ArticleJournal of cardiovascular computed tomography
Prediction of obstructive coronary artery disease using coronary calcification and epicardial adipose tissue assessments from CT calcium scoring scans.
Article in Journal of cardiovascular computed tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparing machine learning models and traditional approaches for predicting obstructive coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Analysis of epicardial adipose tissue in relation to arterial hypertension using radiomics in photon-counting CT.Frontiers in cardiovascular medicine · 2026Article
- Sex Differences in Atherosclerotic Cardiovascular Disease Risk in Obstructive Sleep Apnea.Current atherosclerosis reports · 2025Review
- C-peptide and epicardial adipose tissue in dialysis-dependent chronic kidney disease patients.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundLow-cost/no-cost non-contrast CT calcium scoring (CTCS) exams can provide direct evidence of coronary atherosclerosis. In this study, using features from CTCS images, we developed a novel machine learning model to predict obstructive coronary artery disease (CAD), as defined by the coronary artery disease-reporting and data system (CAD-RADS).
methodsThis study analyzed 1324 patients from the SCOT-HEART trial who underwent both CTCS and CT angiography. Obstructive CAD was defined as CAD-RADS 4A-5, while CAD-RADS 0-3 were considered non-obstructive CAD. We analyzed clinical, Agatston-score-derived, and epicardial fat-omics features to predict obstructive CAD. The most predictive features were selected using elastic net logistic regression and used to train a CatBoost model. Model performance was evaluated using 1000 repeated five-fold cross-validation and survival analyses to predict major adverse cardiovascular event (MACE) and revascularization. Generalizability was assessed using an external validation set of 2316 patients for survival predictions.
resultsAmong the 1324 patients, obstructive CAD was identified in 334 patients (25.2 %). Elastic net regression identified the top 14 features (5 clinical, 2 Agatston-score-derived, and 7 fat-omics). The proposed method achieved excellent performance for classifying obstructive CAD, with an AUC of 90.1 ± 0.9 % and sensitivity/specificity/accuracy of 83.5 ± 5.5 %/93.7 ± 1.9 %/82.4 ± 2.0 %. The inclusion of Agatston-score-derived and fat-omics features significantly improved classification performance. Survival analyses showed that both actual and predicted obstructive CAD significantly differentiated patients who experienced MACE and revascularization.
conclusionsWe developed a novel machine learning model to predict obstructive CAD from non-contrast CTCS scans. Our findings highlight the potential clinical benefits of CTCS imaging in identifying patients likely to benefit from advanced imaging.
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