Predictive value of coronary stenosis degree combined with CT-FFR and resting-state CTP for major adverse cardiac events in obstructive coronary artery disease.

Abstract

CT-based flow reserve fraction (CT-FFR) and CT perfusion (CTP), as a complement to coronary computed tomographic angiography (CCTA) have been revealed to be associated with the prognosis of patients with obstructive coronary artery disease (CAD). However, the prognostic value of coronary stenosis combined with CT-FFR and resting-state CTP based on CCTA for major adverse cardiac events (MACE) is not known and requires further investigation. Fifty-two patients with obstructive CAD (50%-90% stenosis) examined by CCTA were retrospectively collected and followed-up for the occurrence of MACE. Logistic regression was performed to analyze the effects of the degree of coronary stenosis, resting-state CTP, and CT-FFR in predicting the risk of MACE. MACE prediction models were developed, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive validity of different models for MACE. Ethics approval was provided by the First Affiliated Hospital of Hebei North University (Zhangjiakou, China; No. K2020237). Logistic regression analysis showed that coronary artery stenosis ≥ 70%, CT-FFR ≤ 0.80, and perfusion index (PI) were independent predictors for MACE in patients with obstructive CAD (P < .05). The model based on coronary stenosis combined with PI and CT-FFR (AUC = 0.944) was better than those based on the degree of coronary stenosis combined with PI (AUC = 0.874), coronary stenosis degree combined with CT-FFR (AUC = 0.895), and any single index (P < .05). The combined model established by coronary stenosis, CT-FFR, and resting-state CTP based on a "1-stop" CCTA examination for predicting MACE among patients with obstructive CAD has good diagnostic efficacy and shows incremental discriminatory power.