The aim of this study has been to develop a dynamic model of the knee joint after total knee replacement (TKR) to analyse the stress distribution in the distal femur during daily activities. Using MSC/ADAMS and MSC/MARC software, a dynamic model of an implanted knee joint has been developed. This model consists of the components of the knee prosthesis as well as the bones and ligaments of the knee. The femur, tibia, fibula, and patella have been modelled as mixed cortico-cancellous bone. The distal part of femur has been modelled as a flexible body with springs used to simulate the ligaments positioned at their anatomical insertion points. With this dynamic model a gait cycle was simulated. Stress shielding was identified in the distal femur after TKR, which is consistent with other investigators' results. Interestingly, higher stresses were found in the bone adjacent to the femoral component peg. This dynamic model can now be used to analyse the stress distribution in the distal femur with different load conditions. This will help to improve implant designs and will allow comparison of prostheses from different manufacturers.

Two optimization techniques, static optimization (SO) and computed muscle control (CMC), are often used in OpenSim to estimate the muscle activations and forces responsible for movement. Although differences between SO and CMC muscle function have been reported, the accuracy of each technique and the combined effect of optimization and model choice on simulated muscle function is unclear. The purpose of this study was to quantitatively compare the SO and CMC estimates of muscle activations and forces during gait with the experimental data in the Gait2392 and Full Body Running models. In OpenSim (version 3.1), muscle function during gait was estimated using SO and CMC in 6 subjects in each model and validated against experimental muscle activations and joint torques. Experimental and simulated activation agreement was sensitive to optimization technique for the soleus and tibialis anterior. Knee extension torque error was greater with CMC than SO. Muscle forces, activations, and co-contraction indices tended to be higher with CMC and more sensitive to model choice. CMC’s inclusion of passive muscle forces, muscle activation-contraction dynamics, and a proportional-derivative controller to track kinematics contributes to these differences. Model and optimization technique choices should be validated using experimental activations collected simultaneously with the data used to generate the simulation.

Instability after total knee arthroplasty (TKA) can lead to suboptimal outcomes and revision surgery. Medially-stabilized implants aim to more closely replicate normal knee motion than other implants following TKA, but no study has investigated knee laxity (motion under applied loads) and balance (i.e., difference in varus/valgus motion under load) following medially-stabilized TKA. The primary purposes of this study were to investigate how medially-stabilized implants change knee laxity in non-arthritic, cadaveric knees, and if it produces a balanced knee after TKA. Force-displacement data were collected on 18 non-arthritic cadaveric knees before and after arthroplasty using medially-stabilized implants. Varus-valgus and anterior-posterior laxity and varus-valgus balance were compared between native and medially-stabilized knees at 0˚, 20˚, 60˚, and 90˚under three different loading conditions. Varus-valgus and anterior-posterior laxities were not different between native and medially-stabilized knees under most testing conditions (p ! 0.068), but differences of approximately 2˚less varus-valgus laxity at 20˚of flexion and 4 mm more anterior-posterior laxity at 90ẘ ere present from native laxities (p 0.017) Medially-stabilized implant balance had 1.5˚varus bias at all flexion angles. Future studies should confirm if the consistent laxity afforded by the medially-stabilized implant is associated with better and more predictable postoperative outcomes. ß

Background: Achieving a stable joint is an important yet challenging part of total knee arthroplasty (TKA). Neither manual manipulation of the knee nor instrumented sensors biomechanically characterize knee laxity or objectively characterize how TKA changes the laxity of an osteoarthritic (OA) knee. Therefore, the purposes of this study were: 1) objectively characterize changes in knee laxity due to TKA, 2) objectively determine whether TKA resulted in equal amounts of varus-valgus motion under a given load (i.e., balance) and 3) determine how TKA knee laxity and balance differ from values seen in non-osteoarthritic knees. Methods:Two surgeons used a custom navigation system and intra-operative device to record varus-valgus motion under quantified loads in a cohort of 31 patients (34 knees ) undergoing primary TKA. Similar data previously were collected from a cohort of 42 native cadaveric knees. Results: Performing a TKA resulted in a "looser knee" on average, but great variability existed within and between surgeons. Under the maximum applied moment, 20 knees were "looser" in the varus-valgus direction, while 14 were "tighter". Surgeon 1 generally "loosened" knees (OA laxity 6.1°±2.3°, TKA laxity 10.1°±3.6°), while Surgeon 2 did not substantially alter knee laxity (OA laxity 8.2°±2.4°, TKA laxity 7.5°±3.3°). TKA resulted in balanced knees, and, while several differences in laxity were observed between OA, TKA, and cadaveric knees, balance was only different under the maximum load between OA and cadaveric knees. Conclusions: Large variability exists within and between surgeons suggests in what is considered acceptable laxity and balance of the TKA knee when it is assessed by only manual manipulation of the leg. Knees were "balanced" yet displayed different amounts of motion under applied load. Clinical Relevance: Our results suggest that current assessments of knee laxity may leave different patients with biomechanically different knees. Objective intra-operative measurements should inform surgical technique to ensure consistency across different patients.