Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction

Morteza Naghavi; Anthony P Reeves; Kyle Atlas; Chenyu Zhang; Thomas Atlas; Claudia I Henschke; David F Yankelevitz; Matthew J Budoff; Dong Li; Sion K Roy; Khurram Nasir; Sabee Molloi; Zahi Fayad; Michael V McConnell; Ioannis Kakadiaris; David J Maron; Jagat Narula; Kim Williams; Prediman K Shah; Daniel Levy; Nathan D Wong

doi:10.1038/s41746-024-01308-0

Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction

NPJ Digit Med. 2024 Nov 5;7(1):309. doi: 10.1038/s41746-024-01308-0.

Authors

Morteza Naghavi¹, Anthony P Reeves², Kyle Atlas³, Chenyu Zhang³, Thomas Atlas⁴, Claudia I Henschke⁵, David F Yankelevitz⁵, Matthew J Budoff⁶, Dong Li⁶, Sion K Roy⁶, Khurram Nasir⁷, Sabee Molloi⁸, Zahi Fayad⁷, Michael V McConnell⁹, Ioannis Kakadiaris¹⁰, David J Maron⁹, Jagat Narula¹⁰, Kim Williams¹¹, Prediman K Shah¹², Daniel Levy¹³, Nathan D Wong¹⁴

Affiliations

¹ HeartLung.AI, Houston, TX, 77021, USA. mn@vp.org.
² Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, 14853, USA.
³ HeartLung.AI, Houston, TX, 77021, USA.
⁴ Tustin Teleradiology, Tustin, CA, 92780, USA.
⁵ Mount Sinai Hospital, New York, NY, 10029, USA.
⁶ The Lundquist Institute, Torrance, CA, 90502, USA.
⁷ Houston Methodist Hospital, Houston, TX, 77030, USA.
⁸ Department of Radiology, University of California Irvine, Irvine, CA, 92697, USA.
⁹ Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA, 94305, USA.
¹⁰ The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
¹¹ University of Louisville, Louisville, KY, 40292, USA.
¹² Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
¹³ Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20824, USA.
¹⁴ Heart Disease Prevention Program, Division of Cardiology, University of California Irvine, Irvine, CA, 92697, USA.

Abstract

Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) applied to CAC scans can predict non-CHD events, including heart failure, atrial fibrillation, and stroke. We applied AI-enabled automated cardiac chambers volumetry and calcified plaque characterization to CAC scans (AI-CAC) of 5830 asymptomatic individuals (52.2% women, age 61.7 ± 10.2 years) in the multi-ethnic study of atherosclerosis during 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow-up for AI-CAC vs. Agatston score was (0.784 vs. 0.701), (0.771 vs. 0.709), (0.789 vs. 0.712) and (0.816 vs. 0.729) (p < 0.0001 for all), respectively. AI-CAC plaque characteristics, including number, location, density, plus number of vessels, significantly improved CHD prediction in the CAC 1-100 cohort vs. Agatston Score. AI-CAC significantly improved the Agatston score for predicting all CVD events.

Abstract

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