Abstract
Total knee replacement (TKR) is one of the most performed orthopedic surgeries to treat knee joint diseases in the elderly population. Although the survivorship of knee implants may extend beyond two decades, the poor outcome rate remains considerable. A recent computational approach used to better understand failure modes and improve TKR outcomes is based on the combination of musculoskeletal (MSK) and finite element models. This combined multiscale modeling approach is a promising strategy in the field of computational biomechanics; however, some critical aspects need to be investigated. In particular, the identification and quantification of the uncertainties related to the boundary conditions used as inputs to the finite element model due to a different definition of the MSK model are crucial. Therefore, the aim of this study is to investigate this problem, which is relevant for the model credibility assessment process. Three different generic MSK models available in the OpenSim platform were used to simulate gait, based on the experimental data from the fifth edition of the “Grand Challenge Competitions to Predict in vivo Knee Loads.” The outputs of the MSK analyses were compared in terms of relative kinematics of the knee implant components and joint reaction (JR) forces and moments acting on the tibial insert. Additionally, the estimated knee JRs were compared with those measured by the instrumented knee implant so that the “global goodness of fit” was quantified for each model. Our results indicated that the different kinematic definitions of the knee joint and the muscle model implemented in the different MSK models influenced both the motion and the load history of the artificial joint. This study demonstrates the importance of examining the influence of the model assumptions on the output results and represents the first step for future studies that will investigate how the uncertainties in the MSK models propagate on disease-specific finite element model results.
Highlights
Total knee replacement (TKR) surgeries are commonly performed to alleviate severe pain at the knee joint resulting from musculoskeletal (MSK) disorders/conditions that mostly affect elderly patients
The variation range (VR) was lower than 0.9◦ for the three Euler angles and 1 mm for the ML translation, while maximum VR values equal to 4.65 mm and 7.72 mm were observed during the stance phase in the AP translation and during the swing phase for the SI translation, respectively (Table 2)
The aim of this study was to compare the simulation results obtained from three different generic MSK models, scaled to fit the patient of the Fifth Knee Grand Challenge (KGC), in terms of TKR kinematics and knee joint force during level walking which can be used as boundary conditions in a coupled finite element model when scaled to fit the patient of the Fifth KGC (Fregly et al, 2012)
Summary
Total knee replacement (TKR) surgeries are commonly performed to alleviate severe pain at the knee joint resulting from musculoskeletal (MSK) disorders/conditions (e.g., inflammatory arthritis) that mostly affect elderly patients In recent times, this is considered an effective procedure in orthopedics with a failure rate at 10 years postoperatively lower than 5% (Khan et al, 2016). A multiscale forward-dynamic framework of the lower extremity that combined muscle modeling and deformable FEA was presented in the study by Hume et al (2019) This approach was used to predict healthy joint mechanics during different physical activities, and it is considered a promising strategy for the preclinical evaluation and design of TKR
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