Incremental value of coronary computed tomography-derived fractional flow reserve for the prediction of major adverse cardiovascular events in patients with ischemic stroke.

Background: Patients with ischemic stroke (IS) experience high recurrence rates and are susceptible to adverse cardiac events. To enhance risk stratification, this study aims to investigate the prognostic value of coronary computed tomography-derived fractional flow reserve (CT-FFR) in this population and to integrate CT-FFR into a risk prediction model for major adverse cardiovascular events (MACEs). Methods: Patients diagnosed with IS who underwent one-step integrated coronary-carotid-cerebral computed tomography angiography (ICCC-CTA) were prospectively enrolled. The morphological parameters of atherosclerosis among the included arteries and coronary CT-FFR were extracted from the computed tomography (CT) data. All participants were followed up for 1 year. Multivariate Cox proportional hazards regression models were applied on the clinical and imaging data to determine the independent risk factors for the occurrence of MACE. These significant clinical factors and atherosclerosis imaging parameters were included to construct prediction model 1 (without CT-FFR) and model 2 (with CT-FFR) if CT-FFR was identified as an independent risk factor. The performance of the two models was compared. Results: A total of 347 patients (mean age: 61.7±9.1 years, 278 males) were included. MACE occurred in 11.5% (40/347) of the patients. Multivariate Cox regression analysis showed that age [hazard ratio (HR) =1.06 per year, P=0.005], CT-FFR ≤0.80 (HR =3.52, P=0.037), diabetes history (HR =2.03, P=0.034), and cerebral atherosclerosis score (CAS) (HR =1.21, P=0.001) were the independent predictors of MACE. Model 1 included age, CAS, and diabetes history, whereas model 2 additionally incorporated CT-FFR ≤0.80. The concordance index (C-index) and area under the curve (AUC) of model 2 were higher than those of model 1. Model 2 demonstrated superior performance, showing better calibration curve agreement, greater integrated discrimination improvement (IDI), improved net reclassification improvement (NRI), a lower Akaike information criterion (AIC), and offered greater net clinical benefit compared to Model 1. Conclusions: Coronary CT-FFR ≤0.80 was found to be an independent predictor of MACE in patients with IS. The model incorporating CT-FFR along with morphological atherosclerotic burden demonstrated superior performance in predicting MACE.