Aims. Revision rates for ankle arthroplasties are higher than hip or knee arthroplasties. When a total ankle arthroplasty (TAA) fails, it can either undergo revision to another ankle replacement, revision of the TAA to ankle arthrodesis (fusion), or amputation. Currently there is a paucity of literature on the outcomes of these revisions. The aim of this meta-analysis is to assess the outcomes of revision TAA with respect to surgery type, functional outcomes, and reoperations. Methods. A systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Medline, Embase, Cinahl, and Cochrane reviews were searched for relevant papers. Papers analyzing surgical treatment for failed ankle arthroplasties were included. All papers were reviewed by two authors. Overall, 34 papers met the inclusion criteria. A meta-analysis of proportions was performed. Results. Six papers analyzed all-cause reoperations of revision ankle arthroplasties, and 14 papers analyzed failures of conversion of a TAA to fusion. It was found that 26.9% (95% confidence interval (CI) 15.4% to 40.1%) of revision ankle arthroplasties required further surgical intervention and 13.0% (95% CI 4.9% to 23.4%) of conversion to fusions; 14.4% (95% CI 8.4% to 21.4%) of revision ankle arthroplasties failed and 8% (95% CI 4% to 13%) of conversion to fusions failed. Conclusion. Revision of primary TAA can be an effective procedure with improved functional outcomes, but has considerable risks of failure and reoperation, especially in those with periprosthetic joint infection. In those who undergo conversion of TAA to fusion, there are high rates of nonunion. Further comparative studies are required to compare both operative techniques. Cite this article: Bone Jt Open 2022;3(7):596–606

Aims

Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.