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Murray-Darling Freshwater Research CentreMDFRC item.

Radiolucent lines frequently appear on radiographs around unicompartmental knee arthroplasties. It is unknown why this occurs, although the lines usually appear during the first year following the procedure. Knee arthroplasty implants are also known to migrate during the first year. Considering the similarity in the timing of appearance of radiolucency and implant migration, it seems that they could be related. The aim of this study was to determine whether there is a correlation between presence of radiolucency and the migration of Oxford unicompartmental knee arthroplasty implants.

Finite element (FE) analysis is widely used to calculate stresses and strains within human bone in order to improve implant designs. Although validated FE models of the human femur have been created (Lengsfeld et al., 1998), no equivalent yet exists for the tibia. The aim of this study was to create such an FE model, both with and without the tibial component of a knee replacement, and to validate it against experimental results. A set of reference axes was marked on a cleaned, fresh frozen cadaveric human tibia. Seventeen triaxial stacked strain rosettes were attached along the bone, which was then subjected to nine axial loading conditions, two four-point bending loading conditions, and a torsional loading condition using a materials testing machine (MTS 858). Deflections and strain readings were recorded. Axial loading was repeated after implantation of a knee replacement (medial tibial component, Biomet Oxford Unicompartmental Phase 3). The intact tibia was CT scanned (GE HiSpeed CT/i) and the images used to create a 3D FE mesh. The CT data was also used to map 600 transversely isotropic material properties (Rho, 1996) to individual elements. All experiments were simulated on the FE model. Measured principal strains and displacements were compared to their corresponding FE values using regression analysis. Experimental results were repeatable (mean coeffi-cients of variation for intact and implanted tibia, 5.3% and 3.9%). They correlated well with those of the FE analysis (R squared = 0.98, 0.97, 0.97, and 0.99 for axial (intact), axial (implanted), bending, torsional loading). For each of the load cases the intersects of the regression lines were small in comparison to the maximum measured strains (

There has been recent renewed interest in proximal femur epiphyseal replacement as an alternative to conventional total hip replacement. In many branches of engineering, risk analysis has proved to be an efficient tool for avoiding premature failures of innovative devices. An extensive risk analysis procedure has been developed for epiphyseal hip prostheses and the predictions of this method have been compared to the known clinical outcomes of a well-established contemporary design, namely hip resurfacing devices.Clinical scenarios leading to revision (i.e. loosening, neck fracture and failure of the prosthetic component) were associated with potential failure modes (i.e. overload, fatigue, wear, fibrotic tissue differentiation and bone remodelling). Driving parameters of the corresponding failure mode were identified together with their safe thresholds. For each failure mode, a failure criterion was identified and studied under the most relevant physiological loading conditions. All failure modes were investigated with the most suitable investigation tool, either numerical or experimental.Results showed a low risk for each failure scenario either in the immediate postoperative period or in the long term. These findings are in agreement with those reported by the majority of clinical studies for correctly implanted devices. Although further work is needed to confirm the predictions of this method, it was concluded that the proposed risk analysis procedure has the potential to increase the efficacy of preclinical validation protocols for new epiphyseal replacement devices.

Finite element (FE) models of long bones are widely used to analyze implant designs. Experimental validation has been used to examine the accuracy of FE models of cadaveric femurs; however, although convergence tests have been carried out, no FE models of an intact and implanted human cadaveric tibia have been validated using a range of experimental loading conditions. The aim of the current study was to create FE models of a human cadaveric tibia, both intact and implanted with a unicompartmental knee replacement, and to validate the models against results obtained from a comprehensive set of experiments. Seventeen strain rosettes were attached to a human cadaveric tibia. Surface strains and displacements were measured under 17 loading conditions, which consisted of axial, torsional, and bending loads. The tibia was tested both before and after implantation of the knee replacement. FE models were created based on computed tomography (CT) scans of the cadaveric tibia. The models consisted of ten-node tetrahedral elements and used 600 material properties derived from the CT scans. The experiments were simulated on the models and the results compared to experimental results. Experimental strain measurements were highly repeatable and the measured stiffnesses compared well to published results. For the intact tibia under axial loading, the regression line through a plot of strains predicted by the FE model versus experimentally measured strains had a slope of 1.15, an intercept of 5.5 microstrain, and an R2 value of 0.98. For the implanted tibia, the comparable regression line had a slope of 1.25, an intercept of 12.3 microstrain, and an R2 value of 0.97. The root mean square errors were 6.0% and 8.8% for the intact and implanted models under axial loads, respectively. The model produced by the current study provides a tool for simulating mechanical test conditions on a human tibia. This has considerable value in reducing the costs of physical testing by pre-selecting the most appropriate test conditions or most favorable prosthetic designs for final mechanical testing. It can also be used to gain insight into the results of physical testing, by allowing the prediction of those variables difficult or impossible to measure directly.