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Updated: Nov 26, 2025
Treatment of Ankle Osteoarthritis with Total Ankle Replacement Through a Lateral Transfibular Approach
Published on: January 24, 2018
Clinical Decision Support Tools for Predicting Outcomes in Patients Undergoing Total Knee Arthroplasty: A Systematic
Jodie A Cochrane1, Traci Flynn2, Adrian Wills3
1School of Engineering, The University of Newcastle, Callaghan, New South Wales, Australia; Centre for Rehab Innovations, The University of Newcastle, Callaghan, New South Wales, Australia; Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia.
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.
