ArticleClinical cardiology2016
Comparison of Coronary Artery Disease Consortium 1 and 2 Scores and Duke Clinical Score to Predict Obstructive Coronary Disease by Invasive Coronary Angiography.
Article in Clinical cardiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Prediction of coronary artery disease using retinal optical coherence tomography angiography imaging and electronic health records: a multimodal machine learning approach.BMC cardiovascular disorders · 2026Article
- Development and validation of a deep-learning algorithm for rule-in and rule-out coronary artery disease based on electrocardiogram without evidence of myocardial ischemia.International journal of cardiology. Heart & vasculature · 2025Article
- Machine learning approaches that use clinical, laboratory, and electrocardiogram data enhance the prediction of obstructive coronary artery disease.Scientific reports · 2023Article
- Article
- Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom.PloS one · 2022Article
- Pretest models for predicting abnormal stress single-photon emission computed tomography myocardial perfusion imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- Impact of sex-specific differences in calculating the pretest probability of obstructive coronary artery disease in symptomatic patients: a coronary computed tomographic angiography study.Coronary artery disease · 2019Article
- Comparison of International Guidelines for Assessment of Suspected Stable Angina: Insights From the PROMISE and SCOT-HEART.JACC. Cardiovascular imaging · 2018Article
- Coronary calcium score improves the estimation for pretest probability of obstructive coronary artery disease and avoids unnecessary testing in individuals at low extreme of traditional risk factor burden: validation and comparison of CONFIRM score and genders extended model.BMC cardiovascular disorders · 2018Observational
- New Algorithm for the Prediction of Cardiovascular Risk in Symptomatic Adults with Stable Chest Pain.Current cardiology reports · 2018Review
- High-Sensitivity Cardiac Troponin I and the Diagnosis of Coronary Artery Disease in Patients With Suspected Angina Pectoris.Circulation. Cardiovascular quality and outcomes · 2018Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe first step in evaluating a patient with suspected stable coronary artery disease (CAD) is the determination of the pretest probability. The European Society of Cardiology guidelines recommend the use of the CAD Consortium 1 score (CAD1), which contrary to CAD Consortium 2 (CAD2) score and Duke Clinical Score (DCS), does not include modifiable cardiovascular risk factors. HYPOTHESIS: Using scores that include modifiable risk factors (DCS and CAD2) enhances prediction of CAD.
methodsWe retrospectively included all patients referred to invasive coronary angiography for suspected CAD from January/2008-December/2012 (N = 2234). Pretest probability was calculated using 3 models (CAD1, DCS, and CAD2), and they were compared using the net reclassification improvement.
resultsMean patient age was 63.7 years, 67.5% were male, and the majority (66.9%) had typical angina. Coronary artery disease was diagnosed in 58.5%, and the area under the curve was 0.685 for DCS, 0.664 for CAD1, and 0.683 for CAD2, with a statistically significant difference between CAD1 and the others (P < 0.001). The net reclassification improvement was 20% for DCS, related to adequate reclassification of 32% of patients with CAD to a higher risk category, and 5% for CAD2, at the cost of adequate reclassification of 34% of patients without CAD to a lower risk category.
conclusionsPrediction of CAD using scores that include modifiable cardiovascular risk factors seems to improve accuracy. Our results suggest that, in high-prevalence populations, DCS may better identify patients at higher risk and CAD2 those at lower risk for CAD.
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