Clinical Decision Support Tools for Predicting Outcomes in Patients Undergoing Total Knee Arthroplasty: A Systematic Review

Background: Total knee arthroplasty is the standard surgical treatment for end-stage osteoarthritis. Although widely accepted as a successful procedure, approximately 30% of patients are not satisfied due to non-optimal postoperative outcomes. Clinical decision support tools that are able to accurately predict post-surgery outcomes would assist in providing individualized advice or services to help alleviate possible issues, resulting in significant benefits to both the healthcare system and individuals. Methods: Five databases (Ovid Medline, Ovid EMBASE, CINAHL complete, Cochrane Library, and Scopus) were searched for the key phrases knee replacement or knee arthroplasty and decision support tool, decision tool, predict* tool, predict* model, algorithm or nomogram. Searches were limited to peer-reviewed journal articles published between January 2000 and June 2019. Reference lists of included articles were examined. Authors came to a consensus on the final list of included articles. Results: Eighteen articles were included for review. Most models reported low predictive success and inability to externally validate. Both candidate and final predictor variables were inconsistent between studies. Only 1 model was considered strongly predictive (AUROC >0.8), and only 2 studies were able to externally validate their developed model. In general, models that performed well used large patient numbers, were tested on similar demographics, and used either nonlinear input transformations or a completely nonlinear model. Conclusion: Some models do show promise; however, there remains the question of whether the reported predictive success can continue to be replicated. Furthermore, clinical applicability and interpretation of predictive tools should be considered during development. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

The majority of models reported low predictive success rates and inability to externally validate, with only a few performing well using large sample sizes, similar demographics, and nonlinear models. The question of replicating reported predictive success and the challenge of model consistency and external validation remain.