BACKGROUND: The 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. METHODS: We 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. RESULTS: Mean 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. CONCLUSIONS: Prediction 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.
BACKGROUND: The 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. METHODS: We 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. RESULTS: Mean 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. CONCLUSIONS: Prediction 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.
Authors: Philip D Adamson; David E Newby; C Larry Hill; Adrian Coles; Pamela S Douglas; Christopher B Fordyce Journal: JACC Cardiovasc Imaging Date: 2018-09
Authors: Casper G M J Eurlings; Sema Bektas; Sandra Sanders-van Wijk; Andrew Tsirkin; Vasily Vasilchenko; Steven J R Meex; Michael Failer; Caroline Oehri; Peter Ruff; Michael J Zellweger; Hans-Peter Brunner-La Rocca Journal: BMJ Open Date: 2022-09-26 Impact factor: 3.006
Authors: Philip D Adamson; Amanda Hunter; Debbie M Madsen; Anoop S V Shah; David A McAllister; Tania A Pawade; Michelle C Williams; Colin Berry; Nicholas A Boon; Marcus Flather; John Forbes; Scott McLean; Giles Roditi; Adam D Timmis; Edwin J R van Beek; Marc R Dweck; Hans Mickley; Nicholas L Mills; David E Newby Journal: Circ Cardiovasc Qual Outcomes Date: 2018-02