Key result
Adding aortic arch calcification to ASCVD and SCORE2 models improves significant CAD prediction by ~19%.
Why the study?
The study evaluated whether combining ASCVD and SCORE2 risk models with aortic arch calcification could identify patients at high risk for significant CAD undergoing coronary angiography.
Does adding aortic arch calcification on chest radiograph to ASCVD and SCORE2 risk scores improve the prediction of significant coronary artery disease in patients undergoing coronary angiography?
Does adding aortic arch calcification on chest radiograph to ASCVD and SCORE2 risk scores improve the prediction of significant coronary artery disease in patients undergoing coronary angiography?
Adding aortic arch calcification from routine chest radiographs to standard ASCVD and SCORE2 risk scores significantly improves the prediction of severe coronary artery disease.
May enhance CAD prediction beyond ASCVD/SCORE2 in cross-sectional data; leaves open prospective validation before practice change.
The present study evaluated 10-year atherosclerotic cardiovascular disease (ASCVD) risk using ASCVD and Systematic Coronary Risk Evaluation (SCORE2) risk models in combination with aortic arch calcification (AAC) to identify those at high risk for significant coronary artery disease (CAD) in patients undergoing coronary angiography. Of the 402 patients enrolled, 48 had normal coronary angiograms and served as group 1. The 131 patients with CAD with stenosis of <70% as group 2 and 223 patients with CAD with stenosis of ≥70% as group 3. ASCVD and SCORE2 risk scores, and the presence of AAC differed significantly among these groups. For prediction of significant CAD, the area under the curve (AUC) of ASCVD and SCORE2 risk scores in receiver operating characteristic (ROC) curve analysis were statistically similar ([AUC: .647, P < .001] and [AUC: .654, P < .001], respectively). When AAC was added to ASCVD risk and SCORE2, it increased their predictive value for significant CAD in the ROC curve analysis ( P = .003, and P = .019, respectively). In addition, significant net reclassification improvement (NRI) values were obtained by adding AAC to ASCVD and SCORE2 risk models ([NRI = .10, P = .04], and [NRI = .19, P = .04], respectively). These results suggest that the predictive value of ASCVD and SCORE2 increases when AAC is combined.
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Cetin et al. (2023) studied this question. Adding aortic arch calcification to ASCVD and SCORE2 risk models improved their predictive value for significant coronary artery disease (NRI = .19, P = .04).
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