Chest Pain in Emergency Department Patients: A Comparison of Logistic Regression Versus Machine Learning in Predicting Major Adverse Cardiac Events and Abnormal Troponin

Abstract

Myocardial infarction is the primary diagnosis to rule out in emergency department chest pain patients. In this retrospective, multi-site study, we compared logistic regression (LR) with machine learning (ML) in predicting which patients were at risk of major adverse cardiac events (MACE) and abnormal troponin. Of the 1,538 patients identified over 43 days, 1,014 were retained of whom 70 suffered a MACE. LR and ML models for MACE were internally validated and achieved similar area under curve (AUC): 0.89 (95% CI: 0.87, 0.93) and 0.92 (95% CI: 0.89, 0.94) respectively. Abnormal troponin models had overlapping AUCs. Two novel clinical decision scores were derived: the Preliminary Chest Pain Risk Score with a sensitivity of 100.00% (95% CI: 94.87%, 100.00%) for identifying low risk chest pain patients and the Ultra-Low Risk Troponin Score which could be used in lieu of troponin. Future prospective studies will be required to externally validate these scores.