Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.

ABSTRACT:

Purpose

Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).

Methods

From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to st

SUBMITTER: Williams MC 

PROVIDER: S-EPMC10317876 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature