Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach

Evaluation of resting myocardial computed tomography perfusion (ctp) by coronary ct angiography (ccta) might serve as a useful addition for determining coronary artery disease. we aimed to evaluate the incremental benefit of resting ctp over coronary stenosis for predicting ischemia using a computational algorithm trained by machine learning methods. 252 patients underwent ccta and invasive fractional flow reserve (ffr). ct stenosis was classified as 0%, 1-30%, 31-49%, 50-70%, and >70% maximal stenosis. significant ischemia was defined as invasive ffr < 0.80. resting ctp analysis was performed using a gradient boosting classifier for supervised machine learning. on a per-patient basis, accuracy, sensitivity, specificity, positive predictive, and negative predictive values according to resting ctp when added to ct stenosis (>70%) for predicting ischemia were 68.3%, 52.7%, 84.6%, 78.2%, and 63.0%, respectively. compared with ct stenosis [area under the receiver operating characteristic curve (auc): 0.68, 95% confidence interval (ci) 0.62-0.74], the addition of resting ctp appeared to improve discrimination (auc: 0.75, 95% ci 0.69-0.81, p value .001) and reclassification (net reclassification improvement: 0.52, p value < .001) of ischemia. the addition of resting ctp analysis acquired from machine learning techniques improve the predictive utility of significant ischemia over coronary stenosis.