AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CACTM) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis

Morteza Naghavi; Anthony Reeves; Matthew Budoff; Dong Li; Kyle Atlas; Chenyu Zhang; Thomas Atlas; Sion K Roy; Claudia I Henschke; Nathan D Wong; Christopher Defilippi; Daniel Levy; David F Yankelevitz

doi:10.1016/j.jcct.2024.04.006

AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CAC^TM) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis

J Cardiovasc Comput Tomogr. 2024 Apr 24:S1934-5925(24)00080-7. doi: 10.1016/j.jcct.2024.04.006. Online ahead of print.

Authors

Affiliations

¹ HeartLung.AI, Houston, TX, USA. Electronic address: mn@vp.org.
² Department of Computer Engineering, Cornell University, Ithaca, NY, USA.
³ The Lundquist Institute, Torrance, CA, USA.
⁴ HeartLung.AI, Houston, TX, USA.
⁵ Tustin Teleradiology, Tustin, CA, USA.
⁶ Mount Sinai Hospital, New York, NY, USA.
⁷ Heart Disease Prevention Program, Division of Cardiology, University of California Irvine, CA, USA.
⁸ Inova Heart and Vascular Institute, Falls Church, VA, USA.
⁹ National Institutes of Health, Bethesda, MD, USA.

PMID: 38664073
DOI: 10.1016/j.jcct.2024.04.006

Abstract

Introduction: Coronary 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 cardiac chambers for prediction of incident heart failure (HF).

Methods: We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.

Results: Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p < 0.0001), and comparable to clinical risk factors (0.85, p = 0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p < 0.0001).

Conclusion: AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.

Keywords: Artificial intelligence; Coronary artery calcium; Heart failure; Left ventricular volume; NT-proBNP.