Machine Learning-Based prediction models for postoperative delirium: a systematic review and Meta-Analysis.

ABSTRACT:

Background

The number of risk prediction models for postoperative delirium(POD) has increased yearly, but their quality and applicability in clinical practice and future research remain unclear.

Aims

This systematic review aimed to evaluate published studies on POD risk prediction models, evaluate the diagnostic accuracy of these models, and provide guidance for model establishment and improvement.

Methods

We conducted a systematic search in PubMed, Embase, the Cochrane Library, and other scientific databases to identify eligible studies published up to February 15, 2025. The included studies provided data on the sensitivity and specificity of predictive models.

Results

We included 17 articles with 69 machine learning (ML) prediction models, encompassing 205,20

SUBMITTER: Tu Y 

PROVIDER: S-EPMC12506401 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature