Observational studyEuropean heart journal2020
Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry.
Observational study in European heart journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01443637 (COronary CT Angiography Evaluation For Clinical Outcomes), which is not on this map. Cited by 121 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
COronary CT Angiography Evaluation For Clinical Outcomes: An International Multicenter Registry
Who cites it
121 citing papers in PubMed, 5 syntheses or guidelines pooled it, 234 citations in OpenAlex.
- 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
- Application of artificial intelligence in non-invasive cardiovascular imaging for coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Pooled it
- Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.BMC medical informatics and decision making · 2020Pooled it
- Pooled it
- Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial.Open heart · 2025Trial
- Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis.Frontiers in cellular and infection microbiology · 2022Trial
- Are risk factors necessary for pretest probability assessment of coronary artery disease? A patient similarity network analysis of the PROMISE trial.Journal of cardiovascular computed tomographyTrial
- Article
- Translating machine learning predictions into meaningful risk estimates to support clinical decisions: a post hoc analysis of chronic obstructive pulmonary disease adverse outcomes using unified auto clinical scores.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Application Progress of Machine Learning in Prognostic Prediction of Percutaneous Coronary Intervention: A Systematic Review.Journal of clinical medicine research · 2026Review
- Detection of obstructive coronary artery disease using a deep learning and machine learning ensemble: a retrospective feasibility study.European heart journal. Digital health · 2026Article
- Development and Validation of Artificial Intelligence Prediction of Epicardial Coronary Artery Spasm in Patients Without Obstructive Coronary Artery Disease.Diagnostics (Basel, Switzerland) · 2026Article
- Plasma proteomic signatures of early retinal neurodegeneration in diabetes: A multi-cohort study.PLoS medicine · 2026Observational
- Development and Validation of a Coronary Computed Tomography Angiography-Based Radiomics-Integrated Model for Noninvasive Detection of Left Atrial Appendage Thrombus in Atrial Fibrillation.Journal of the American Heart Association · 2026Article
- A narrative review on the use of artificial intelligence in cardiovascular medicine.Cardiovascular diagnosis and therapy · 2026Review
- A machine learning model for predicting adverse prognostic events in patients with neurosyphilis: Results from the DEFEAT-NS study.iScience · 2026Article
- Artificial Intelligence-Driven Hypertension Management: Implications for Quality Improvement and Prevention of End-Organ Damage.Life (Basel, Switzerland) · 2026Review
- A preprocessing-enhanced stacking classifier for generalized cardiovascular disease detection across diverse datasets.Scientific reports · 2026Article
- Integrating conformal prediction with machine learning for uncertainty-aware risk stratification in coronary artery disease.Frontiers in cardiovascular medicine · 2026Article
61 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
42 authors at 20 institutions in 10 countries.
Funding
Abstract
aimsSymptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA). METHODS AND
resultsThe study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features.
conclusionA ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.