Utility score is a preference-based measure of general health state – where 0 is equal to death, and 1 is equal to perfect health. To understand a patient's smallest A tertiary institutional registry (SMART) was used as the study cohort. Patients who underwent unilateral TKA for osteoarthritis from January 2012 to January 2020 were included. Utility score was calculated from VR12 responses using the standardised Brazier's method. Distribution and anchor methods were used for the MCID calculation. For distribution methods, 0.5 standard deviations of the baseline and change scores were used. For anchor methods, the physical and emotional anchor questions in the VR12 survey were used to benchmark utility score outcomes. Anchor methods included mean difference in change score, mean difference in 12 month score, and receiver operating characteristics (ROC) analysis with the Youden index. Complete case analysis of 1735 out of 1809 eligible patients was performed. Significant variation in the MCID estimates for VR12 utility score were reported dependent on the calculation method used. The MCID estimate from 0.5 standard deviations of the change score was 0.083. The MCID estimate from the ROC analysis method using physical or emotional anchor question improvement was 0.115 (CI95 0.08-0.14; AUC 0.656). Different MCID calculation methods yielded different MCID values. Our results suggest that MCID is not an umbrella concept but rather many distinct concepts. A general consensus is required to standardise how MCID is defined, calculated, and applied in clinical practice.
Approximately 20% of patients feel unsatisfied 12 months after primary total knee arthroplasty (TKA). Current predictive tools for TKA focus on the clinician as the intended user rather than the patient. The aim of this study is to develop a tool that can be used by patients without clinician assistance, to predict health-related quality of life (HRQoL) outcomes 12 months after total knee arthroplasty (TKA). All patients with primary TKAs for osteoarthritis between 2012 and 2019 at a tertiary institutional registry were analysed. The predictive outcome was improvement in Veterans-RAND 12 utility score at 12 months after surgery. Potential predictors included patient demographics, co-morbidities, and patient reported outcome scores at baseline. Logistic regression and three machine learning algorithms were used. Models were evaluated using both discrimination and calibration metrics. Predictive outcomes were categorised into deciles from 1 being the least likely to improve to 10 being the most likely to improve. 3703 eligible patients were included in the analysis. The logistic regression model performed the best in out-of-sample evaluation for both discrimination (AUC = 0.712) and calibration (gradient = 1.176, intercept = -0.116, Brier score = 0.201) metrics. Machine learning algorithms were not superior to logistic regression in any performance metric. Patients in the lowest decile (1) had a 29% probability for improvement and patients in the highest decile (10) had an 86% probability for improvement. Logistic regression outperformed machine learning algorithms in this study. The final model performed well enough with calibration metrics to accurately predict improvement after TKA using deciles. An ongoing randomised controlled trial (ACTRN12622000072718) is evaluating the effect of this tool on patient willingness for surgery. Full results of this trial are expected to be available by April 2023. A free-to-use online version of the tool is available at
Cementless total knee arthroplasty (TKA) implants use an interference fit to achieve fixation, which depends on the difference between the inner dimensions of the implant and outer dimensions of the bone. However, the most optimal interference fit is still unclear. A higher interference fit could lead to a superior fixation, but it could also cause bone abrasion and permanent deformation during implantation. Therefore, this study aims to investigate the effect of increasing the interference fit from 350 µm to 700 µm on the primary stability of cementless tibial implants by measuring micromotions and gaps at the bone-implant interface when subjected to two loading conditions. Two cementless e.motion® tibial components (Total Knee System, B. Braun) with different interference fit and surface coating were implanted in six pairs of relatively young human cadaver tibias (47–60 years). The Orthoload peak loads of gait (1960N) and squat (1935N) were applied to the specimens with a custom made load applicator (Figure 1A). The micromotions (shear displacement) and opening/closing gaps (normal displacement) were measured with Digital Image Correlation (DIC) in 6 different regions of interest (ROIs - Figure 1B). Two General Linear Mixed Models (GLMMs) were created with micromotions and interfacial gaps as dependent variables, bone quality, loading conditions, ROIs, and interference fit implants as independent variables, and the cadaver specimens as subject variables.Introduction
Methods
Persistent patellofemoral (PF) pain is a common postoperative complication after total knee arthroplasty (TKA). In the USA, patella resurfacing is conducted in more than 80% of primary TKAs [1], and is, therefore, an important factor during surgery. Studies have revealed that the position of the patellar component is still controversially discussed [2–4]. However, only a limited number of studies address the biomechanical impact of patellar component malalignment on PF dynamics [2]. Hence, the purpose of our present study was to analyze the effect of patellar component positioning on PF dynamics by means of musculoskeletal multibody simulation in which a detailed knee joint model resembled the loading of an unconstrained cruciate-retaining (CR) total knee replacement (TKR) with dome patella button. Our musculoskeletal multibody model simulation of a dynamic squat motion bases on the SimTK data set (male, 88 years, 66.7 kg) [5] and was implemented in the multibody dynamics software SIMPACK (V9.7, Dassault Systèmes Deutschland GmbH, Gilching, Germany). The model served as a reference for our parameter analyses on the impact on the patellar surfacing, as it resembles an unconstrained CR-TKR (P.F.C. Sigma, DePuy Synthes, Warsaw, IN) while offering the opportunity for experimental validation on the basis of instrumented implant components [5]. Relevant ligaments and muscle structures were considered within the model. Muscle forces were calculated using a variant of the computed muscle control algorithm. PF and tibiofemoral (TF) joints were modeled with six degrees of freedom by implementing a polygon-contact model, enabling roll-glide kinematics. Relative to the reference model, we analyzed six patellar component alignments: superior-inferior position, mediolateral position, patella spin, patella tilt, flexion-extension and thickness. The effect of each configuration was evaluated by taking the root-mean-square error (RMSE) of the PF contact force, patellar shift and patellar tilt with respect to the reference model along knee flexion angle.Introduction
Material and Methods
Acetabular bone defects are still challenging to quantify. Numerous classification schemes have been proposed to categorize the diverse kinds of defects. However, these classification schemes are mainly descriptive and hence it remains difficult to apply them in pre-clinical testing, implant development and pre-operative planning. By reconstructing the native situation of a defect pelvis using a Statistical Shape Model (SSM), a more quantitative analysis of the bone defects could be performed. The aim of this study is to develop such a SSM and to validate its accuracy using relevant clinical scenarios and parameters. An SSM was built on the basis of segmented 66 CT dataset of the pelvis showing no orthopedic pathology. By adjusting the SSM's so called The validity of the SSM was tested by a Leave-one-out study, whereby one pelvis at a time was removed from the 66 pelvises and was reconstructed using a SSM of the remaining 65 pelvises. The reconstruction accuracy was assessed by comparing each original pelvis with its reconstruction based on the root-mean-square (RMS) surface error and five clinical parameters (center of rotation, acetabulum diameter, inclination, anteversion, and volume). The influence of six different numbers of shape variation modes (reflecting the degrees of freedom of the SSM) and four different mask sizes (reflecting different clinical scenarios) was analyzed.Introduction
Methods
Total knee replacement (TKR) is an established and effective surgical procedure in case of advanced osteoarthritis. However, the rate of satisfied patients amounts only to about 75 %. One common cause for unsatisfied patients is the anterior knee pain, which is partially caused by an increase in patellofemoral contact force and abnormal patellar kinematics. Since the malpositioning of the tibial and the femoral component affects the interplay in the patellofemoral joint and therefore contributes to anterior knee pain, we conducted a computational study on a cruciate-retaining (CR) TKR and analysed the effect of isolated femoral and tibial component malalignments on patellofemoral dynamics during a squat motion. To analyse different implant configurations, a musculoskeletal multibody model was implemented in the software Simpack V9.7 (Simpack AG, Gilching, Germany) from the SimTK data set (Fregly et al.). The musculoskeletal model comprised relevant ligaments with nonlinear force-strain relation according to Wismans and Hill-type muscles spanning the lower extremity. The experimental data were obtained from one male subject, who received an instrumented CR TKR. Muscle forces were calculated using a variant of the computed muscle control algorithm. To enable roll-glide kinematics, both tibio- and patellofemoral joint compartments were modelled with six degrees of freedom by implementing a polygon-contact-model representing the detailed implant surfaces. Tibiofemoral contact forces were predicted and validated using data from experimental squat trials (SimTK). The validated simulation model has been used as reference configuration corresponding to the optimal surgical technique. In the following, implant configurations, i.e. numerous combinations of relative femoral and tibial component alignment were analysed: malposition of the femoral/tibial component in mediolateral (±3 mm) and anterior-posterior (±3 mm) direction.Introduction
Methods
Although cementless press-fit femoral total knee arthroplasty (TKA) components are routinely used in clinical practice, the effect of the interference fit on primary stability is still not well understood. Intuitively, one would expect that a thicker coating and a higher surface roughness lead to a superior fixation. However, during implant insertion, a thicker coating can introduce more damage to the underlying bone, which could adversely influence the primary fixation. Therefore, in the current study, the effect of coating thickness and roughness on primary stability was investigated by measuring the micromotions at the bone-implant interface with experimental testing. A previous experimental set-up was used to test 6 pairs of human cadaveric femurs (47–60 years, 5 females) implanted with two femoral component designs with either the standard e.motion (Total Knee System, B. Braun, Germany) interference fit of 350 µm (right femurs) or a novel, thicker interference fit of 700 µm (left femurs). The specimens were placed in a MTS machine (Figure 1) and subjected to the peak loads of normal gait (1960N) and squat (1935N), based on the Orthoload dataset for Average 75. Varus/valgus moments were incorporated by applying the loads at an offset relative to the center of the implants, leading to a physiological mediolateral load distribution. Under these loads, micromotions at the implant-bone interface were measured using Digital Image Correlation (DIC) at different regions of interest (ROIs – Figure 1). In addition, DIC was used to measure opening and closing of the implant-bone interface in the same ROIs.Introduction
Methods
