Kinematics post-TKA are complex; component alignment, component geometry and the patient specific musculoskeletal environment contribute towards the kinematic and kinetic outcomes of TKA. Tibial rotation in particular is largely uncontrolled during TKA and affects both tibiofemoral and patellofemoral kinematics. Given the complex nature of post- TKA kinematics, this study sought to characterize the contribution of tibial tray rotation to kinematic outcome variability across three separate knee geometries in a simulated framework. Five 50th percentile knees were selected from a database of planned TKAs produced as part of a pre-operative dynamic planning system. Virtual surgery was performed using Stryker (Kalamazoo, MI) Triathlon CR and PS and MatOrtho (Leatherhead, UK) SAIPH knee medially stabilised (MS) components. All components were initially planned in mechanical alignment, with the femoral component neutral to the surgical TEA. Each knee was simulated through a deep knee bend, and the kinematics extracted. The tibial tray rotational alignment was then rotated internally and externally by 5° & 10°. The computational model simulates a patient specific deep knee bend and has been validated against a cadaveric Oxford Knee Rig. Preoperative CT imaging was obtained, landmarking to identify all patient specific axes and ligament attachment sites was performed by pairs of trained biomedical engineers. Ethics for this study is covered by Bellberry Human Research Ethics Committee application number 2012-03-710.Introduction
Method
Component alignment cannot fully explain total knee arthroplasty [TKA] performance with regards to patient reported outcomes and pain. Patient specific variations in musculoskeletal anatomy are one explanation for this. Computational simulations allow for the impact of component alignment and variable patient specific musculoskeletal anatomy on dynamics to be studied across populations. This study aims to determine if simulated dynamics correlate with Patient Reported Outcomes. Landmarking of key anatomical points and 3D registration of implants was performed on 96 segmented post-operative CT scans of TKAs. A cadaver rig validated platform for generating patient specific rigid body musculoskeletal models was used to assess the resultant motions. Resultant dynamics were segmented and tested for differentiation with and correlation to a 6 month postoperative Knee injury and Osteoarthritis Outcome Score (KOOS). Significant negative correlations were found between the postoperative KOOS symptoms score and the rollback occurring in midflexion (p<0.001), quadriceps force in mid flexion (p=0.025) and patella tilt throughout flexion (p=0.009, p=0.005, p=0.010 at 10°, 45° and 90° of flexion). A significant positive correlation was found between lateral shift of the patella through flexion and the symptoms score. (p=0.012) Combining a varus/valgus angular change from extension to full flexion between 0° and 4° (long leg axis) and measured rollback of no more than 6mm without roll forward forms a ‘kinematic safe zone’ of outcomes in which the postoperative KOOS score is 11.5 points higher (p=0.013). The study showed statistically significant correlations between kinematic factors in a simulation of postoperative TKR and post-operative KOOS scores. The presence of a ‘kinematic safe zone’ in the data suggests a patient specific optimisation target for any given individual patient and the opportunity to preoperatively determine a patient specific alignment target.
Despite of the high success of TKA, 20% of recipients remain dissatisfied with their surgery. There is an increasing discordance in the literature on what is an optimal goal for component alignment. Furthermore, the unique patient specific anatomical characteristics will also play a role. The dynamic characteristic of a TKR is a product of the complex interaction between a patient's individual anatomical characteristics and the specific alignment of the components in that patient knee joint. These interactions can be better understood with computational models. Our objective was to characterise ligament characteristics by measuring knee joint laxity with functional radiograph and with the aid of a computational model and an optimisation study to estimate the subject specific free length of the ligaments. Pre-operative CT and functional radiographs, varus and valgus stressed X-rays assessing the collateral ligaments, were captured for 10 patients. CT scan was segmented and 3D–2D pose estimation was performed against the radiographs. Patient specific tibio-femoral joint computational model was created. The model was virtually positioned to the functional radiograph positions to simulate the boundary conditions when the knee is stressed. The model was simulated to achieve static equilibrium. Optimisation was done on ligament free length and a scaling coefficient, flexion factor, to consider the ligaments wrapping behaviour. Our findings show the generic values for reference strain differ significantly from reference strains calculated from the optimised ligament parameters, up to 35% as percentage strain. There was also a wide variation in the reference strain values between subjects and ligaments, with a range of 37% strain between subjects. Additionally, the knee laxity recorded clinically shows a large variation between patients and it appears to be divorced from coronal alignment measured in CT. This suggests the ligaments characteristics vary widely between subjects and non-functional imaging is insufficient to determine its characteristics. These large variations necessitate a subject-specific approach when creating knee computational models and functional radiographs may be a viable method to characterise patient specific ligaments.
Ambulation in the postoperative period following TKR is a marker of speed of recovery and, potentally, longer term outcomes. However, patient lifestyle factors are a major confounder. This study sought to develop a model of expected patient step count taking into account preoperative condition and demographics in order to benchmark recovery at a patient specific level. 94 patients were recruited to the study. BMI, demographics, the Short Form 12 (SF-12) and the Knee injury and Osteoarthritis Outcome Score (KOOS) were all captured preoperatively. Step count was measured using commercially available Fitbit devices preoperatively, immediately postoperatively and at 6 weeks postoperatively. Stepwise multiple linear regression models were developed using the preoperative information to define a predictive model of the postoperative step count levels. Spearman's Rho correlations for all relevant data series were also calculated.Introduction
Method
Surgical planning for Patient Specific Instrumentation (PSI) in total knee arthroplasty (TKA) is based on static non-functional imaging (CT or MRI). Component alignment is determined prior to any assessment of clinical soft tissue laxity. This leads to surgical planning where assumptions of correctability of preoperative deformity are false and a need for intraoperative variation or abandonment of the PSI blocks occurs. The aim of this study is to determine whether functional radiology complements pre-surgical planning by identifying non-predictable patient variation in laxity. Pre-operative CT's, standing radiographs and functional radiographs assessing coronal laxity at 20° flexion were collected for 20 patients. Varus/valgus laxity was assessed using the TELOS stress device (TELOS GmbH, Marburg, Germany, see Figure 1). The varus/valgus load was incrementally increased to either a maximum load of 150N or until the patient could not tolerate the discomfort. Radiographs were taken whilst the knee was held in the stressed position. CT scans were segmented and anatomical points landmarked. 2D–3D pose estimations were performed using the femur and tibia against the radiographs to determine knee alignment with each functional radiograph and so characterise the varus/valgus laxityIntroduction
Method
Both navigation and instrumented bone referencing use unreliable intraoperative landmark identification or fixed referencing rules which don't reflect patient specific variability. PSI, however, lacks the flexibility to adapt to soft tissue factors not known during preoperative planning, in addition to suffering error from guide fit. A novel method of recreating surgical cut planes that combines preoperative image based identification of landmarks and planning with intraoperative adjustability is under development. This method uses an intraoperative 3D scan of the bone in conjunction with a preoperative CT scan to achieve the desired cuts and so avoids issues of intraoperative identification of landmarks. During TKA surgery, a reference device is placed on the exposed femur. The device is used to position a target block which is pinned to the bone (see Figure 1). The condyles and target block are then scanned, the process taking a second to complete. This 3D scan is filtered to remove extraneous bodies and noise leaving only the bony geometry and target block (see Figure 2). The scan is then reconciled to the known bone geometry taken from preoperative CT scans. A cutting block is then fixed to the target block with a reference array visible to the camera attached. Pre-planned cut planes on a computer model of the bone are compared to the position and configuration of the distal cutting guide. Software guides the surgeon in real-time on the necessary configuration changes required to align the cutting block. The cut is performed on the distal femur, the cutting guide removed from the target-block, and a second scan performed. The software repeats the filtering and alignment processes and provides the surgeon with data on how closely the performed cut matches the alignment planned.Introduction
Method
Recent studies have challenged the concept that a single ‘correct’ alignment to standardised anatomical references is the primary driver of TKA performance with regards to patient satisfaction outcomes. Patient specific variations in musculoskeletal anatomy are one explanation for this. Virtual simulated environments such as rigid body modelling allow for the impact of component alignment and variable patient specific musculoskeletal anatomy to be studied simultaneously. This study aims to determine if the output kinematics derived from consideration of both postoperative component alignment and patient specific musculoskeletal modelling has predictive potential of Patient Reported Outcomes. Landmarking of key anatomical points and 3D registration of implants was performed on 96 segmented post-operative CT scans of TKAs. Both femoral and tibia implant components were registered. Acadaver rig validated platform for generating patient specific rigid body musculoskeletal models was used to assess the resultant motions and contact forces through a 0 to 140 degree deep knee bend cycle. Resultant kinematics were segmented and tested for differentiation with and correlation to a 12 month postoperative Knee injury and Osteoarthritis Outcome Score (KOOS).Introduction
Method
Total Knee Replacement (TKR) alignment measured intra-operatively with Navigation has been shown to differ from that observed in long leg radiographs (Deep 2011). Potential explanations for this discrepancy may be the effect of weight bearing or the dynamic contributions of soft tissue loads. A validated, 3D, dynamic patient specific musculoskeletal model was used to analyse 85 post-operative CT scans using a common implant design. Differences in coronal and axial plane tibio-femoral alignment in three separate scenarios were measured: Unloaded as measured in a post-op CT Unloaded, with femoral and tibial components set aligned to each other Weight bearing with the extensor mechanism engaged Scenario number two illustrates the tibio-femoral alignment when the femoral component sits congruently on the tibia with no soft tissue acting whereas scenario three is progression of scenario number two with weight applied and all ligaments are active. Two tailed paired students t-test were used to determine significant differences in the means of absolute difference of axial and coronal alignments.Introduction
Method
Despite generally excellent patient outcomes for Total Knee Arthroplasty (TKA), there remains a contingent of patients, up to 20%, who are not satisfied with the outcome of their procedure. (Beswick, 2012) There has been a large amount of research into identifying the factors driving these poor patient outcomes, with increasing recognition of the role of non-surgical factors in predicting achieved outcomes. However, most of this research has been based on single database or registry sources and so has inherited the limitations of its source data. The aim of this work is to develop a predictive model that uses expert knowledge modelling in conjunction with data sources to build a predictive model of TKR patient outcomes. The preliminary Bayesian Belief Network (BBN) developed and presented here uses data from the Osteoarthritis Initiative, a National Institute of Health funded observational study targeting improved diagnosis and monitoring of osteoarthritis. From this data set, a pared down subset of patient outcome relevant preoperative questionnaire sets has been extracted. The BBN structure provides a flexible platform that handles missing data and varying data collection preferences between surgeons, in addition to temporally updating its predictions as the patient progresses through pre and postoperative milestones in their recovery. In addition, data collected using wearable activity monitoring devices has been integrated. An expert knowledge modelling process relying on the experience of the practicing surgical authors has been used to handle missing cross-correlation observations between the two sources of data.Introduction
Method
Total Knee Arthroplasty (TKA) is an established procedure for relieving patients of pain and functional degradation associated with end-stage osteoarthritis of the knee. Historically, alignment of components in TKA has focused on a ‘reconstructive’ approach neutral to the mechanical axes of the femur and tibia coupled with ligament balancing to achieve a balanced state. More recently, Howell et al. have proposed an alternate approach to TKA alignment, called kinematic alignment. (Howell, 2012) This approach seeks to position the implants to reproduce underlying, pre-disease state femoral condylar and tibial plateau morphology, and in doing is ‘restorative’ of the patients underlying knee kinematic behaviour rather than ‘reconstructive’. While some promising early clinical results have been reported at the RCT level (Dosset, 2014), In 20 TKR subjects, 3D geometry of the patient was reconstructed from preoperative CT scans, which were then used to define a patient specific soft tissue attachment model. The knees were then modelled passing through a 0 to 140 degree flexion cycle post TKR under each alignment technique. A multi-radius CR knee design has been used to model the TKA under each alignment paradigm. Kinematic measurements of femoral rollback, internal to external rotation, coronal plane joint torque, patella shear force and varus-valgus angulation are reported at 5, 30, 60, 90 and 120 degrees of flexion. Student's paired 2 sample t-tests are used to determine significant differences in means of the kinematic variables.Introduction
Method
