Objective To evaluate the diagnostic performance of the automated quantitative analysis by coronary computed tomography angiography (CCTA) for lesion specific hemodynamic significance assessed by fractional flow reserve(FFR). Methods One hundred and fifteen patients with one hundred and fifty?one vessels,who successively underwent invasive coronary angiography with evaluation of FFR(values≤0.8 were defined as lesion specific hemodynamically significant), were analyzed by coronary CT angiography. FFR≤0.80 was found in 54(35.76％) of the 151 vessels, which was divided into two groups (group of hemodynamically significant and group of hemodynamically non-significant). CCTA images were quantitatively analyzed with automated software to obtain the following index:minimal lumen diameter(MLD), maximum diameter stenosis(MDS％), minimal lumen area(MLA), maximum area stenosis(MAS％), lesion length (LL), total plaque volume(TPV), total plaque burden(TPB), calcified plaque volume(CPV), calcified plaque burden (CPB), non-calcified plaque volume(NCPV), non-calcified plaque burden(NCPB), lipid plaque volume(LPV), lipid plaque burden(LPB), fibrous plaque volume(FPV), fibrous plaque burden(FPB), napkin-ring sign(NRS), remodeling index(RI) and eccentric index(EI). Logistic regression and area under the receiver operating characteristics were used for statistical analysis. Results MDS％(65.04％± 8.20％), MAS％(73.91％± 7.58％), TPB(57.96％± 11.17％), CPB[4.32％(0.11％, 5.34％)], LPB[14.89％(9.30％, 19.23％)], CPV[30.68 (0.29, 33.36)mm3], LPV[(81.72(33.92, 94.68)mm3]in the group with hemodynamic significance were larger than those in group with normal hemodynamic status[58.27％± 9.50％, 64.83％± 8.31％, 53.88％± 11.77％, 2.05％(0.00％, 3.42％), 11.83％(6.34％, 16.8％), 12.53(0.00, 13.24)mm3, 60.71(24.1, 75.11)mm3, respectively], which was statistically significant(t=4.41,P<0.01;Z=6.63,P<0.01;t=2.08,P<0.05;Z=-2.47,P<0.01;Z=-2.30,P<0.05;Z=-2.48, P<0.01;Z=-2.55, P<0.01, respectively). MLD[1.24(1.04, 1.46)mm]and MLA[3.61(2.40, 4.80) mm2]in the group with hemodynamic significance were smaller than those in group with normal hemodynamic status[1.53(1.32,1.72)mm, 5.28(4.00,6.40)mm2],which was statistically significant[Z=-4.82,-5.40, respectively;P<0.01].In logistic regression analysis, only MAS％(OR:1.08,95％CI:1.01-1.15,P=0.02), CPB (OR:1.16,95％CI:1.02-1.33,P=0.02) and LPB(OR:1.10,95％CI:1.01-1.19,P=0.02), MLA(OR:0.69, 95％CI:0.49-0.98,P=0.04)were significant predictors of hemodynamic significance. For predicting lesion specific hemodynamic significance, compared with MLA(0.76), MDS％(0.71), CPB(0.62) and LPB(0.61), except for MLA(Z=0.77, P=0.44), the AUC of MAS％(0.79) was significantly increased(Z=2.54, P=0.01;Z=2.91, P<0.01;Z=2.94, P<0.01, respectively). However, combination of other index to MAS％[MAS％+MLA％(0.81), MAS％+MDS％(0.80), MAS％+TPB(0.80), MAS％+CPB(0.80), MAS％+LPB(0.81)] did not show significantly difference over MAS％(Z=1.10, 0.71, 0.40, 0.54, 1.07, respectively;P>0.05). Conclusion Compared with diameter stenosis, area stenosis substantially improves the prediction of lesion specific hemodynamic significance.

Objective: To characterize the hemodynamic force towards coronary plaque based on noninvasive coronary computed tomographic angiography and to investigate its relationship with plaque features and stenosis severity by computational fluid dynamics. Methods: Twenty-six patients underwent invasive fractional flow reserve measurement following coronary computed tomography angiography examination from March to September 2016 were retrospectively included. Computational fluid dynamics was applied and wall shear stress (WSS) and axial plaque stress (APS), which extracted the axial component of hemodynamic stress acting on stenotic lesions, were calculated based on the results of noninvasive coronary computed tomographic angiography. Plaque analysis was performed to elucidate plaque features and relative plaque burden. The fluid dynamics distributions in lesions with different stenosis severity were investigated. Results: Thirty-one coronary plaques with satisfactory imaging quality were analyzed, there were 11 (35.5%) dominant low WSS (<1 Pa) lesion and 20 high WSS lesion (64.5%), 8(25.8%) net retrograde APS lesion and 23(74.2%) anterograde lesion. Plaque volume was (78.5±48.6) mm³ and plaque burden was (69.1±12.1)% in the low WSS group, which was(60.5±57.3) mm³, and(57.5±14.0)%, respectively in the high WSS group, the plaque burden was significantly higher in the low WSS group than in the high WSS group (P=0.028), while the percentage of calcified plaque, fibrotic plaque and lipid core volume were similar between the two groups (P>0.05). Plaque volume was (79.7±69.1) mm³ and plaque burden was(68.7±13.7)% in the group with anterograde-dominant APS plaque, which was(61.7±24.9)mm³, and(68.9±10.4)%, respectively in the net retrograde APS lesion group (P>0.05). Percentage of lipid core area was significantly higher in the anterograde lesion group than in the retrograde lesion group ((25.1±18.1)% vs.(10.8±12.7)%, P=0.049). Both WSS and APS were significant higher in the severe obstructive coronary stenosis group than in non-severe obstructive coronary stenosis group (P<0.05). Although there was no difference in WSS between functional coronary ischemia group and non-functional coronary ischemia group ( (13.3±8.7) Pa vs. (12.5±14.2) Pa, P>0.05), the distribution of APS was different between the functional coronary ischemia group and non-functional coronary ischemia group ((1 698.8±652.6) Pa vs. (981.4±787.5) Pa, P<0.05). Conclusion WSS and APS can uniquely characterize the stenotic segment and has a strong relationship with lesion geometry. APS may be related to the necrotic core plaque and functional coronary ischemia. Clinical application of these hemodynamic and geometric indices may be helpful to assess the future risk of plaque progress and plaque rupture, which will be helpful on determining respective treatment strategy for patients with coronary artery disease.

Objective: Evidence supports the efficacy of coronary computed tomography angiography (CCTA)-based risk scores in cardiovascular risk stratification of patients with suspected coronary artery disease (CAD). We aimed to compare two CCTAbased risk score algorithms, Leiden and Confirm scores, in patients with diabetes mellitus (DM) and suspected CAD. Materials and Methods: This single-center prospective cohort study consecutively included 1241 DM patients (54.1% male, 60.2 ± 10.4 years) referred for CCTA for suspected CAD in 2015–2017. Leiden and Confirm scores were calculated and stratified as < 5 (reference), 5–20, and > 20 for Leiden and < 14.3 (reference), 14.3–19.5, and > 19.5 for Confirm. Major adverse cardiovascular events (MACE) were defined as the composite outcomes of cardiovascular death, nonfatal myocardial infarction (MI), stroke, and unstable angina requiring hospitalization. The Cox model and Kaplan–Meier method were used to evaluate the effect size of the risk scores on MACE. The area under the curve (AUC) at the median follow-up time was also compared between score algorithms. Results: During a median follow-up of 31 months (interquartile range, 27.6–37.3 months), 131 of MACE were recorded, including 17 cardiovascular deaths, 28 nonfatal MIs, 64 unstable anginas requiring hospitalization, and 22 strokes. An incremental incidence of MACE was observed in both Leiden and Confirm scores, with an increase in the scores (log-rank p < 0.001). In the multivariable analysis, compared with Leiden score < 5, the hazard ratios for Leiden scores of 5–20 and > 20 were 2.37 (95% confidence interval [CI]: 1.53–3.69; p < 0.001) and 4.39 (95% CI: 2.40–8.01; p < 0.001), respectively, while the Confirm score did not demonstrate a statistically significant association with the risk of MACE. The Leiden score showed a greater AUC of 0.840 compared to 0.777 for the Confirm score (p < 0.001). Conclusion CCTA-based risk score algorithms could be used as reliable cardiovascular risk predictors in patients with DM and suspected CAD, among which the Leiden score outperformed the Confirm score in predicting MACE.

Objective:To develop a pretest probability model of obstructive coronary artery disease with machine learning based on multi-site Chinese population data.Methods:Chinese regiStry in early deTection and Risk strAtificaTion of coronary plaques (C-Strat) study is a prospective multi-center cohort study, in which consecutive patients with suspected obstructive coronary artery disease and ≥64 detector row coronary computed tomography angioplasty (CCTA) evaluation were included. Data from the patients were randomly split into a training set (70%) and a test set (30%). More than 50% of coronary artery stenosis by CCTA was defined as positive outcome. A boosted ensemble algorithm (XGBoost), 10-fold cross-validation and Bayesian optimization were used to establish a new prediction model-CARDIACS(pretest probability model from Chinese registry in eARly Detection and rIsk stratificAtion of Coronary plaques Study), and a logistic regression was used to establish a model-LOGISTIC in training set. The test set was used for validation and comparison among CARDIACS, LOGISTIC, UDFM (updated Diamond-Forrester Model) and DFCASS(Diamond-Forrester and CASS).Results:The study population included 29 455 patients with age of (57.0±9.7) years and 44.8% women, of whom 19.1% (5 622/29 455) had obstructive coronary artery disease. For CARDIACS, the age, the reason for visit and the body mass index (BMI) were the most important predictive variables. In the independent test set, the area under the curve (AUC) of CARDIACS was 0.72 (95% CI 0.70-0.73), which was significantly superior to that of LOGISTIC (AUC 0.69, 95% CI 0.68-0.71, P=0.015), UDFM (AUC 0.64, 95% CI 0.62-0.65, P<0.001) and DFCASS (AUC 0.66, 95% CI 0.64-0.67, P<0.001), respectively. Conclusion:Based on Chinese population, the study developed a new pretest probability model--CARDIACS, which was superior to the traditional models. CARDIACS is expected to assist in the clinical decision-making for patients with stable chest pain.