Abstract
Femoral knee implants have promising outcomes, although some high-flex designs have shown rather high loosening rates (Han et al., 2007). In uncemented implants, it is vital to limit micromotions at the implant-bone interface, to facilitate secondary fixation through bone ingrowth (kienapfel et al., 1999). Hence, it is essential to investigate how micromotions of different uncemented implants are affected by various loading conditions when a range of bone qualities as a patient-related factor is applied.
Using finite element (FE) analysis, we simulated implant-bone interface micromotions during four consecutive cycles of normal gait and squat movements. An FE model of a distal femur was generated based on calibrated CT-scans, after which Sigma® and LCS® Cruciate-Retaining Porocoat® components (DePuy Synthes, Leeds, UK) were implanted. Using a frictional contact algorithm (µ=0.95), an initial press-fit fixation was simulated, which was previously validated against experimental data. The micromotions were calculated by tracking the projection of implant nodes on the bone surface excluding overhang area. The applied loading patterns were based on discretized simulations, providing incremental loads for each activity based on implant-specific kinematics, which was derived from Orthoload database using inverse dynamics (Fitzpatrick et al., 2012). This provided the opportunity to calculate incremental micromotions, but also the resulting micromotions for each single cycle, for both activities. In addition, the percentage of implant surface area with resulting micromotions less than a defined threshold was calculated.
Regardless of the type of loading, in all simulations, the predicted micromotions were highest in the first cycle, suggesting settling of the implant during initial cycle. The Sigma®implant displayed a 30% larger area with micromotions below the threshold of 5 microns, for both loading conditions (Fig. 1A). The highest micromotions occurred at the anterior flange, regardless of type of activity or design. Squatting had a more detrimental effect on the primary stability, with smaller areas of low micromotions as compared to the gait load (Fig. 1B). Bone stiffness had a minor effect, which was more apparent for squatting (Fig. 1B).
We found acceptable low ranges of micromotions in both implant designs, although demanding activities such as squatting generated higher motions. In addition, LCS® experienced higher micromotions, probably caused by the smaller contact area at bone-implant interface compared with Sigma®. Nevertheless, the predicted micromotions were all below the clinically relevant threshold for bone ingrowth (<40 microns) (kienapfel et al., 1999). Furthermore, our simulated settling behavior stresses the necessity for simulating multiple loading cycles, rather than just a single cycle. The effect of bone stiffness was evident, but only to a limited extent. The main current limitation of our study is the utilization of an elastic material model for the bone which is probably the reason to predict a low range of micromotions. We are planning to make the material model more realistic, by including plasticity and viscoelastic bone behavior.
