Abstract
Background:
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.
Methods:
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.
Findings:
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.
Interpretation:
This study introduces the application of a probabilistic approach to predict the aetiology of fracture after unicompartmental knee replacement and has quantified the potential importance of surgical saw cut variations for the first time. Targeted changes to operative technique can now be considered to seek to reduce the risk of periprosthetic fracture.
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.
Methods:
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.
Findings:
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.
Interpretation:
This study introduces the application of a probabilistic approach to predict the aetiology of fracture after unicompartmental knee replacement and has quantified the potential importance of surgical saw cut variations for the first time. Targeted changes to operative technique can now be considered to seek to reduce the risk of periprosthetic fracture.
| Original language | English |
|---|---|
| Pages (from-to) | 46-54 |
| Number of pages | 9 |
| Journal | Clinical Biomechanics |
| Volume | 73 |
| Early online date | 18 Dec 2019 |
| DOIs | |
| Publication status | Published - 1 Mar 2020 |
Keywords
- Knee
- Bone
- Fracture
- Unicompartmental
- Finite Element
Cite this
Pegg, E., Walter, J., D'Lima, D. D., Fregly, B. J., Gill, R., & Murray, D. W. (2020). Minimising Tibial Fracture after Unicompartmental Knee Replacement: A Probabilistic Finite Element Study. Clinical Biomechanics, 73, 46-54. https://doi.org/10.1016/j.clinbiomech.2019.12.014