Different internal fixation methods for Hoffa-like fractures of the tibial plateau: a finite element analysis

Due to the low incidence of posteromedial tibial plateau fractures and limited clinical data available, the optimal treatment for this type of fracture remains to be established. This type of fracture, also known as Hoffa-like fracture of the tibial plateau, shares a similar mechanism of injury with the Hoffa fracture of the femoral condyle. In the field of orthopedics, finite element analysis is considered a valuable method to guide clinical decision-making. In this study, four methods used for internal fixation of Hoffa-like fractures of the tibial plateau were compared using computer simulation and applying a finite element method (FEM). The methods compared were lateral L-plate fixation alone (Model A); lateral L-plate combined with posterior anti-slip plate (reconstruction plate/T-plate) fixation (Model B); lateral L-plate combined with posterior hollow nail fixation of the fracture block (Model C); and lateral L-plate combined with anterior hollow nail fixation of the fracture (Model D). The maximum displacement of the model and the maximum stress of the internal fixation material were analyzed by applying an axial load of 2,500 N. The results showed that, in the normal bone model, the maximum displacement of the fracture in Model A was 0.60032 mm, with improved stability through the addition of posterior lateral plate fixation in Model B and reduction of the displacement to 0.38882 mm. The maximum displacement in Model C and Model D was comparable, amounting to 0.42345 mm and 0.42273 mm, respectively. Maximum stress was 1235.6 MPa for Model A, 84.724 MPa for Model B, 99.805 MPa for Model C, and 103.19 MPa for Model D. In the internal fixation analysis of the osteoporotic fracture model, we observed patterns similar to the results of the normal bone model. The results indicated that Model B yielded the overall best results in the treatment of Hoffa-like fractures of the tibial plateau. The orthopedic surgeon may wish to implement these insights into the perioperative algorithm, thereby refining and optimizing clinical patient care. In addition, our findings pave the way for future research efforts.