Value of CT-derived fractional flow reserve in identifying patients with acute myocardial infarction based on coronary computed tomography angiography

The aim of the present study was to determine whether coronary stenosis and computed tomography-derived fractional flow reserve (CT-FFR), detected by coronary computed tomography angiography (CCTA), can potentially contribute to distinguish acute myocardial infarction (AMI) from unstable angina (UA). The study retrospectively collected data from consecutive patients who were admitted with obstructive coronary artery disease (CAD) and who received CCTA and invasive coronary angiography (ICA) as part of their clinical workup. According to the inclusion criteria, the patients were divided into the AMI group and UA group, and the basic clinical data, CCTA stenosis degree and CT-FFR values were compared between the two groups. Univariate and multivariate logistic regression methods were used to analyze the association between >= 70% CCTA stenosis,-0.80 CT-FFR and AMI. A diagnostic model of AMI was established (model 1,-0.80 CT-FFR; model 2, >= 70% CCTA stenosis; and model 3,-0.80 CT-FFR combined with >= 70% CCTA stenosis), and the diagnostic efficacy of the three models for AMI was compared. The significance level was set at P<0.05. A total of 116 participants were finally enrolled in this study. There were 37 patients in the AMI group, with an average age of 62.06 +/- 7.74 years, and 79 patients in the UA group, with an average age of 58.11 +/- 10.0 years; there was no significant difference in age (P>0.05). The multivariate 10.3892/etm.2023.12258 regression analysis revealed that-0.80 CT-FFR (HR=28.074; 95% CI: 5.712-137.973; P<0.001), and >= 70% CCTA stenosis (HR=10.796; 95% CI: 2.566-45.425; P=0.001) were independent risk factors for AMI. The diagnostic model of-0.80 CT-FFR combined with >= 70% CCTA stenosis (AUC=0.914; 95% CI: 0.847-0.958) exhibited increased diagnosis performance than the-0.80 CT-FFR model (AUC=0.865; 95% CI: 0.790-0.922; P=0.0060) and the >= 70% CCTA stenosis model (AUC=0.827; 95% CI: 0.745-0.891; P=0.0008). Collectively, it was demonstrated that-0.80 CT-FFR and >= 70% CCTA stenosis were independent risk factors for the diagnosis of AMI, and the combination of CT-FFR and CCTA stenosis further improved AMI diagnosis performance.