Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction
Year: 2024
Type: article
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.
Source: npj Digital Medicine
Authors: 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
Institutions: Cornell University, Laboratory for Research on Enterprise and Decisions, Mount Sinai Hospital, The Lundquist Institute, Houston Methodist, University of California, Irvine, Stanford Medicine, The University of Texas Health Science Center at Houston, University of Louisville, Cedars-Sinai Medical Center, National Institutes of Health, National Heart, Lung, and Blood Institute, National Heart, Lung, and Blood Institute Division of Intramural Research
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,790
Awards: G1261793039, G1269631540, G1276754516, G1294553593, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY-FIELD CENTER, G1409423729, G1412221515, G1446319095, G1516086731, G1518934888, G1522144118, G1539384734, G1558725192, G1640378483, G1686417863, G1740741465, G17630474, G1803853571, G1983464145, SUBCLINICAL CARDIOVASCULAR DISEASE COORDINATING CENTER, G2095942735, G2144938473, G22458031, G2262782284, G2580996367, G2726417417, G2769513335, G2779429302, G2837638605, G290884426, G291913881, G296458788, G3093995152, G3161396180, G3252259982, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTER, G3570589137, G3595232572, G3647016462, G3676546685, G3687594596, G370977179, G3990748671, G399481048, G4036023366, G4086764833, G4118980254, G4186273061, G4309390892, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY-ULTRASOUND, G4512137732, G4563812999, G4590187187, G4986157935, G5075046117, G5088122703, G5108002584, G514410172, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTER, G5653954166, G5654179670, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTER, G5787256936, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY, G6095866717, G6359107525, Institute for Clinical and Translational Research, G6434471666, G6514094751, G66341962, G6742279884, G6798586005, G6856766826, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY, G7166352866, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTER, G7462069030, G78770644, G7924587209, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--EBCT READING, Clinical and Translational Science Award, G8145863542, G818308888, G842756795, SUBCLINICAL CARDIOVASCULAR DISEASE STUDY-FIELD CENTER, G8761005068, G8852914737, G9000004549, G9000004784, G9000006353, G9000017360, G9000019818, G9000025517, G9000038858, G9000063950, G9000065698, G9000136152, G94668252, G991334795
OpenAlex ID: W4404060993