A machine learning-based clinical decision support algorithm for reducing unnecessary coronary angiograms.

ABSTRACT:

Background

Conventional clinical risk scores and diagnostic algorithms are proving to be suboptimal in the prediction of obstructive coronary artery disease, contributing to the low diagnostic yield of invasive angiography. Machine learning could help better predict which patients would benefit from invasive angiography vs other noninvasive diagnostic modalities.

Objective

To reduce patient risk and cost to the healthcare system by improving the diagnostic yield of invasive coronary angiography through optimized outpatient selection.

Methods

Retrospective analysis of 12 years of referral data from a provincial cardiac registry, including all patients referred for invasive angiography of more than 1.4 million individuals in Ontario, Canada. Stable outpatients undergoin

SUBMITTER: Schwalm JD 

PROVIDER: S-EPMC8890355 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature