Abstract:

Objective To explore the values of CT derived fractional flow reserve（CT⁃FFR），coronary artery stenosis and clinical cardiovascular risk factors and their combinationin the risk prediction of major adverse cardiac events（MACE）in patients with obstructive coronary artery disease（CAD）. Methods A total of 133 patients with obstructive CAD who underwent coronary artery CT angiography examination were collected retrospectively. The occurrence of MACE was follow up. Cox proportional hazard regression model was used to analyze the role of clinical cardiovascular risk factors，coronary artery stenosis and CT ⁃ FFR in predicting the risk of MACE. Three models were established for predicting MACE and the under curve of the ROC was used to evaluate the ability of the three models in predicting MACE. Results Multivariate Cox regression analysiss showed that CT⁃FFR value ≤ 0.80（HR = 4.41），coronary artery stenosis ≥ 70%（HR = 3.65），diabetes mellitus（HR = 2.54）were indepen⁃ dent predictors for MACE in patients with obstructive CAD（P < 0.05）. The model based on CT⁃FFR，coronary artery stenosis degree and clinical cardiovascular risk factors（AUC = 0.85）was better than the model based on CT⁃ FFR（AUC = 0.74）and the model based on CT⁃FFR and coronary artery stenosis degree（AUC = 0.79）（P < 0.001）. Conclusion The combined prediction models of CT⁃FFR、coronary stenosis and clinical cardiovascular risk factors which were established to predict the risk of MACE in patients with CAD has good diagnostic power，providing reference information for the timely clinical intervention and improved prognosis of patients with CAD.

Key words:

CT derived fractional flow reserve, coronary heart disease, major adverse cardiac events, angiography, artificial intelligence