Coronary Artery Disease Risk Prediction in Patients With Severe Aortic Stenosis: Development and Validation of the Aortic Stenosis-Coronary Artery Disease (AS-CAD) Score.

Patients at a low risk of coronary artery disease (CAD) could be triaged to noninvasive coronary computed tomography angiogram instead of invasive coronary angiography, reducing health care costs and patient morbidity. Therefore, we aimed to develop a CAD risk prediction score to identify those who underwent transcatheter aortic valve implantation (TAVI) at a low risk of CAD. We enrolled 1,782 patients who underwent TAVI and randomized the patients to the derivation or validation cohort 2:1. The aortic stenosis-CAD (AS-CAD) score was developed using logistic regression, followed by separation into low- (score 0 to 5), intermediate- (6 to 10), or high-risk (>11) categories. The AS-CAD was validated initially through the k-fold cross-validation, followed by a separately held validation cohort. The average age of the cohort was 82 ± 7 years, and 41% (730 of 1,782) were female; 35% (630) had CAD. The male sex, previous percutaneous coronary intervention, stroke, peripheral arterial disease, diabetes, smoking status, left ventricular ejection fraction <50%, and right ventricular systolic pressure >35 mm Hg were all associated with an increased risk of CAD and were included in the final AS-CAD model (all p <0.03). Within the validation cohort, the AS-CAD score stratified those into low, intermediate, and high risk of CAD (p <0.001). Discrimination was good within the internal validation cohort, with a c-statistic of 0.79 (95% confidence interval 0.74 to 0.84), with similar power obtained using k-fold cross-validation (c-statistic 0.74 [95% confidence interval 0.70 to 0.77]). In conclusion, The AS-CAD score robustly identified those at a low risk of CAD in patients with severe AS. The use of AS-CAD in practice could avoid potential complications of invasive coronary angiogram by triaging low-risk patients to noninvasive coronary assessment using existing computed tomography data.