Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database (Preprint)

Abstract

Since acute myocardial infarction (AMI) is a leading cause of mortality worldwide, the accurate evaluation of risk factors of AMI at prehospital stage enables appropriate prehospital management and rapid transportation of patients to the most appropriate hospital for treatment. The prediction of AMI derived from national database may accelerate early recognition and timely management to improve the survival rate.

This study was conducted to develop and compare the efficacy of models for the prediction of AMI at a prehospital stage based on variables identified from a nationwide systematic emergency medical service (EMS) registry using conventional statistical methods and machine learning algorithms.

From patients transferred from the public EMS to emergency departments in Korea from January 2016 to December 2018, patients with age >15 years in the EMS cardiovascular registry were enrolled. Two datasets were constructed according to the hierarchical structure of the EMS cardiovascular registry. For each dataset, several predictive models for AMI were derived and compared using conventional statistical methods and machine learning.

In total, 184,577 patients (Dataset 1) in the EMS cardiovascular registry were included in the final analysis. Among them, 72,439 patients (Dataset 2) were suspected to have AMI at a prehospital stage (as assessed by paramedics). Among the models derived using the conventional logistic regression method, the B-type model incorporated AMI-specific variables from the A-type model, and exhibited a superior discriminative ability (P = 0.02). The models that used extreme gradient boosting and multilayer perceptron yielded a higher predictive performance than that of the model derived based on conventional logistic regression for all analyses that used both datasets. Each machine learning algorithm yielded different classification lists regarding the 10 most important features.

This study demonstrates that prediction models that use nationwide prehospital data and are developed with appropriate structures can improve the identification of patients who need timely AMI management.