Biomarker-based depression risk prediction in chronic heart failure patients: an interpretable machine learning approach.

ABSTRACT:

Background

Chronic heart failure (CHF) is frequently complicated by depression, which worsens prognosis but remains underdiagnosed due to symptom overlap and a lack of objective screening tools. Although biomarkers reflecting lipid metabolism, insulin resistance, inflammation, and neuro-immuno-endocrine imbalance have been implicated in both CHF and depression, their predictive value for psychiatric outcomes in CHF patients is unclear.

Aim

This study aimed to develop and validate interpretable machine learning (ML) models for predicting depression risk in CHF patients via the use of clinical and biomarker data.

Methods

We retrospectively enrolled 3, 110 CHF patients admitted between January 2015 and December 2024 at Guang'anmen Hospital. Demographic, clinical, and lab

SUBMITTER: Tao Y 

PROVIDER: S-EPMC12738324 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature