1. AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CAC TM ) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis.
- Author
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Naghavi M, Reeves A, Budoff M, Li D, Atlas K, Zhang C, Atlas T, Roy SK, Henschke CI, Wong ND, Defilippi C, Levy D, and Yankelevitz DF
- Subjects
- Humans, Female, Male, Aged, Middle Aged, Risk Factors, Risk Assessment, Prognosis, United States, Time Factors, Incidence, Aged, 80 and over, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Multidetector Computed Tomography, Asymptomatic Diseases, Peptide Fragments blood, Natriuretic Peptide, Brain blood, Heart Failure ethnology, Heart Failure diagnostic imaging, Predictive Value of Tests, Coronary Artery Disease diagnostic imaging, Coronary Artery Disease ethnology, Biomarkers blood, Vascular Calcification diagnostic imaging, Vascular Calcification ethnology, Computed Tomography Angiography, Artificial Intelligence, Coronary Angiography
- Abstract
Introduction: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF)., Methods: We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years., Results: Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p < 0.0001), and comparable to clinical risk factors (0.85, p = 0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p < 0.0001)., Conclusion: AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2024
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