Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features.

ABSTRACT:

Background

Cardiac Resynchronization Therapy (CRT) is a widely used, device-based therapy for patients with left ventricle (LV) failure. Unfortunately, many patients do not benefit from CRT, so there is potential value in identifying this group of non-responders before CRT implementation. Past studies suggest that predicting CRT response will require diverse variables, including demographic, biomarker, and LV function data. Accordingly, the objective of this study was to integrate diverse variable types into a machine learning algorithm for predicting individual patient responses to CRT.

Methods

We built an ensemble classification algorithm using previously acquired data from the SMART-AV CRT clinical trial (n = 794 patients). We used five-fold stratified cross-validation on

SUBMITTER: Haque A 

PROVIDER: S-EPMC9620606 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature