AI-CVD-HF: A heart failure risk prediction model based on coronary artery calcium scans compared with PREVENT-HF

Background: The AI-CVD initiative seeks to extract actionable information from coronary artery calcium (CAC) scans beyond the CAC score. We aimed to develop a heart failure (HF) prediction model, AI-CVD-HF, based on AI-derived features from non-contrast CAC scans, and compare it with PREVENT-HF. Method: AI features from CAC scans of 6743 asymptomatic participants in the Multi-Ethnic Study of Atherosclerosis (MESA) and Framingham Heart Study-Offspring (FHS-O) (mean age 62.3 ± 10.1; 47.2% male; median follow-up 17.1 years; 429 HF events) were analyzed. Features were selected using random forest and modeled with random survival forest. Performance was assessed against the base PREVENT-HF using 5-fold cross-validation for area under the receiver-operating-characteristic curve (AUC), area under the precision-recall curve (AUPRC), net-benefit, and calibration. External validation experiment was conducted to assess generalizability. Results: = 0.049), and consistent performance across age, sex, and race/ethnicity. Calibration metrics favored AI-CVD-HF over PREVENT-HF (Brier score [0.0356 vs 0.0383], slope [1.02 vs 1.20]). External validation showed performance and calibration consistent with internal validation. Conclusions: AI-CVD-HF, using AI-derived features from CAC scans, demonstrated more favorable discrimination and calibration than PREVENT-HF for HF prediction, extending the utility of CAC scans beyond coronary artery disease risk assessment.