The Multi-Ethnic Study of Atherosclerosis (MESA)
AUTHORS (FIRST NAME, LAST NAME):
Morteza Naghavi, MD, Anthony P. Reeves, PhD, Kyle Atlas, BS, Chenyu Zhang, MS, Thomas Atlas, MD, Claudia Henschke, PhD., MD, David Yankelevitz, MD, Matthew J. Budoff, MD, Dong Li, PhD, Sion K. Roy, MD, Khurram Nasir, M.D, Sabee Molloi, PhD, Zahi Fayad, PhD, Michael V. McConnell, MD, MSEE, Ioannis Kakadiaris, MD, David J. Maron, MD, Jagat Narula, MD, Kim Williams, MD, Daniel Levy, M.D, and Nathan D. Wong, PhD.
a. HeartLung.AI, Houston, TX, 77021
b. Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853
c. Tustin Teleradiology, Tustin, CA 92780
d. Mount Sinai Hospital, New York, NY 10029
e. The Lundquist Institute, CA 90502
f. Houston Methodist Hospital, Houston, TX 77030
g. Department of Radiology, University of California Irvine, CA 92697
h. Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA, 94305
i. The University of Texas Health Science Center at Houston, TX, 77030
j. University of Louisville, Louisville, KY
k. Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, 20824
l. Heart Disease Prevention Program, Division of Cardiology, University of California Irvine, CA 92697
Abstract:
Background:
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) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths.
Methods:
We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score.
Results:
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. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342).
Conclusion:
In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.
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