Using Machine Learning to Risk Stratify Emergency Department Patients With Chest Pain but No Acute Myocardial Infarction: A Multicenter Retrospective Analysis.

ABSTRACT:

Background

This study aimed to develop a machine learning-based model to predict the risk of major adverse cardiac events (MACE) in patients presenting to the emergency department (ED) with chest pain, for whom acute myocardial infarction was excluded after serial high-sensitivity cardiac troponin testing.

Methods

This retrospective analysis included adult patients presenting with chest pain at 5 study hospitals between 2021 and 2024 in Texas. Patients diagnosed with acute myocardial infarction during the index visit were excluded. The primary outcome was the occurrence of 30-day MACE, defined as myocardial infarction or all-cause mortality within 30 days of the index ED visit. A long short-term memory algorithm was used to develop the prediction model.

Results

The an

SUBMITTER: Chou EH 

PROVIDER: S-EPMC12553410 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature