Risk stratification of chest pain in the emergency department using artificial intelligence applied to electrocardiograms.

ABSTRACT:

Background

Despite standardised approaches, subjective assessment and inconsistent diagnostic testing for chest pain in the emergency department (ED) drive costs, disparities and adverse outcomes. Artificial intelligence offers potential to automate and improve risk stratification.

Methods and results

Using a retrospective cohort of 15 048 patients presenting to the ED of a tertiary care hospital, we trained a neural network classifier ('Chest Pain-AI' or 'CP-AI') to predict a 7-day composite endpoint of major cardiovascular diagnoses including myocardial infarction, pulmonary embolism, aortic dissection and all-cause mortality. Inputs to CP-AI included age, sex, cardiac biomarkers (D-dimer or troponin I or T positivity) and numerical representations of presenting 12-lead EC

SUBMITTER: Haimovich JS 

PROVIDER: S-EPMC12406858 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature