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

Development of a bespoke finite element wear algorithm to investigate the effect of femoral centre of rotation on the wear evolution in total knee replacements

Total Knee Replacements (TKRs) are a commonly used treatment to help patients suffering from severely damaged knee joints, which is normally brought on by osteoarthritis. The aim of the surgery is to reduce pain and regain function of the joint, however, some of these implants fail prematurely with implant wear being one of the main factors of failure. Computational analysis is an efficient tool that can provide an in-depth insight on the evolution of wear, before utilising experimental techniques which are time-consuming and costly. In this study, a bespoke finite element (FE) based wear algorithm has been further developed for TKRs and was used to investigate how location of femoral centre of rotation (CoR) affects the evolution of wear at the bearing surfaces. Three locations of femoral CoR have been investigated: international standards (ISO) CoR, being the location defined in ISO 14243-3, distal CoR being the centre of the femoral component's distal radius, and reference CoR being the middle ground between the two. All investigations were setup in accordance with ISO 14243-3 for displacement-controlled wear testing conditions for knee simulators. The wear algorithm extracts contact pressure and sliding distance from the FE analysis to determine wear depth, wear pattern, volumetric wear, and wear rates on the polymeric insert and femoral component's bearing surfaces using Archard's wear law. The polymeric insert volumetric wear rate after 5 million cycles (Mc) for ISO, reference, and distal CoR are 4.37mm3/Mc, 5.40mm3/Mc, and 6.83mm3/Mc respectively. Furthermore, the wear pattern's location on the bearing surfaces is dependent on the femoral CoR, with ISO CoR wear pattern being positioned more posteriorly, distal CoR being more anteriorly, and reference CoR in between ISO and distal. The ISO CoR investigation showed a region of minimal wear between two wear regions at the middle of the femoral component's wear pattern, on both medial and lateral condyles. This region of minimal wear reduces for the reference CoR and further reduces for the distal CoR. After 5 Mc, the average polymeric insert-femoral component contact area changes with femoral CoR, with the average contact area being 66.53mm2, 68.35mm2, and 71.21mm2 for ISO, reference, and distal CoRs respectively, with distal having around 7% more contact area than ISO. The results from this study show that there is a wide range of wear values for different locations of femoral CoR. As such the choice of femoral CoR should be carefully considered when performing any wear investigation to ensure that the CoR location is consistent for all studies being compared.

Computational Lower Limb Simulator Boundary Conditions to Reproduce Measured TKA Loading in a Cohort of Telemetric Implant Patients

Recent advancements in computational modeling offer opportunities to refine total knee arthroplasty (TKA) design and treatment strategies. This study developed patient-specific simulator external boundary conditions (EBCs) using a PID-controlled lower limb finite element (FE) model. Calibration of the external actuation required to achieve measured patient-specific joint loading and motion was completed for nine patients with telemetric implants during gait, stair descent, and deep knee bend. The study also compared two EBC scenarios: activity-specific hip AP motion and pelvic rotation (that was averaged across all patients for an activity) and patient-specific hip AP motion and pelvic rotation. Including patient-specific data significantly improved reproduction of joint-level loading, reducing root mean squared error between the target and achieved loading by 28.7% and highlighting the importance of detailed patient data in replicating joint kinematics and kinetics. The principal component analysis (PCA) of the EBCs for the patient dataset showed that one component represented 77.8% of the overall variation, while the first three components represented 97.8%. Given the significant loading variability within the patient cohort, this group of patient-specific models can be run individually to provide insight into expected TKA mechanics variability, and the PCA can be utilized to further create reasonable EBCs that expand the variability evaluated.

Implementing Machine Learning approaches for accelerated prediction of bone strain in acetabulum of a hip joint

The Finite Element (FE) methods for biomechanical analysis involving implant design and subject parameters for musculoskeletal applications are extensively reported in literature. Such an approach is manually intensive and computationally expensive with longer simulations times. Although Artificial Intelligence (AI) based approaches are implemented to a limited extent in biomechanics, such approaches to predict bone strain in acetabulum of a hip joint, are hardly explored. In this context, the primary objective of this paper is to evaluate machine learning (ML) models in tandem with high-fidelity FEA data for the accelerated prediction of the biomechanical response in the acetabulum of the human hip joint, during the walking gait. The parameters used in the FEA study included the subject weight, number and distribution of fins on the periphery of the acetabular shell, bone condition and phases of the gait cycle. The biomechanical response has also been evaluated using three different acetabular liners, including pre-clinically validated HDPE-20% HA-20% Al2O3, highly-crosslinked ultrahigh molecular weight polyethylene (HC-UHMWPE) and ZrO2-toughened Al2O3 (ZTA). Such parametric variation in FEA analysis, involving 26 variables and a full factorial design resulted in 10,752 datasets for spatially varying bone strains. The bone condition, as opposed to subject weight, was found to play a statistically significant role in determining the strain response in the periprosthetic bone of the acetabulum. While utilising hyperparameter tuning, K-fold cross validation and statistical learning approaches, a number of ML models were trained on the FEA dataset, and the Random Forest model performed the best with a coefficient of determination (R2) value of 0.99/0.97 and Root Mean Square Error (RMSE) of 0.02/0.01 on the training/test dataset. Taken together, this study establishes the potential of ML approach as a fast surrogate of FEA for implant biomechanics analysis, in less than a minute.

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.

A review of advances in tribology in 2020–2021

Around 1,000 peer-reviewed papers were selected from 3,450 articles published during 2020–2021, and reviewed as the representative advances in tribology research worldwide. The survey highlights the development in lubrication, wear and surface engineering, biotribology, high temperature tribology, and computational tribology, providing a show window of the achievements of recent fundamental and application researches in the field of tribology.

Influence of Psychological Distress in Patients with Hypoallergenic Total Knee Arthroplasty. Treatment Algorithm for Patients with Metal Allergy and Knee Osteoarthritis

The outcome in total knee arthroplasty (TKA) depends on multiples factors, among them is the psychological condition. In addition, up 15 to 30% of the patients that undergo TKA show little or no improvement after surgery, which implies the diagnosis of a painful TKA is a challenge for the orthopedic surgeon, who must rule out a possible metal allergy (MA). It is considered an exclusion diagnosis. Due to the complex relationship between psychological condition and MA, and according to the worse results in patients treated with a hypoallergenic TKA, we asked: (1). What degree of psychological distress (PD) is present in patients who have a hypoallergenic TKA, and how does it influence the results of quality of life (QoL) and functional capacity. (2). Can we develop a new algorithm for patients with a possible MA that improves the outcomes? A pragmatic clinical study was carried out that included patients who underwent hypoallergenic TKA during three consecutive years. Quality of life and functional capacity were measured with (Western Ontario McMaster Universities Osteoarthritis Index) WOMAC index, the Short Form 12 questionnaire (SF-12) questionnaire, and the The EQ-5D-5L questionnaire essentially consists of two pages: the EQ-5D descriptive system and the EQ visual analogue scale (EQ VAS) (Euro-QoL-5D L-VAS (EQ5D)), in all patients. To assess PD, a Psychological Distress Score was developed. SPSS software was performed to statistical analysis, and Student´s test for independent variables with a p < 0.005 as statistically significant. A total of 72 anallergic TKAs in 64 patients were treated during this period; 31.3% of these patients showed features of PD before the surgery. According with the severity of the PD, 60% were classified as severe, 10% as moderate and 30% as mild. Patients with PD had statistically significant worse results on the final WOMAC, SF-12, and EQ5D questionnaires. The final scores of the physical subscale of the SF-12 and EQ5D showed better results in patients diagnosed by psychiatrist. Up to one third of the patients with hypoallergenic TKAs have PD, and their results are clearly inferior to those patients with MA without PD. When PD was diagnosed according with Psychological Distress Score, patients should be carefully assessed in order to determine if a specialist referral is recommended. According with our results, PD should be assessed either by the PCP or by us. If the PD is confirmed, a psychiatry referral is then requested for better preoperative management and treatment. We believe that this approach would lead to better TKA outcomes.