The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFR_CT, or high-risk plaque features?

Abstract

The present study aimed to compare the diagnostic performance of a machine learning (ML)-based FFR_CT algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFR_ICA. Patients who underwent both CCTA and FFR_ICA measurement within 2 weeks were retrospectively included. ML-based FFR_CT, volume of subtended myocardium (V_sub), percentage of subtended myocardium volume versus total myocardium volume (V_ratio), high-risk plaque features, minimal lumen diameter (MLD), and minimal lumen area (MLA) along with other parameters were recorded. Lesions with FFR_ICA ≤ 0.8 were considered to be functionally significant. One hundred eighty patients with 208 lesions were included. The lesion length (LL), diameter stenosis, area stenosis, plaque burden, V_sub, V_ratio, V_ratio/MLD, V_ratio/MLA, and LL/MLD⁴ were all significantly longer or larger in the group of FFR_ICA ≤ 0.8 while smaller minimal lumen area, MLD, and FFR_CT value were noted. The AUC of FFR_CT + V_ratio/MLD was significantly better than that of FFR_CT alone (0.935 versus 0.873, p < 0.001). High-risk plaque features failed to show difference between functionally significant and insignificant groups. V_ratio/MLD-complemented ML-based FFR_CT for "gray zone" lesions with FFR_CT value ranged from 0.7 to 0.8 and the combined use of these two parameters yielded the best diagnostic performance (86.5%, 180/208). ML-based FFR_CT simulation and V_ratio/MLD both provide incremental value over CCTA-derived diameter stenosis and high-risk plaque features for predicting hemodynamically significant lesions. V_ratio/MLD is more accurate than ML-based FFR_CT for lesions with simulated FFR_CT value from 0.7 to 0.8. • Machine learning-based FFR _CT and subtended myocardium volume both performed well for predicting hemodynamically significant coronary stenosis. • Subtended myocardium volume was more accurate than machine learning-based FFR _CT for "gray zone" lesions with simulated FFR value from 0.7 to 0.8. • CT-derived high-risk plaque features failed to correctly identify hemodynamically significant stenosis.