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.
A retrospective review of a single-site ankle arthritis database was conducted. All patients with current-generation ankle replacements including the Hintegra and Infinity prostheses implanted between 2004 and 2015 and with complete postoperative radiographs taken between 6 and 12 weeks postoperatively were included. Eight coronal and sagittal radiographic parameters were assessed and performed twice by 2 independent orthopedic surgeons on included TAAs. These radiographic parameters were then analyzed in association with patient demographics and PRO. Advanced statistical methods including survival analysis were used to construct a predictive model for TAA survival. A total of 107 patients were included and analyzed with a median clinical follow-up of 49 months (minimum 24 months).
A predictive model was created, with 4 parameters identified as being statistically associated with TAA metal-component revision: diabetes mellitus, poor baseline Ankle Osteoarthritis Scale (AOS) score, excessively dorsiflexed talar component, and an anteriorly/posteriorly translated talus relative to the tibial axis. The presence of 3 parameters predicted TAA survival of 0.60 whereas presence of all 4 parameters predicted survival of only 0.13 in the period studied.
Level III, retrospective cohort study using prospectively collected data.
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.