Risk Factors for Readmission After Knee Arthroplasty Based on Predictive Models: A Systematic Review

• Main Authors: Satish M. Mahajan, PhD, MStat, MEng, RN, Chantal Nguyen, BS, Justin Bui, BS, Enomwoyi Kunde, MSN, RN, Bruce T. Abbott, MLS, Amey S. Mahajan, BS, BA
• Format: Article
• Language: English
• Published: Elsevier 2020-09-01
• Series: Arthroplasty Today
• Subjects: Total knee arthroplasty; Patient readmission; Risk factors; Statistical models
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352344120300819

Background: An increase in the aging yet active US population will continue to make total knee arthroplasty (TKA) procedures routine in the coming decades. For such joint procedures, the Centers for Medicare and Medicaid Services introduced programs such as the Comprehensive Care for Joint Replacement to emphasize accountable and efficient transitions of care. Accordingly, many studies have proposed models using risk factors for predicting readmissions after the procedure. We performed a systematic review of TKA literature to identify such models and risk factors therein using a reliable appraisal tool for their quality assessment. Methods: Five databases were searched to identify studies that examined correlations between post-TKA readmission and risk factors using multivariate models. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology and Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis criteria established for quality assessment of prognostic studies. Results: Of 29 models in the final selection, 6 models reported performance using a C-statistic, ranging from 0.51 to 0.76, and 2 studies used a validation cohort for assessment. The average 30-day and 90-day readmission rates across the studies were 5.33% and 7.12%, respectively. Three new significant risk factors were discovered. Conclusions: Current models for TKA readmissions lack in performance measurement and reporting when assessed with established criteria. In addition to using new techniques for better performance, work is needed to build models that follow the systematic process of calibration, external validation, and reporting for pursuing their deployment in clinical settings.