AI-guided refinement of coronary revascularization need in patients suspected of acute coronary syndrome.

ABSTRACT:

Aims

Overdiagnosis in patients suspected of acute coronary syndrome (ACS) leads to unnecessary coronary angiographies, particularly in cases with non-specifically elevated troponin (Trop) levels. We established machine learning (ML) models integrating sequentially available prehospital and in-hospital variables to improve early prediction of the need for coronary re while minimizing overdiagnosis.

Methods and results

Retrospective cohort study analysing patients with suspected ACS from 2016 to 2020. Machine learning models were trained using data available at different diagnostic time points, including prehospital assessment, arterial blood gas analysis, full laboratory results, and sequential Trop measurements. A total of 2756 patients were included, identified through emer

SUBMITTER: Sigle M 

PROVIDER: S-EPMC12629660 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature