Machine learning-based risk stratification for mortality in patients with severe aortic regurgitation.

ABSTRACT:

Aims

The current guidelines recommend aortic valve intervention in patients with severe aortic regurgitation (AR) with the onset of symptoms, left ventricular enlargement, or systolic dysfunction. Recent studies have suggested that we might be missing the window of early intervention in a significant number of patients by following the guidelines.

Methods and results

The overarching goal was to determine if machine learning (ML)-based algorithms could be trained to identify patients at risk for death from AR independent of aortic valve replacement (AVR). Models were trained with five-fold cross-validation on a dataset of 1035 patients, and performance was reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a conditional random

SUBMITTER: Anand V 

PROVIDER: S-EPMC10232267 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature