Heart Failure Outcome Prediction Using Artificial Intelligence-enabled Coronary Artery Calcium CT Chamber Volumetry Compared to American Heart Association PREVENT and the Agatston Score for Heart Failure Risk Stratification.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology and Imaging Sciences, Emory University, 101 Woodruff Cir, Ste 308A, Atlanta, GA 30322.
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ.
- School of Nursing, Emory University, Atlanta, Ga.
- Department of Medicine, Division of Cardiology, Emory University, Atlanta, Ga.
Abstract
Purpose To evaluate whether artificial intelligence (AI)-derived chamber volumetry from coronary artery calcium (CAC) CT improves heart failure (HF) risk prediction. Materials and Methods This retrospective study included asymptomatic patients without known cardiac disease undergoing CAC CT between 2010 and 2023. CAC CT images were analyzed using a validated AI model to calculate chamber volumes. HF events were identified based on <i>International Classification of Diseases, Ninth and Tenth Revisions</i> codes. Cox proportional hazard regression models incorporating chamber volumes, adjusted for Predicting Risk of cardiovascular disease EVENTs-Heart Failure (PREVENT-HF) score, were used to assess the association with HF. Time-dependent areas under the receiver operating characteristic curve (AUCs) at 3, 5, 8, and 10 years were calculated to compare the performance of volumetry, PREVENT-HF, CAC scoring, and their combination. Results A total of 5892 patients were included (mean age ± SD, 58.2 years ± 9.4; 3258 male). During a mean follow-up of 4 years ± 3, 377 patients (6.3%) developed HF. Larger left atrial, left ventricular, right atrial, and left ventricular myocardial volumes were associated with increased HF risk (<i>P</i> < .001). The composite, multivariable Cox regression model incorporating all chamber volumes, CAC score, and PREVENT-HF scores outperformed PREVENT-HF (AUC, 0.80 vs 0.76; ΔAUC, 0.04; <i>P</i> < .001) and CAC scores (AUC, 0.80 vs 0.70; ΔAUC, 0.10; <i>P</i> < .001) alone in predicting 10-year HF risk. A phase-volume interaction effect was identified, indicating that diastolic volumes were independently associated with higher HF risk than were systolic volumes (<i>P</i> < .001), adjusted for PREVENT-HF. Conclusion AI-derived cardiac chamber volumetry obtained from CAC CT improved HF risk prediction compared with PREVENT-HF or CAC scores alone. <b>Keywords:</b> Applications-CT, Deep Learning, Cardiac <i>Supplemental material is available for this article.</i> © RSNA, 2026.