Abstract

Introduction

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.

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.

Introduction

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.

Introduction

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.

Introduction

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.

Introduction

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 vivo comparisons of the kinematic outcome achieved at patient specific levels with the two alignment techniques remain an impossibility. The aim of this research is to develop and report preliminary findings of a means of simulating both alignment techniques on a number of patients.

Introduction and Aims: Knee dislocation is an uncommon but serious injury. This study assessed the initial mechanism of injury, pattern of ligament injury, osteochondral and peri-articular soft-tissue trauma, and associated neurovascular injuries in the multi-ligament knee injury. Outcomes following operative and non-operative management were reviewed.

Method: Retrospective review of patients with multi-ligament knee injuries was performed. Inclusion criteria were either a confirmed knee dislocation, or complete rupture of two or more ligaments requiring reconstruction. Systematic review of hospital records and imaging was performed for all patients, and clinical assessment, including validated outcome scores, were performed in the majority of patients. All reconstructive surgery was performed by the two senior authors.

Results: Forty-five patients with 47 knee injuries were identified over a 13-year period (1990–2003). The most common mechanisms of injury were motorcycle and motor vehicle accidents. Other mechanisms included pedestrians hit by cars, sporting injuries and falls. Approximately half had a documented knee dislocation, while the remainder were located at the time of presentation. Vascular injury occurred in around 25% of patients, all having positive clinical findings. Routine angiography was not performed in the absence of positive clinical findings. Neurological injury also occurred in approximately 20% of patients. Transient neuropraxia was more common than permanent nerve palsy, and there was an association between neurological and vascular injury. Associated injuries were varied, the most frequent being long-bone fracture. A significant number of patients had no associated injuries. Injury patterns were varied, though correlations were found between the reported mechanism and the pattern of ligament rupture and osteochondral injury. The majority of cases were managed with operative repair, and assessment of outcomes revealed that most returned to a good level of function, with some minor objective residual laxity and/or stiffness.

Conclusion: Multi-ligament injuries of the knee are uncommon but serious injuries with potentially catastrophic consequences. This detailed analysis provides correlation between mechanism and resulting injury to the knee and periarticular structures. The analysis of this large series provides valuable information to better understand natural history, and improve future management.