Systematic review and meta-analysis of machine learning-based prediction models for readmission risk after total hip and knee arthroplasty.

Background: Postoperative readmission is a critical metric after total hip and knee arthroplasty (THA/TKA). While machine learning (ML) models for predicting readmission risk are proliferating, the stability of their performance and the robustness of their methodology remain contentious. This study aimed to systematically review and quantitatively synthesize the evidence base for ML-based readmission prediction after THA/TKA. Methods: A systematic search was conducted across PubMed, Embase, Cochrane Library, and Web of Science from inception to December 31, 2025. Studies developing or validating ML models for THA/TKA readmission were included. Model performance (C-statistic/AUC) was extracted. Study quality was assessed using the PROBAST + AI tool. A multivariate random-effects meta-analysis was performed to pool C-statistics and quantify heterogeneity, with subgroup analyses stratified by study design, surgery type, prediction timeframe, and algorithmic class. Results: Fifteen studies (57 distinct models) were included. The pooled C-statistic was 0.76 (95% CI: 0.71-0.81). However, extreme heterogeneity ( Conclusion: The current body of evidence for ML-based readmission prediction after THA/TKA is characterized by extreme heterogeneity and high methodological bias, severely constraining clinical utility. The inability to pool calibration metrics represents a critical evidence gap. Future research must prioritize multi-institutional validation, stringent adherence to reporting standards (e.g., TRIPOD-AI), and the mandatory transparent reporting of both discrimination and calibration metrics to realize any potential clinical benefit. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/1305608, identifier CRD420261305608.