Total ankle arthroplasty (TAA) is an increasingly selected treatment for end-stage ankle arthritis; however, failure and revision of the tibial and talar components remains an issue. Although multiple risk factors have been shown to contribute to early component revision, no study has looked at combining such risk factors into a predictive model that could potentially decrease revision rates and improve implant survival. This study aimed to develop a predictive model for TAA failure based on patient characteristics, patient-reported outcomes (PROs), and immediate postoperative radiographs.

Our predictive model is based on a combination of patient factors, PROs, and radiographic TAA alignment. We believe it can be used by surgeons to predict failure in their TAA patients, thereby optimizing postoperative outcomes by improving patient selection and modifiable outcome-specific parameters.

 

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