Acute coronary syndrome screening in patients presenting with arteriosclerosis in health check-ups: a case-control study

Objectives This research aimed to develop a simple and effective acute coronary syndrome (ACS) screening model in order to intervene early and focus on prevention in patients presenting with arteriosclerosis. Design A case-control study. Setting The study used a cross-sectional survey to collect data from 2243 patients who completed anonymous electronic medical record (EMR) data and coronary angiography was gathered at a hospital in Shandong Province between December 2013 and April 2016. Participants Adults 18 years old and above diagnosed as ACS or non-ACS according to the records in hospital EMR database, and with completed basic information (age and sex). Predictors 54 laboratory biomarkers and demographic factors (age and sex). Statistical analysis A dataset without missing data of all patients' laboratory indicators and demographic factors was divided into training set and validation set after being balanced. After the training set balanced, area under the curve of random forest (AUCRF) and least absolute shrinkage and selection operator (LASSO) regression were used for feature extraction. Then two set random forest models were established with the different feature sets, and the process of comparison and analysis was made to evaluate models for the optimal model including sensitivity, accuracy and AUC receiver operating characteristic curves with the internal validation set. Main outcome measures To establish an ACS screening model. Results An RF model with 31 features selected by LASSO with an AUC of 0.616 (95% CI 0.650 to 0.772), a sensitivity of 0.832 and an accuracy of 0.714 in the validation set. The other RF model with 27 features selected by AUCRF with an AUC of 0.621 (95% CI 0.664 to 0.785), a sensitivity of 0.849 and an accuracy of 0.728 in the validation set. Conclusions The established ACS screening model with 27 clinical features provides a better performance for practical solution in predicting ACS.