Computationally efficient prediction of bone–implant interface micromotion of a cementless tibial tray during gait

An Anterior Spike Decreases Bone-Implant Micromotion in Cementless Tibial Baseplates for Total Knee Arthroplasty: A Biomechanical Study

BACKGROUND

Cementless tibial baseplates in total knee arthroplasty include fixation features (eg, pegs, spikes, and keels) to ensure sufficient primary bone-implant stability. While the design of these features plays a fundamental role in biologic fixation, the effectiveness of anterior spikes in reducing bone-implant micromotion remains unclear. Therefore, we asked: Can an anterior spike reduce the bone-implant micromotion of cementless tibial implants?

METHODS

We performed computational finite element analyses on 13 tibiae using the computed tomography scans of patients scheduled for primary total knee arthroplasty. The tibiae were virtually implanted with a cementless tibial baseplate with 2 designs of fixation of the baseplate: 2 pegs and 2 pegs with an anterior spike. We compared the bone-implant micromotion under the most demanding loads from stair ascent between both designs.

RESULTS

Both fixation designs had peak micromotion at the anterior-lateral edge of the baseplate. The design with 2 pegs and an anterior spike had up to 15% lower peak micromotion and up to 14% more baseplate area with micromotions below the most conservative threshold for ingrowth, 20 μm, than the design with only 2 pegs. The greatest benefit of adding an anterior spike occurred for subjects who had the smallest area of tibial bone below the 20 μm threshold (ie, most at risk for failure to achieve bone ingrowth).

CONCLUSIONS

An anteriorly placed spike for cementless tibial baseplates with 2 pegs can help decrease the bone-implant micromotion during stair ascent, especially for subjects with increased bone-implant micromotion and risk for bone ingrowth failure.

A novel computational workflow to holistically assess total knee arthroplasty biomechanics identifies subject-specific effects of joint mechanics on implant fixation

Computational studies of total knee arthroplasty (TKA) often focus on either joint mechanics (kinematics and forces) or implant fixation mechanics. However, such disconnect between joint and fixation mechanics hinders our understanding of overall TKA biomechanical function by preventing identification of key relationships between these two levels of TKA mechanics. We developed a computational workflow to holistically assess TKA biomechanics by integrating musculoskeletal and finite element (FE) models. For our initial study using the workflow, we investigated how tibiofemoral contact mechanics affected the risk of failure due to debonding at the implant-cement interface using the four available subjects from the Grand Challenge Competitions to Predict In Vivo Knee Loads. We used a musculoskeletal model with a 12 degrees-of-freedom knee joint to simulate the stance phase of gait for each subject. The computed tibiofemoral joint forces at each node in contact were direct inputs to FE simulations of the same subjects. We found that the peak risk of failure did not coincide with the peak joint forces or the extreme tibiofemoral contact positions. Moreover, despite the consistency of joint forces across subjects, we observed important variability in the profile of the risk of failure during gait. Thus, by a combined evaluation of the joint and implant fixation mechanics of TKA, we could identify subject-specific effects of joint kinematics and forces on implant fixation that would otherwise have gone unnoticed. We intend to apply our workflow to evaluate the impact of implant alignment and design on TKA biomechanics.

Instantaneous Generation of Subject-Specific Finite Element Models of the Hip Capsule

Subject-specific hip capsule models could offer insights into impingement and dislocation risk when coupled with computer-aided surgery, but model calibration is time-consuming using traditional techniques. This study developed a framework for instantaneously generating subject-specific finite element (FE) capsule representations from regression models trained with a probabilistic approach. A validated FE model of the implanted hip capsule was evaluated probabilistically to generate a training dataset relating capsule geometry and material properties to hip laxity. Multivariate regression models were trained using 90% of trials to predict capsule properties based on hip laxity and attachment site information. The regression models were validated using the remaining 10% of the training set by comparing differences in hip laxity between the original trials and the regression-derived capsules. Root mean square errors (RMSEs) in laxity predictions ranged from 1.8° to 2.3°, depending on the type of laxity used in the training set. The RMSE, when predicting the laxity measured from five cadaveric specimens with total hip arthroplasty, was 4.5°. Model generation time was reduced from days to milliseconds. The results demonstrated the potential of regression-based training to instantaneously generate subject-specific FE models and have implications for integrating subject-specific capsule models into surgical planning software.

Can a Musculoskeletal Model Adapted to Knee Implant Geometry Improve Prediction of 3D Contact Forces and Moments?

Tibiofemoral contact loads are crucial parameters in the onset and progression of osteoarthrosis. While contact loads are frequently estimated from musculoskeletal models, their customization is often limited to scaling musculoskeletal geometry or adapting muscle lines. Moreover, studies have usually focused on superior-inferior contact force without investigating three-dimensional contact loads. Using experimental data from six patients with instrumented total knee arthroplasty (TKA), this study customized a lower limb musculoskeletal model to consider the positioning and the geometry of the implant at knee level. Static optimization was performed to estimate tibiofemoral contact forces and contact moments as well as musculotendinous forces. Predictions from both a generic and a customized model were compared to the instrumented implant measurements. Both models accurately predict superior-inferior (SI) force and abduction-adduction (AA) moment. Notably, the customization improves prediction of medial-lateral (ML) force and flexion-extension (FE) moments. However, there is subject-dependent variability in the prediction of anterior-posterior (AP) force. The customized models presented here predict loads on all joint axes and in most cases improve prediction. Unexpectedly, this improvement was more limited for patients with more rotated implants, suggesting a need for further model adaptations such as muscle wrapping or redefinition of hip and ankle joint centers and axes.

The influence of body weight index on initial stability of uncemented femoral knee protheses: A finite element study

Background and objective

Obesity is one of the risk factors for osteoarthritis. The end-stage treatment for osteoarthritis is total knee arthroplasty (TKA). However, it remains controversial whether a high body mass index (BMI) affects the initial stability of the femoral prosthesis after TKA. Finite element analysis (FEA) was used to investigate this question in this study.

Methods

Four femur models that assembled with TKA femoral components were reconstructed and divided into high BMI group and normal BMI group. The three-dimensional femurs were modeled and assigned inhomogeneous materials based on computed tomography (CT) images. Then each FEA model was applied with gait and deep bend loading conditions to evaluate the maximum principal strain on the distal femur and the relative micromotion between the femur and prosthesis.

Results

The mean strain of the high BMI group increased by 32.7% (936.9 με versus 706.1 με) and 50.9% (2064.5 με versus 1368.2 με) under gait and deep bend loading conditions, respectively, compared to the normal BMI group. Meanwhile, the mean micromotion of the high BMI group increased by 41.6% (2.77 μm versus 1.96 μm) and 58.5% (62.1 μm versus 39.2 μm), respectively. Under gait condition, the maximum micromotion for high BMI group was 33.8 μm and would compromise the initial stability. Under deep bend condition, the maximum strain and micromotion exceeded -7300 με and 28 μm for both groups.

Conclusion

High BMI caused higher strain on the bone and higher micromotion between the prosthesis and the femur. Gait activities could be risky for prosthesis stability in high BMI group while be safe in normal group. Deep bend activities were highly dangerous for both groups with high BMI and normal BMI and should be avoided.

Effect of varus alignment on the bone-implant interaction of a cementless tibial baseplate during gait

Component alignment in total knee arthroplasty is a determining factor for implant longevity. Mechanical alignment, which provides balanced load transfer, is the most common alignment strategy. However, a retrospective review found that varus alignment, which could lead to unbalanced loading, can happen in up to 18% of tibial baseplates. This may be particularly burdensome for cementless tibial baseplates, which require low bone-implant micromotion and avoidance of bone overload to obtain bone ingrowth. Our aim was to assess the effect of varus alignment on the bone-implant interaction of cementless baseplates. We virtually implanted 11 patients with knee OA with a modern cementless tibial baseplate in mechanical alignment and in 2° of tibial varus alignment. We performed finite element simulations throughout gait, with loading conditions derived from literature. Throughout the stance phase, varus alignment had greater micromotion and percentage of bone volume at risk of failure than mechanical alignment. At mid-stance, when the most critical conditions occurred, the average increase in peak micromotion and amount of bone at risk of failure due to varus alignment were 79% and 59%, respectively. Varus alignment also resulted in the decrease of the surface area with micromotion compatible with bone ingrowth. However, for both alignments, this surface area was larger than the average area of ingrowth reported for well-fixed implants retrieved post-mortem. Our findings suggest that small varus deviations from mechanical alignment can adversely impact the biomechanics of the bone-implant interaction for cementless tibial baseplates during gait; however, the clinical implications of such changes remain unclear.

Optimal surgical component alignment minimizes TKR wear - An in silico study with nine alignment parameters

Currently, preclinical mechanical wear testing of total knee replacements (TKRs) is done using ideally aligned components using standardized TKR level walking under either force or displacement-control regimes. To understand the influence of implant alignment and testing control regime, we studied the effect of nine component alignment parameters on TKR volumetric wear in silico. We used a computational framework combining Latin Hypercube sampling design of experiments, finite element analysis, and a numerical model of polyethylene wear, to create a predictive model of how component alignment affects wear rate for each control regime. Nine component alignment parameters were investigated, five femoral variables and four tibial variables. To investigate perturbations of the nine implant alignment variables, two separate 300-point designs were executed, one for each control regime. The results were then used to generate surrogate statistical models using stepwise multiple linear regression. Wear at the neutral position was 4.5mm3/million cycle and 8.6mm3/million cycle for displacement and force-control, respectively. Stepwise multiple linear regression surrogate models were highly significant for each control regime, but force-control generated a stronger predictive model, with a higher R2, more included terms, and a lower RMSE. Both models predicted transverse plane rotational mismatch can lead to large changes in predicted wear; a transverse plane alignment mismatch of 15° can elicit a change in wear of up to 5mm3/million cycle, almost double that of neutral alignment. Therefore, transverse plane alignment is particularly important when considering failure of the implant due to wear.

Micromotion and Migration of Cementless Tibial Trays Under Functional Loading Conditions

BACKGROUND

The outcome of cementless total knee arthroplasty (TKA) relies on successful bony ingrowth into the implant surfaces. Failures due to aseptic loosening are still reported, especially in younger and more active patients. The objective of this study is to quantify the micromotion of a commercially available design of cementless tibial tray under loading conditions simulating walking and stair descent.

METHOD

A commercially available design of cementless total knee arthroplasty was implanted in 7 cadaveric knees which were preconditioned with 500 cycles of 0°-100° flexion under a vertical load of 1050 N in a custom-built, multiaxial functional activity simulator. This was followed by application of the peak forces and moments occurring during walking and stair descent. During each loading procedure, 3-dimensional motion at the bone-prosthesis interface was measured using digital image correlation.

RESULTS

The tray migrated 101 ± 25 μm on average during preconditioning, which was dominated by rotation in the sagittal plane (92% of total migration), combined with posterior translation (28%) and minimal rotation in the transverse plane (14%). The migration varied 2.7-fold (61-167 μm) between the 6 measurement zones. Stair descent produced significantly higher total micromotion than walking in zone #5 (62 ± 9 vs 51 ± 10 μm, P < .05) and zone #6 (68 ± 17 vs 37 ± 10 μm, P < .05). In addition, during stair descent, the tray exhibited significantly more tilting (anterior zones: 31 ± 17 vs -16 ± 20 μm, P < .05; posterior zones: -60 ± 8 vs -40 ± 7 μm, P < .05) and more anteroposterior displacement in the anterior zones (-25 ± 3 vs -13 ± 2 μm, P < .05) when compared to walking.

CONCLUSION

The relative motion at the bone-prosthesis interface varied substantially around the periphery of the cementless tray. Under the loading conditions evaluated, the tray primarily underwent a rocking motion in the sagittal plane. Compared with walking, stair descent produced significantly more micromotion, especially in the posterior zones.

Biomechanical evaluation of total ankle arthroplasty. Part II: Influence of loading and fixation design on tibial bone-implant interaction

Finite element (FE) models to evaluate the burden placed on the interaction between total ankle arthroplasty (TAA) implants and the bone often rely on peak axial forces. However, the loading environment of the ankle is complex, and it is unclear whether peak axial forces represent a challenging scenario for the interaction between the implant and the bone. Our goal was to determine how the loads and the design of the fixation of the tibial component of TAA impact the interaction between the implant and the bone. To this end, we developed a framework that integrated robotic cadaveric simulations to determine the ankle kinematics, musculoskeletal models to determine the ankle joint loads, and FE models to evaluate the interaction between TAA and the bone. We compared the bone-implant micromotion and the risk of bone failure of three common fixation designs for the tibial component of TAA: spikes, a stem, and a keel. We found that the most critical conditions for the interaction between the implant and the bone were dependent on the specimen and the fixation design, but always involved submaximal forces and large moments. We also found that while the fixation design influenced the distribution and the peak value of bone-implant micromotion, the amount of bone at risk of failure was specimen dependent. To account for the most critical conditions for the interaction between the implant and the bone, our results support simulating multiple specimens under complex loading profiles that include multiaxial moments and span entire activity cycles.

Sensitivity of total knee replacement wear to variability in motion and load input: A parametric finite element analysis study

Polyethylene wear remains a contributor to long term failure in total knee replacements (TKRs). Advances in materials have improved polyethylene wear rates, therefore further wear reductions require a better understanding of patient-specific factors that lead to wear. Variability of gait within patients is considerable and could lead to significant variability in wear rates that cannot be predicted by standard testing methods. An in-silico study was performed to investigate the influence of gait variability on TKR polyethylene wear. Nine characteristic peaks within the load and motion profiles used for TKR wear testing were varied 75% to 125% from baseline (ISO-14243-3:2014) to generate 310 unique waveforms. Wear was calculated for 1-million cycles using a finite element TKR wear model. From the results, a surrogate model was developed using multiple linear regression, and used to predict how wear changes due to dispersion of motion and force peaks within a) ±5%, the maximum allowable input tolerance of ISO, and b) ±25%, more reflective of patient gait inter-variability. The range of wear within the ±5% tolerance was 0.65 mm³ /million cycles and was 3.24 mm³ /million cycles within the ±25% variability more in line with the dispersion observed within patients. Although no one kinematic or kinetic peak dominated variability in TKR volumetric wear, variability within flexion/extension peaks were the largest contributor to wear rate variability. Interaction between the peaks of different waveforms was also important. This study, and future studies incorporating patient-specific data, could help to explain the connection between patient-specific gait factors and wear rates.

Computational efficient method for assessing the influence of surgical variability on primary stability of a contemporary femoral stem in a cohort of subjects

Finite element (FE) modelling can provide detailed information on implant stability; however, its computational cost limits the possibility of completing large numerical analyses into the effect of surgical variability in a cohort of patients. The aim of this study was to develop an efficient surrogate model for a cohort of patients implanted using a common cementless hip stem. FE models of implanted femora were generated from computed tomography images for 20 femora (11 males, 9 females; 50-80 years; 52-116 kg). An automated pipeline generated FE models for 61 different unique scenarios that span the femur-specific range of implant positions. Peak hip contact and muscle forces for stair climbing were scaled to the donors' body weight and applied to the models. A cohort-specific surrogate for implant micromotion was constructed from Gaussian process models trained using data from FE simulations representing the median and extreme implant positions for each femur. A convergence study confirmed suitability of the sampling method for cohorts with 10+ femora. The final model was trained using data from the 20 femora. Results showed very good agreement between the FE and the surrogate predictions for a total of 1036 alignment scenarios [root mean squared error (RMSE) < 20 µm; [Formula: see text] = 0.81]. The total time required for the surrogate model to predict the micromotion range associated with surgical variability was approximately one-eighth of the corresponding full FE analysis. This confirms that the developed model is an accurate yet computationally cheaper alternative to full FE analysis when studying the implant robustness in a cohort of 10+ femora.

A novel training-free method for real-time prediction of femoral strain

Surrogate methods for rapid calculation of femoral strain are limited by the scope of the training data. We compared a newly developed training-free method based on the superposition principle (Superposition Principle Method, SPM) and popular surrogate methods for calculating femoral strain during activity. Finite-element calculations of femoral strain, muscle, and joint forces for five different activity types were obtained previously. Multi-linear regression, multivariate adaptive regression splines, and Gaussian process were trained for 50, 100, 200, and 300 random samples generated using Latin Hypercube (LH) and Design of Experiment (DOE) sampling. The SPM method used weighted linear combinations of 173 activity-independent finite-element analyses accounting for each muscle and hip contact force. Across the surrogate methods, we found that 200 DOE samples consistently provided low error (RMSE < 100 µε), with model construction time ranging from 3.8 to 63.3 h and prediction time ranging from 6 to 1236 s per activity. The SPM method provided the lowest error (RMSE = 40 µε), the fastest model construction time (3.2 h) and the second fastest prediction time per activity (36 s) after Multi-linear Regression (6 s). The SPM method will enable large numerical studies of femoral strain and will narrow the gap between bone strain prediction and real-time clinical applications.

Mechanical performance of cementless total knee replacements: It is not all about the maximum loads

Finite element (FE) models are frequently used to assess mechanical interactions between orthopedic implants and surrounding bone. However, FE studies are often limited by the small number of bones that are modeled; the use of normal bones that do not reflect the altered bone density distributions that result from osteoarthritis (OA); and the application of simplified load cases usually based on peak forces and without consideration of tibiofemoral kinematics. To overcome these limitations, we undertook an integrated approach to determine the most critical scenario for the interaction between an uncemented tibial component and surrounding proximal tibial bone. A cementless component, based on a modern design, was virtually implanted using computed-tomography scans from 13 patients with knee OA. FE simulations were performed across a demanding activity, stair ascent, by combining in vivo experimental forces from the literature with tibiofemoral kinematics measured from patients who had received the same design of knee component. The worst conditions for the bone-implant interaction, in terms of micromotion and percentage of interfacial bone mass at risk of failure, did not arise from the maximum applied loads. We also found large variability among bones and tibiofemoral kinematics sets. Our results suggest that future FE studies should not focus solely on peak loads as this approach does not consistently correlate to worst-case scenarios. Moreover, multiple load cases and multiple bones should be considered to best reflect variations in tibiofemoral kinematics, anatomy, and tissue properties. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:350-357, 2019.

Efficacy and efficiency of multivariate linear regression for rapid prediction of femoral strain fields during activity

Multivariate Linear Regression-based (MLR) surrogate models were explored to reduce the computational cost of predicting femoral strains during normal activity in comparison with finite element analysis. The musculoskeletal model of one individual, the finite-element model of the right femur, and experimental force and motion data for normal walking, fast walking, stair ascent, stair descent, and rising from a chair were obtained from a previous study. Equivalent Von Mises strain was calculated for 1000 frames uniformly distributed across activities. MLR surrogate models were generated using training sets of 50, 100, 200 and 300 samples. The finite-element and MLR analyses were compared using linear regression. The Root Mean Square Error (RMSE) and the 95th percentile of the strain error distribution were used as indicators of average and peak error. The MLR model trained using 200 samples (RMSE < 108 µε; peak error < 228 µε) was used as a reference. The finite-element method required 66 s per frame on a standard desktop computer. The MLR model required 0.1 s per frame plus 1848 s of training time. RMSE ranged from 1.2% to 1.3% while peak error ranged from 2.2% to 3.6% of the maximum micro-strain (5020 µε). Performance within an activity was lower during early and late stance, with RMSE of 4.1% and peak error of 8.6% of the maximum computed micro-strain. These results show that MLR surrogate models may be used to rapidly and accurately estimate strain fields in long bones during daily physical activity.

Finite element analysis of the tibial bone graft in cementless total knee arthroplasty

Background

Achieving stability of the tibial implant is essential following cementless total knee arthroplasty with bone grafting. We investigated the effects of bone grafting on the relative micromotion of the tibial implant and stress between the tibial implant and adjacent bone in the immediate postoperative period.

Methods

Tibial implant models were developed using a nonlinear, three-dimensional, finite element method. On the basis of a preprepared template, several bone graft models of varying sizes and material properties were prepared.

Results

Micromotion was larger in the bone graft models than in the intact model. Maximum micromotion and excessive stress in the area adjacent to the bone graft were observed for the soft and large graft models. With hard bone grafting, increased load transfer and decreased micromotion were observed.

Conclusions

Avoidance of large soft bone grafts and use of hard bone grafting effectively reduced micromotion and undue stress in the adjacent area.

Micro-CT based modelling for characterising injection-moulded porous titanium implants

Design of prosthetic implants to ensure rapid and stable osseointegration remains a significant challenge, and continuous efforts have been directed to new implant materials, structures and morphology. This paper aims to develop and characterise a porous titanium dental implant fabricated by metallic powder injection-moulding. The surface morphology of the specimens was first examined with a scanning electron microscope (SEM), followed by microscopic computerised tomography (μ-CT) scanning to capture its 3D microscopic features non-destructively. The nature of porosity and pore sizes were determined statistically. A homogenisation technique based on the Hills-energy theorem was adopted to evaluate its directional elastic moduli, and the conservation of mass theorem was employed to quantify the oxygen diffusivity for bio-transportation feature. This porous medium was found to have pore sizes varying from 50 to 400 µm and the average porosity of 46.90 ± 1.83%. The anisotropic principal elastic moduli were found fairly close to the upper range of cortical bone, and the directional diffusivities could potentially enable radial osseous tissue ingrowth and vascularisation. This porous titanium successfully reduces the elastic modulus mismatch between implant and bone for dental and orthopaedic applications, and provides improved capacity for transporting oxygen, nutrient and waste for pre-vascular network formation. Copyright © 2016 John Wiley & Sons, Ltd.

A Computational Efficient Method to Assess the Sensitivity of Finite-Element Models: An Illustration With the Hemipelvis

Assessing the sensitivity of a finite-element (FE) model to uncertainties in geometric parameters and material properties is a fundamental step in understanding the reliability of model predictions. However, the computational cost of individual simulations and the large number of required models limits comprehensive quantification of model sensitivity. To quickly assess the sensitivity of an FE model, we built linear and Kriging surrogate models of an FE model of the intact hemipelvis. The percentage of the total sum of squares (%TSS) was used to determine the most influential input parameters and their possible interactions on the median, 95th percentile and maximum equivalent strains. We assessed the surrogate models by comparing their predictions to those of a full factorial design of FE simulations. The Kriging surrogate model accurately predicted all output metrics based on a training set of 30 analyses (R2 = 0.99). There was good agreement between the Kriging surrogate model and the full factorial design in determining the most influential input parameters and interactions. For the median, 95th percentile and maximum equivalent strain, the bone geometry (60%, 52%, and 76%, respectively) was the most influential input parameter. The interactions between bone geometry and cancellous bone modulus (13%) and bone geometry and cortical bone thickness (7%) were also influential terms on the output metrics. This study demonstrates a method with a low time and computational cost to quantify the sensitivity of an FE model. It can be applied to FE models in computational orthopaedic biomechanics in order to understand the reliability of predictions.

Prediction of contact mechanics in metal-on-metal Total Hip Replacement for parametrically comprehensive designs and loads

Manufacturers and investigators of Total Hip Replacement (THR) bearings require tools to predict the contact mechanics resulting from diverse design and loading parameters. This study provides contact mechanics solutions for metal-on-metal (MoM) bearings that encompass the current design space and could aid pre-clinical design optimization and evaluation. Stochastic finite element (FE) simulation was used to calculate the head-on-cup contact mechanics for five thousand combinations of design and loading parameters. FE results were used to train a Random Forest (RF) surrogate model to rapidly predict the contact patch dimensions, contact area, pressures and plastic deformations for arbitrary designs and loading. In addition to widely observed polar and edge contact, FE results included ring-polar, asymmetric-polar, and transitional categories which have previously received limited attention. Combinations of design and load parameters associated with each contact category were identified. Polar contact pressures were predicted in the range of 0-200 MPa with no permanent deformation. Edge loading (with subluxation) was associated with pressures greater than 500 MPa and induced permanent deformation in 83% of cases. Transitional-edge contact (with little subluxation) was associated with intermediate pressures and permanent deformation in most cases, indicating that, even with ideal anatomical alignment, bearings may face extreme wear challenges. Surrogate models were able to accurately predict contact mechanics 18,000 times faster than FE analyses. The developed surrogate models enable rapid prediction of MoM bearing contact mechanics across the most comprehensive range of loading and designs to date, and may be useful to those performing bearing design optimization or evaluation.