Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance.

ABSTRACT:

Background

Cardiac resynchronization therapy (CRT) response is complex, and better approaches are required to predict survival and need for advanced therapies.

Objective

The objective was to use machine learning to characterize multidimensional CRT response and its relationship with long-term survival.

Methods

Associations of 39 baseline features (including cardiac magnetic resonance [CMR] findings and clinical parameters such as glomerular filtration rate [GFR]) with a multidimensional CRT response vector (consisting of post-CRT left ventricular end-systolic volume index [LVESVI] fractional change, post-CRT B-type natriuretic peptide, and change in peak VO₂) were evaluated. Machine learning generated response clusters, and cross-validation assessed associa

SUBMITTER: Bivona DJ 

PROVIDER: S-EPMC9626744 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature