Clinical prediction models for post-stroke depression: a systematic review and meta-analysis.

ABSTRACT:

Background

Post-stroke depression (PSD) is a prevalent neuropsychological consequence of stroke, associated with cognitive decline, disability, and increased mortality. Early prediction of PSD is critical for timely interventions and better outcomes. This study evaluates the effectiveness of various clinical prediction models, particularly machine learning methods, in forecasting PSD.

Methods

A systematic review and meta-analysis were conducted to evaluate predictive models for post-stroke depression (PSD) using data from 16 studies. The databases searched included PubMed, Embase, Cochrane Library, and Web of Science, covering publications from the year 2000 to the present. The risk of bias in the predictive models was assessed using the Prediction model Risk of Bias Assessm

SUBMITTER: Wu H 

PROVIDER: S-EPMC12753921 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature