Machine learning models in heart failure with mildly reduced ejection fraction patients.

ABSTRACT:

Objective

Heart failure with mildly reduced ejection fraction (HFmrEF) has been recently recognized as a unique phenotype of heart failure (HF) in current practical guideline. However, risk stratification models for mortality and HF re-hospitalization are still lacking. This study aimed to develop and validate a novel machine learning (ML)-derived model to predict the risk of mortality and re-hospitalization for HFmrEF patients.

Methods

We assessed the risks of mortality and HF re-hospitalization in HFmrEF (45-49%) patients enrolled in the TOPCAT trial. Eight ML-based models were constructed, including 72 candidate variables. The Harrell concordance index (C-index) and DeLong test were used to assess discrimination and the improvement in discrimination between models, respec

SUBMITTER: Zhao H 

PROVIDER: S-EPMC9748556 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature