Minimising tibial fracture after unicompartmental knee replacement: A probabilistic finite element study

• Published in: Clinical biomechanics (Bristol) 2020-03, Vol.73, p.46-54
• Main Authors: Pegg, Elise C., Walter, Jonathan, D'Lima, Darryl D., Fregly, Benjamin J., Gill, Harinderjit S., Murray, David W.
• Format: Article
• Language: English
• Subjects: Arthroplasty, Replacement, Knee - adverse effects; Bone; Female; Finite element; Finite Element Analysis; Fracture; Humans; Knee; Knee Prosthesis - adverse effects; Male; Middle Aged; Postoperative Complications - etiology; Postoperative Complications - prevention & control; Probability; Rotation; Tibial Fractures - etiology; Tibial Fractures - prevention & control; Unicompartmental
• ISSN: 0268-0033; 1879-1271
• DOI: 10.1016/j.clinbiomech.2019.12.014

Periprosthetic tibial fracture after unicompartmental knee replacement is a challenging post-operative complication. Patients have an increased risk of mortality after fracture, the majority undergo further surgery, and the revision operations are less successful. Inappropriate surgical technique increases the risk of fracture, but it is unclear which technical aspects of the surgery are most problematic and no research has been performed on how surgical factors interact. Firstly, this study quantified the typical variance in surgical cuts made during unicompartmental knee replacement (determined from bones prepared by surgeons during an instructional course). Secondly, these measured distributions were used to create a probabilistic finite element model of the tibia after replacement. A thousand finite element models were created using the Monte Carlo method, representing 1000 virtual operations, and the risk of tibial fracture was assessed. Multivariate linear regression of the results showed that excessive resection depth and making the vertical cut too deep posteriorly increased the risk of fracture. These two parameters also had high variability in the prepared synthetic bones. The regression equation calculated the risk of fracture from three cut parameters (resection depth, vertical and horizonal posterior cuts) and fit the model results with 90% correlation. This study introduces for the first time the application of a probabilistic approach to predict the aetiology of fracture after unicompartmental knee replacement, providing unique insight into the relative importance of surgical saw cut variations. Targeted changes to operative technique can now be considered to seek to reduce the risk of periprosthetic fracture. •1000 probabilistic models created with varying unicompartmental surgical cuts.•Tibia fracture risk increased for excessive resection or posterior vertical cut depth.•Regression fitted finite element results with 90% correlation using 3 cut parameters.•Predicted instrumentation modifications may reduce the likelihood of fracture.