A Machine Learning-Derived Score to Effectively Identify Heart Failure With Preserved Ejection Fraction.

ABSTRACT:

Background

The diagnosis of heart failure with preserved ejection fraction (HFpEF) in the clinical setting remains challenging, especially in patients with obesity.

Objectives

This study aimed to identify novel predictors of HFpEF well suited for patients with obesity.

Methods

We performed a retrospective analysis of a well-characterized cohort of patients with obesity with HFpEF (n = 404; mean body mass index [BMI] 36.6 kg/m²) and controls (n = 67). We used the machine learning algorithm Gradient Boosting Machine to analyze the association of various parameters with the diagnosis of HFpEF and subsequently created a multivariate logistic model for the diagnosis.

Results

Gradient Boosting Machine identified BMI, estimated glomerular filtration rate, l

SUBMITTER: Bermea KC 

PROVIDER: S-EPMC11312345 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature