A machine learning model in predicting hemodynamically significant coronary artery disease: A prospective cohort study.

ABSTRACT:

Background

Coronary artery disease (CAD) costs healthcare billions of dollars annually and is the leading cause of death despite available noninvasive diagnostic tools.

Objective

This study aims to examine the usefulness of machine learning in predicting hemodynamically significant CAD using routine demographics, clinical factors, and laboratory data.

Methods

Consecutive patients undergoing cardiac catheterization between March 17, 2015, and July 15, 2016, at UNC Chapel Hill were screened for comorbidities and CAD risk factors. In this pilot, single-center, prospective cohort study, patients were screened and selected for moderate CAD risk (n = 185). Invasive coronary angiography and CAD prediction with machine learning were independently performed. Results were blind

SUBMITTER: Liu Y 

PROVIDER: S-EPMC9204796 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature