Development and validation of a machine learned algorithm to IDENTIFY functionally significant coronary artery disease.

ABSTRACT:

Introduction

Multiple trials have demonstrated broad performance ranges for tests attempting to detect coronary artery disease. The most common test, SPECT, requires capital-intensive equipment, the use of radionuclides, induction of stress, and time off work and/or travel. Presented here are the development and clinical validation of an office-based machine learned algorithm to identify functionally significant coronary artery disease without radiation, expensive equipment or induced patient stress.

Materials and methods

The IDENTIFY trial (NCT03864081) is a prospective, multicenter, non-randomized, selectively blinded, repository study to collect acquired signals paired with subject meta-data, including outcomes, from subjects with symptoms of coronary artery disease. Time

SUBMITTER: Stuckey T 

PROVIDER: S-EPMC9481304 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature