[Investigation of the impact of the deep learning based CT fractional flow reserve on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease].

<b>Objective:</b> To investigate the impact of the deep-learning-based CT fractional flow reserve (CT-FFR) on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease. <b>Methods:</b> In this single-center retrospective cohort study, consecutive patients with obstructive coronary heart disease (with at least one stenosis≥50%) on their first coronary computed tomography angiography (CCTA) in Beijing Anzhen Hospital from February 2017 to July 2018 were included. Baseline clinical and CT characteristics were collected. Deep-learning-based CT-FFR and Leiden CCTA risk score were calculated. All patients enrolled were followed up for at least 5 years. The study endpoint was major adverse cardiovascular events (MACE), defined as the composite of cardiac death, nonfatal myocardial infarction, unstable angina requiring hospitalization, and unplanned revascularization. Receiver operating characteristic (ROC) curves were drawn to define the optimal cut-off point of the Leiden score in predicting the 5-year MACE, and survival analysis and Cox regression were performed to explore the related factors of MACE. <b>Results:</b> A total of 622 patients, aged 61 (54, 66) years, with 407 (65.4%) males were included. Diagnostic coronary angiography was performed in 78 patients after their baseline CCTA, with 34 (43.6%) patients had CT-FFR>0.80. During a follow-up time of 2 181 (2 093, 2 355) days, 155 patients (24.9%) suffered from MACE. ROC derived optimal cut-off point of Leiden score for predicting MACE was 15.48. Survival analysis found that male patients, Leiden risk score>15 and CT-FFR≤0.80 had worse prognosis. Multivariate Cox regression analysis identified CT-FFR≤0.80 as an robust and independent predictor of MACE (<i>HR</i>=4.98, 95%<i>CI</i> 3.15-7.86, <i>P</i><0.001). <b>Conclusion:</b> Deep-learning-based CT-FFR aids in clinical decision-making and the evaluation of long-term prognosis in patients with obstructive coronary heart disease.