Comparison of Coronary Artery Disease Consortium 1 and 2 Scores and Duke Clinical Score to Predict Obstructive Coronary Disease by Invasive Coronary Angiography

Abstract

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.

Comparison of International Guidelines for Assessment of Suspected Stable Angina

“…Whereas a Bayesian probabilistic approach to patient selection has many theoretical advantages, our results point toward a key limitation in this strategy. Namely, that despite revisions, risk models continue to both over- and underestimate disease prevalence 9 , 33 , 34 , 35 , 36 , 37 , 38 when applied in settings external to the derivation cohort. Given it is the presence of symptoms that identifies patients with suspected angina, it would appear to follow that it is the nature of these symptoms that should inform diagnostic decisions.…”

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

“…Although ESC guidelines recommend UDFM as the model to estimate PTP of obstructive CAD, it revealed significantly overestimates in several external validation studies completed in CCTA-based cohorts [ 7 – 9 ]. To address this shortcoming, the medical history-based CONFIRM score was developed from an international cohort of patients undergoing CCTA [ 10 ].…”

“…Updated Diamond-Forrester method (UDFM), a traditional age, sex and chest pain typicality-based approach to the PTP of obstructive CAD on invasive coronary angiography [ 6 ], is currently recommended by the European Society of Cardiology (ESC) [ 4 ]. However, several studies determined that UDFM seemed to overestimate the PTP of obstructive CAD, especially in low risk populations [ 7 – 9 ].…”

High-Sensitivity Cardiac Troponin I and the Diagnosis of Coronary Artery Disease in Patients With Suspected Angina Pectoris

“…3-5 However, there is evidence both that these models may overestimate risk [6][7][8] and that clinician use of stratification tools remains suboptimal. 9,10 In light of these challenges, there is widespread interest in identifying suitable biomarkers that may improve diagnostic accuracy in patients with suspected stable CAD.…”

“…2 Several national and international bodies have proposed standardized pathways that use risk models to estimate the pretest probability (PTP) of obstructive CAD and guide decision making with regards to appropriate use of investigations. [3][4][5] However, there is evidence both that these models may overestimate risk [6][7][8] and that clinician use of stratification tools remains suboptimal. 9,10 In light of these challenges, there is widespread interest in identifying suitable biomarkers that may improve diagnostic accuracy in patients with suspected stable CAD.…”