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.

Objective:To explore the risk stratification value of coronary CT angiography (CCTA) in patients with non-obstructive coronary artery disease based on cluster analysis and to identify the high-risk population of cardiovascular adverse events in patients.Methods:Prospective consecutive patients with suspected coronary artery disease who underwent CCTA examination and were confirmed as non-obstructive coronary heart disease were enrolled in the General Hospital of Chinese PLA from January 1, 2015 to December 31, 2017. The clinical characteristics and CCTA diagnosis information of patients were collected, and then follow-up was performed to obtain adverse cardiovascular events. Firstly, the cluster analysis based on CCTA information divided the patients into different groups. Then, the risk of adverse cardiovascular events was compared between different groups. Finally, segment involvement score (SIS) score, Leiden score, SIS score combined with clinical characteristics, Leiden score combined with clinical characteristics, and cluster information combined with clinical characteristics were used to stratify the population, and the concordance index-time curve and net reclassification improvement (NRI) index were described to compare the risk stratification ability of the five different models.Results:A total of 3 402 patients with non-obstructive coronary artery disease were included in the study, of whom 104 had adverse cardiovascular events during the follow-up period. Cluster analysis based on CCTA information classified patients into 3 different groups. There were statistically significant differences in clinical characteristics, CCTA information, and survival outcomes between groups ( P<0.05). The results of the concordance index-time curve showed that the risk stratification ability of CCTA cluster information combined with clinical characteristics was better than the current SIS score, Leiden score, SIS score combined with clinical characteristics, Leiden score combined with clinical characteristics. At the 1-year and 2-year time cutoffs, cluster information combined with clinical characteristics showed a positive increase in INR compared with the first four models (INR was 0.248 and 0.293, 0.316 and 0.293, 0.147 and 0.003, 0.192 and 0.007, respectively). Conclusion:CCTA based on cluster analysis has a good risk stratification value for patients with non-obstructive coronary artery disease and is helpful for individualized intervention.