Coronary Artery Calcium Scans Powered by Artificial Intelligence (AI-CAC) Predicts Atrial Fibrillation and Stroke Comparably to Cardiac Magnetic Resonance Imaging:

Nov 27, 2024

2 min read

The Multi-Ethnic Study of Atherosclerosis (MESA)

AUTHORS (FIRST NAME, LAST NAME):

Morteza Naghavi, Anthony P. Reeves, Kyle Atlas, Chenyu Zhang, Dong Li, Thomas Atlas, Claudia Henschke, Nathan D. Wong, Sion K. Roy, Matthew J. Budoff, and David Yankelevitz.

a. HeartLung.AI, 2450 Holcombe, Houston, TX, 77021

b. Department of Electrical and Computer Engineering, Cornell University, 616 Thurston Ave. Ithaca, NY 14853

c. The Lundquist Institute, 1124 W Carson St, Torrance, CA 90502

d. Tustin Teleradiology, 13422 Newport Ave Suite I, Tustin, CA 92780

e. Mount Sinai Hospital, 1468 Madison Ave, New York, NY 10029

f. Heart Disease Prevention Program, Division of Cardiology, University of California, Irvine, CA

Abstract:

Background:

AI-CAC provides more actionable information than the Agatston coronary artery calcium (CAC) score. We have recently shown in the Multi-Ethnic Study of Atherosclerosis (MESA) that AI-CAC automated left atrial (LA) volumetry enabled prediction of atrial fibrillation (AF) as early as one year. In this study we evaluated the performance of AI-CAC LA volumetry versus LA measured by human experts using cardiac magnetic resonance imaging (CMRI) for predicting AF and stroke, and compared them with CHARGE-AF risk score, Agatston score, and NT-proBNP.

Methods:

We used 15-year outcomes data from 3552 asymptomatic individuals (52.2% women, age 61.7±10.2 years) who underwent both CAC scans and CMRI in the MESA baseline examination. CMRI LA volume was previously measured by human experts. Data on BNP, CHARGE-AF risk score and the Agatston score were obtained from MESA. Discrimination was assessed using the time-dependent area under the curve (AUC).

Results:

Over 15 years follow-up, 562 cases of AF and 140 cases of stroke accrued. The AUC for AI-CAC versus CMRI for AF and stroke were not significantly different (0.802 vs. 0.798 and 0.762 vs. 0.751 respectively, p=0.60). AI-CAC significantly improved the continuous Net Reclassification Index (NRI) for prediction of AF and stroke when added to CHARGE-AF risk score (0.28, 0.21), NT-proBNP (0.43, 0.37), and Agatston score (0.69, 0.41) respectively (p for all<0.0001).

Conclusion:

AI-CAC automated LA volumetry and CMRI LA volume measured by human experts similarly predicted incident AF and stroke over 15 years. Further studies to investigate the clinical utility of AI-CAC for AF and stroke prediction are warranted.

Keywords:

Left atrial volume, coronary artery calcium, cardiac magnetic resonance imaging, atrial fibrillation, artificial intelligence, stroke.