AI-enabled Cardiac Chambers Volumetry and Calcified Plaque Characterization in Coronary Artery Calcium (CAC) Scans (AI-CAC) Significantly Improves on Agatston CAC Score for Predicting All Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis.

Autor:	Naghavi M; HeartLung.AI., Reeves A; Cornell University., Atlas K; HeartLung.AI., Zhang C; HeartLung.AI., Atlas T; Tustin Teleradiology., Henschke C; Mount Sinai Hospital., Yankelevitz D; Mount Sinai Hospital., Budoff M; The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrace, CA., Li D; The Lundquist Institute., Roy S; The Lundquist Institute., Nasir K; Houston Methodist DeBakey Heart & Vascular Center., Narula J; UTHealth Houston., Kakadiaris I; The University of Texas Health Science Center at Houston., Molloi S; Department of Radiology, University of California Irvine., Fayad Z; Icahn School of Medicine at Mount Sinai., Maron D; Stanford University., McConnell M; Stanford., Williams K; University of Louisville., Levy D; National Heart Lung and Blood Institute., Wong N; University of California at Irvine, Irvine.
Jazyk:	angličtina
Zdroj:	Research square [Res Sq] 2024 Jun 20. Date of Electronic Publication: 2024 Jun 20.
DOI:	10.21203/rs.3.rs-4433105/v1
Abstrakt:	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.
Databáze:	MEDLINE
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