Machine learning-based phenotyping and assessment of treatment responses in heart failure with preserved ejection fraction.

ABSTRACT:

Background

Heart failure with preserved ejection fraction (HFpEF) accounts for over half of heart failure cases, yet effective treatments remain limited due to its clinical heterogeneity. This study aimed to identify distinct HFpEF phenotypes using machine-learning based algorithm and to compare treatment responses across different phenogroups.

Methods

Our training cohort included 2147 hospitalized patients with HF with left ventricular ejection fraction (LVEF) ≥50% at Peking University Third Hospital (2014-2023). A two-stage DeepCluster model with a fully connected neural network was used to identify HFpEF phenogroups based on 107 demographic and clinical variables from electronic medical record (EMR). Cox proportional hazard models were used to assess patients' prognosis a

SUBMITTER: Li R 

PROVIDER: S-EPMC12572816 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature