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

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 analysis included 14 177 patients with a median age of 49.7 years, 41.2% of whom were men. A total of 535 patients (3.8%) had at least 1 high-sensitivity cardiac troponin level above the 99th percentile. Thirty-nine patients (0.3%) experienced 30-day MACE, including 15 (0.1%) with myocardial infarction and 24 (0.2%) with all-cause mortality. The long short-term memory model demonstrated excellent performance in predicting 30-day MACE (area under the receiver operating characteristic curve [AUC], 0.884 [95% CI, 0.815-0.941]), myocardial infarction (AUC, 0.963 [95% CI, 0.926-0.993]), and all-cause mortality (AUC, 0.849 [95% CI, 0.698-0.948]).

CONCLUSIONS

The long short-term memory model accurately predicted 30-day MACE in ED patients presenting with chest pain and no acute myocardial infarction, using patient demographics, vital signs at ED presentation, electrocardiographic findings, and serial high-sensitivity cardiac troponin levels measured at flexible time points within 24 hours of ED arrival.