Clinical Report: Comprehensive Review and Meta-Analysis of Machine Learning Models for Predicting Readmission Risk Following Total Hip and Knee Arthroplasty

Overview

This study systematically reviews and quantitatively synthesizes machine learning models for predicting readmission risk after total hip and knee arthroplasty (THA/TKA). The findings reveal a pooled C-statistic of 0.76, but highlight significant heterogeneity and high methodological bias across studies.

Background

Postoperative readmission is a critical metric for evaluating the efficacy of total hip and knee arthroplasty. Machine learning models have emerged as potential tools for predicting readmission risk, yet their performance and methodological robustness remain contentious.

Data Highlights

The pooled C-statistic for the included studies was 0.76 (95% CI: 0.71–0.81), indicating moderate predictive ability, but with extreme heterogeneity (I2 = 99.9%).

Key Findings

• The pooled C-statistic for machine learning models predicting readmission after THA/TKA was 0.76.
• Single-center models showed higher performance (C-statistic 0.86) compared to multicenter models (C-statistic 0.65).
• THA-specific models yielded higher C-statistics than TKA-specific models.
• Advanced machine learning algorithms did not consistently outperform traditional logistic regression.
• High risk of bias was identified in the majority of studies due to analytical shortcomings.
• The inability to pool calibration metrics represents a critical evidence gap.

Clinical Implications

The findings indicate that while machine learning models show promise for predicting readmission risk, their current heterogeneity and bias limit their clinical utility. Future research should focus on multi-institutional validation and adherence to reporting standards to enhance model reliability.

Conclusion

The evidence for machine learning-based readmission prediction after THA/TKA is characterized by significant variability and methodological concerns.

Related Resources & Content

1. Knee Surgery, Sports Traumatology, Arthroscopy, 2021 -- The Role of Machine Learning in Knee Arthroplasty: Importance of Targeted Data—A Comprehensive Review
2. Frontiers in Surgery, 2026 -- From data to decisions: machine learning in predicting outcomes of robotic-assisted total knee arthroplasty
3. Knee Surgery, Sports Traumatology, Arthroscopy, 2022 -- Assessing the Limited Effectiveness of a Machine Learning Model for Predicting Revision Surgery in Hip Arthroscopy Using National Registry Data
4. Knee Surgery, Sports Traumatology, Arthroscopy, 2022 -- Accurate Prediction of Surgical Duration and Complications in Primary Total Knee Arthroplasty Using Machine Learning with Arthroplasty-Specific Data
5. Hospital Readmissions Reduction Program | CMS -- Medicare’s Hospital Readmissions Reduction Program
6. Outpatient | AAHKS -- Position Statement on Outpatient Joint Replacement
7. Frontiers, 2026 -- Systematic review and meta-analysis of machine learning-based prediction models for readmission risk after total hip and knee arthroplasty
8. Hospital Readmissions Reduction Program | CMS
9. Outpatient | AAHKS
10. Frontiers | Systematic review and meta-analysis of machine learning-based prediction models for readmission risk after total hip and knee arthroplasty