Abstract
AbstractIntroductionCoronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC for prediction of incident heart failure (HF) and compared it with 10 known clinical risk factors, NT-proBNP, and the Agatston CAC score.MethodsWe applied AI-CAC to 5750 CAC scans of asymptomatic individuals (ages 45-84, 52% women, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We then used the 15-year outcomes data and compared the C-statistic of AI-CAC with NT-proBNP and the Agatston score for predicting incident HF versus 10 known clinical risk factors (age, gender, body surface area, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL, total cholesterol, and hs-CRP).ResultsOver 15 years of follow-up, 256 HF events accrued. The ROC area under the curve for predicting HF with AI-CAC (0.826) was significantly higher than NT-proBNP (0.742) and Agatston score (0.712) (p<.0001), and comparable to clinical risk factors (0.818, p=0.4141). AI-CAC category-free NRI significantly improved on clinical risk factors (0.43), NT-proBNP (0.68), and Agatston score (0.71) for HF prediction at 15 years (p<0.0001).ConclusionAI-CAC significantly outperformed NT-proBNP and the Agatston CAC score and significantly improved category-free NRI of clinical risk factors for incident HF prediction.
Publisher
Cold Spring Harbor Laboratory