ABSTRACT
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.
ABSTRACT
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.