A pretest model of obstructive coronary artery disease based on machine learning: from the C-Strat study
10.3760/cma.j.cn112138-20210119-00049
- VernacularTitle:基于机器学习的阻塞性冠心病验前概率模型：来自C-Strat研究
- Author:
Kai WANG
1
;
Junjie YANG
;
Zinuan LIU
;
Guanhua DOU
;
Xi WANG
;
Dongkai SHAN
;
Yundai CHEN
Author Information
1. 解放军总医院第一医学中心心血管内科，北京 100853
- Keywords:
Coronary artery disease;
Coronary angiography;
Pretest probability;
Diagnosis;
Machine learning
- From:
Chinese Journal of Internal Medicine
2022;61(2):185-192
- CountryChina
- Language:Chinese
-
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.
