.64 (95% CI, 1.15-2.33), and 1.83 (95% CI, 1.29-2.59), respectively. AI-derived metrics improved reclassification beyond Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation risk score and Framingham Stroke Risk Profile, with greatest improvements for AF from left atrial volume index (net reclassification improvement, 0.48) and stroke from LA/RA ratio (net reclassification improvement, 0.39), driven mainly by nonevent classification. Although discrimination improvements (area under the curve ) were modest, chamber measurements substantially improved Framingham Stroke Risk Profile calibration (slope, 0.448 to 0.834-0.902). Among all chamber metrics (including volumes and ratios), the least absolute shrinkage and selection operator identified left atrial volume index as the strongest predictor for AF, and LA/RA ratio as the strongest for stroke.
CONCLUSIONS: AI-enabled left atrial volumetric and ratio-based metrics derived opportunistically from CAC scans provide incremental predictive value for AF and stroke prediction.