AI-CVD: Artificial Intelligence-Enabled Opportunistic Screening of Coronary Artery Calcium ComputedTomography Scans for Predicting CVD Events and All-Cause Mortality:

The Multi-Ethnic Study of Atherosclerosis

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, Wenjun Fan, MD, PhD, Ruilin Yu, MPH, Andrea Branch, MD, Ning Ma, PhD, Rowena Yip, PhD, Sion K. Roy, MD, Khurram Nasir, M.D, Sabee Molloi, PhD, Zahi Fayad, PhD, Michael V. McConnell, MD, MSEE, Ioannis Kakadiaris, MD, Javier Zuelueta, MD, David J. Maron, MD, Jagat Narula, MD, PhD, Prediman Shah, 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, Torrance, CA 90502

f.      Houston Methodist Hospital, Houston, TX 77030

g.     Department of Radiology, University of California Irvine, CA 92697

h.     Department of Medicine, Stanford University 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

m.   Department of Epidemiology & Biostatistics, University of California Irvine, CA 92697

n.     Department of Epidemiology, University of California, Los Angeles, CA 90095

o.     Cedars-Sinai Medical Center, Los Angeles, CA, 90048

Abstract:

Background:

The AI-CVD initiative aims to extract all useful opportunistic screening information from coronary artery calcium (CAC) scans and combines them with traditional risk factors to create a stronger predictor of cardiovascular diseases (CVD). These measurements include cardiac chambers volumes (left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV), and left ventricular mass (LVM)), aortic wall and valvular calcification, aorta and pulmonary artery volumes, torso visceral fat, emphysema score, thoracic bone mineral density, and fatty liver score. We have previously reported that the automated cardiac chambers volumetry component of AI-CVD predicts incident atrial fibrillation (AF), heart failure (HF), and stroke in the Multi-Ethnic Study of Atherosclerosis (MESA). In this report, we examine the contribution of other AI-CVD components for all coronary heart disease (CHD), AF, HF, stroke plus transient ischemic attack (TIA), all-CVD, and all-cause mortality.

Methods:

We applied AI-CVD to CAC scans of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at MESA baseline examination. We used 10-year outcomes data and assessed hazard ratios for AI-CVD components plus CAC score and known CVD risk factors (age, sex, diabetes, smoking, LDL-C, HDL-C, systolic and diastolic blood pressure, hypertension medication). AI-CVD predictors were modeled per standard deviation (SD) increase using Cox proportional hazards regression.

Results:

Over 10 years of follow-up, 1058 CVD (550 AF, 198 HF, 163 stroke, 389 CHD) and 628 all-cause mortality events accrued with some cases having multiple events. Among AI-CVD components, CAC score and chamber volumes were the strongest predictors of different outcomes. Expectedly, age was the strongest predictor for all outcomes except HF where LV volume and LV mass were stronger predictors than age. Figure 1 shows contribution of each predictor for various outcomes.

Conclusion:

AI-enabled opportunistic screening of useful information in CAC scans contributes substantially to CVD and total mortality prediction independently of CAC score and CVD risk factors. Further studies are warranted to evaluate the clinical utility of AI-CVD.

Figure 1. Chi-Square contribution of each significant AI-CVD predictor for multiple CVD outcomes

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