Objective:To investigate the additional prognostic value of coronary CT angiography (CCTA)-based flow reserve fraction (CT-FFR) over semi-quantitative CCTA risk scores in predicting the occurrence of major adverse cardiovascular events (MACE) in type 2 diabetic patients.Methods:A total of 231 patients with type 2 diabetes mellitus who underwent CCTA at Lanzhou University from May 2020 to April 2021 were retrospectively enrolled. Clinical baseline data were collected, and patients were divided into a MACE-positive group (20 cases) and a MACE-negative group (211 cases) based on follow-up results. The CCTA images of all patients were analyzed by semi-quantitative CCTA risk score, which included coronary artery disease reporting and data system classification, segment involvement score, segmental stenosis score, Leaman score, and Leiden score. CT-FFR measurements of CCTA data of all patients were performed using Coronary Analysis software. t-test, U-test, and χ2 test were used to compare baseline parameters between MACE-positive and MACE-negative groups. The Cox proportional hazards regression model was used to analyze the relationship between semi-quantitative CCTA risk score and CT-FFR with the occurrence of MACE, and the area under the curve (AUC) of the receiver operating characteristic (ROC) was used to calculate the efficacy of the prediction model established by the semi-quantitative CCTA risk score combined with CT-FFR. Results:There was no statistically significant difference in baseline data between patients in the MACE-positive and MACE-negative groups ( P>0.05), and there were significant differences in semi-quantitative CCTA risk scores and CT-FFR ( P<0.05). Multivariate Cox proportional risk regression analysis of CT-FFR≤0.80 ( HR=3.860, 95% CI 1.477-10.087, P=0.006) and Leaman score≥5 ( HR=5.210, 95% CI 1.136-23.908, P=0.029) were the best and independent predictors for the occurrence of MACE events. The combined CT-FFR and Leaman score prediction model (AUC=0.791, 95% CI 0.733-0.842, P<0.001) was a better predictor of MACE than CT-FFR alone (AUC=0.718, 95% CI 0.656-0.775, P<0.001) and Leaman score alone (AUC=0.711, 95% CI 0.648-0.768, P<0.001) both had better predictive efficacy ( Z=2.62, 1.98, P=0.009, 0.047). Conclusion:CT-FFR independently predict the occurrence of MACE in patients with type 2 diabetes mellitus and significantly improve the predictive capacity of semi-quantitative CCTA risk score for MACE.

Objective To develop a nomogram model based on cardiac computed tomography angiography(CCTA)features to evaluate the risk of stroke in patients with persistent atrial fibrillation(PAF).Methods A total of 387 patients with PAF were retrespectively selected.Among them,127 patients had a history of stroke.After collecting patient data,logistic regression analysis was used to screen independent predictors related to stroke outcome,and a nomogram model was constructed.The receiver operating characteristic(ROC)curve was used to analyze and compare the efficacy of the nomogram model and the conventional score for stroke risk assessment.The concordance index(CI)and decision curve analysis(DCA)were used to evaluate the performance of the model.Results Logistic regression analysis showed that age,CHA2DS2-VASc score,smoking history,left atrial appendage(LAA)shape,left atrial volume index(LAVI),LAA fractal dimension(FD)and left atrium(LA)FD were independent predictors of stroke in patients with PAF(P＜0.05).The area under the curve(AUC)of the training set model was 0.886.The AUC of the validation set was 0.763.DCA showed that the nomogram model had better overall net benefits.Conclusion The nomogram model based on CCTA can better evaluate the risk of stroke in patients with PAF,which is helpful for clinicians to make better clinical decisions.

Objective To develop a nomogram model based on cardiac computed tomography angiography(CCTA)features to evaluate the risk of stroke in patients with persistent atrial fibrillation(PAF).Methods A total of 387 patients with PAF were retrespectively selected.Among them,127 patients had a history of stroke.After collecting patient data,logistic regression analysis was used to screen independent predictors related to stroke outcome,and a nomogram model was constructed.The receiver operating characteristic(ROC)curve was used to analyze and compare the efficacy of the nomogram model and the conventional score for stroke risk assessment.The concordance index(CI)and decision curve analysis(DCA)were used to evaluate the performance of the model.Results Logistic regression analysis showed that age,CHA2DS2-VASc score,smoking history,left atrial appendage(LAA)shape,left atrial volume index(LAVI),LAA fractal dimension(FD)and left atrium(LA)FD were independent predictors of stroke in patients with PAF(P＜0.05).The area under the curve(AUC)of the training set model was 0.886.The AUC of the validation set was 0.763.DCA showed that the nomogram model had better overall net benefits.Conclusion The nomogram model based on CCTA can better evaluate the risk of stroke in patients with PAF,which is helpful for clinicians to make better clinical decisions.

Objective:To investigate the additional prognostic value of coronary CT angiography (CCTA)-based flow reserve fraction (CT-FFR) over semi-quantitative CCTA risk scores in predicting the occurrence of major adverse cardiovascular events (MACE) in type 2 diabetic patients.Methods:A total of 231 patients with type 2 diabetes mellitus who underwent CCTA at Lanzhou University from May 2020 to April 2021 were retrospectively enrolled. Clinical baseline data were collected, and patients were divided into a MACE-positive group (20 cases) and a MACE-negative group (211 cases) based on follow-up results. The CCTA images of all patients were analyzed by semi-quantitative CCTA risk score, which included coronary artery disease reporting and data system classification, segment involvement score, segmental stenosis score, Leaman score, and Leiden score. CT-FFR measurements of CCTA data of all patients were performed using Coronary Analysis software. t-test, U-test, and χ2 test were used to compare baseline parameters between MACE-positive and MACE-negative groups. The Cox proportional hazards regression model was used to analyze the relationship between semi-quantitative CCTA risk score and CT-FFR with the occurrence of MACE, and the area under the curve (AUC) of the receiver operating characteristic (ROC) was used to calculate the efficacy of the prediction model established by the semi-quantitative CCTA risk score combined with CT-FFR. Results:There was no statistically significant difference in baseline data between patients in the MACE-positive and MACE-negative groups ( P>0.05), and there were significant differences in semi-quantitative CCTA risk scores and CT-FFR ( P<0.05). Multivariate Cox proportional risk regression analysis of CT-FFR≤0.80 ( HR=3.860, 95% CI 1.477-10.087, P=0.006) and Leaman score≥5 ( HR=5.210, 95% CI 1.136-23.908, P=0.029) were the best and independent predictors for the occurrence of MACE events. The combined CT-FFR and Leaman score prediction model (AUC=0.791, 95% CI 0.733-0.842, P<0.001) was a better predictor of MACE than CT-FFR alone (AUC=0.718, 95% CI 0.656-0.775, P<0.001) and Leaman score alone (AUC=0.711, 95% CI 0.648-0.768, P<0.001) both had better predictive efficacy ( Z=2.62, 1.98, P=0.009, 0.047). Conclusion:CT-FFR independently predict the occurrence of MACE in patients with type 2 diabetes mellitus and significantly improve the predictive capacity of semi-quantitative CCTA risk score for MACE.