Variation in resection thickness of the femur in Total Knee Arthroplasty (TKA) impacts the flexion and extension tightness of the knee. Less well investigated is how variation in patient anatomy drives flexion or extension tightness pre- and post- operatively. Extension and flexion stability of the post TKA knee is a function of the tension in the ligaments which is proportional to the strain. This study sought to investigate how femoral ligament offset relates to post-operative navigation kinematics and how outcomes are affected by component position in relation to ligament attachment sites. A database of TKA patients operated on by two surgeons from 1-Jan-2014 who had a pre-operative CT scan were assessed. Bone density of the CT scan was used to determine the medial and lateral collateral attachments. Navigation (OmniNav, Raynham, MA) was used in all surgeries, laxity data from the navigation unit was paired to the CT scan. 12-month postoperative Knee Osteoarthritis and Outcome Score (KOOS) score and a postoperative CT scan were taken. Preoperative segmented bones and implants were registered to the postoperative scan to determine change in anatomy. Epicondylar offsets from the distal and posterior condyles (of the native knee and implanted components), resections, maximal flexion and extension of the knee and coronal plane laxity were assessed. Relationships between these measurements were determined. Surgical technique was a mix of mechanical gap balancing and kinematically aligned knees using Omni (Raynham, MA) Apex implants.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.
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
