Zheng Zhang,1,2,* Binbin Shao,3,* Hongzhou Liu,2,4 Ben Huang,2,5 Xuechen Gao,1 Jun Qiu,1 Chen Wang1,2 

1Center of Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, People’s Republic of China; 2Center for Gene Diagnosis, Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, People’s Republic of China; 3Department of Prenatal Diagnosis, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing, Jiangsu Province, People’s Republic of China; 4School of Clinical Medicine, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuang Province, People’s Republic of China; 5Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Chen Wang; Jun Qiu, Center of Clinical Laboratory, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, Jiangsu Province, 215006, People’s Republic of China, Email 1617657224@qq.com; 13606136542@126.com

Purpose: Early recognition of coronary artery disease (CAD) could delay its progress and significantly reduce mortality. Sensitive, specific, cost-efficient and non-invasive indicators for assessing individual CAD risk in community population screening are urgently needed.
Patients and Methods: 3112 patients with CAD and 3182 controls were recruited from three clinical centers in China, and differences in baseline and clinical characteristics were compared. For the discovery cohort, the least absolute shrinkage and selection operator (LASSO) regression was used to identify significant features and four machine learning algorithms (logistic regression, support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost)) were applied to construct models for CAD risk assessment, the receiver operating characteristics (ROC) curve and precision-recall (PR) curve were conducted to evaluate their predictive accuracy. The optimal model was interpreted by Shapley additive explanations (SHAP) analysis and assessed by the ROC curve, calibration curve, and decision curve analysis (DCA) and validated by two external cohorts.
Results: Using LASSO filtration, all included variables were considered to be statistically significant. Four machine learning models were constructed based on these features and the results of ROC and PR curve implied that the XGBoost model exhibited the highest predictive performance, which yielded a high area of ROC curve (AUC) of 0.988 (95% CI: 0.986– 0.991) to distinguish CAD patients from controls with a sensitivity of 94.6% and a specificity of 94.6%. The calibration curve showed that the predicted results were in good agreement with actual observations, and DCA exhibited a better net benefit across a wide range of threshold probabilities. External validation of the model also exhibited favorable discriminatory performance, with an AUC, sensitivity, and specificity of 0.953 (95% CI: 0.945– 0.960), 89.9%, and 87.1% in the validation cohort, and 0.935 (95% CI: 0.915– 0.955), 82.0%, and 90.3% in the replication cohort.
Conclusion: Our model is highly informative for clinical practice and will be conducive to primary prevention and tailoring the precise management for CAD patients.

Keywords: coronary artery disease, predictive model, machine learning, XGBoost, primary prevention