Dislocation is one of the most common complications in total hip arthroplasty (THA) and is primarily driven by bony or prosthetic impingement. The aim of this study was two-fold. First, to develop a simulation that incorporates the functional position of the femur and pelvis and instantaneously determines range of motion (ROM) limits. Second, to assess the number of patients for whom their functional bony alignment escalates impingement risk.

468 patients underwent a preoperative THA planning protocol that included functional x-rays and a lower limb CT scan. The CT scan was segmented and landmarked, and the x-rays were measured for pelvic tilt, femoral rotation, and preoperative leg length discrepancy (LLD). All patients received 3D templating with the same implant combination (Depuy; Corail/Pinnacle). Implants were positioned according to standardised criteria.

Each patient was simulated in a novel ROM simulation that instantaneously calculates bony and prosthetic impingement limits in functional movements. Simulated motions included flexion and standing-external rotation (ER). Each patient's ROM was simulated with their bones oriented in both functional and neutral positions.

13% patients suffered a ROM impingement for functional but not neutral extension-ER. As a result, 48% patients who failed the functional-ER simulation would not be detected without consideration of the functional bony alignment. 16% patients suffered a ROM impingement for functional but not neutral flexion. As a result, 65% patients who failed the flexion simulation would not be detected without consideration of the functional bony alignment.

We have developed a ROM simulation for use with preoperative planning for THA surgery that can solve bony and prosthetic impingement limits instantaneously. The advantage of our ROM simulation over previous simulations is instantaneous impingement detection, not requiring implant geometries to be analysed prior to use, and addressing the functional position of both the femur and pelvis.

Iliopsoas tendonitis occurs in up to 30% of patients after hip resurfacing arthroplasty (HRA) and is a common reason for revision. The primary purpose of this study was to validate our novel computational model for quantifying iliopsoas impingement in HRA patients using a case-controlled investigation. Secondary purpose was to compare these results with previously measured THA patients.

We conducted a retrospective search in an experienced surgeon's database for HRA patients with iliopsoas tendonitis, confirmed via the active hip flexion test in supine, and control patients without iliopsoas tendonitis, resulting in two cohorts of 12 patients. The CT scans were segmented, landmarked, and used to simulate the iliopsoas impingement in supine and standing pelvic positions. Three discrete impingement values were output for each pelvic position, and the mean and maximum of these values were reported. Cup prominence was measured using a novel, nearest-neighbour algorithm.

The mean cup prominence for the symptomatic cohort was 10.7mm and 5.1mm for the asymptomatic cohort (p << 0.01). The average standing mean impingement for the symptomatic cohort was 0.1mm and 0.0mm for the asymptomatic cohort (p << 0.01). The average standing maximum impingement for the symptomatic cohort was 0.2mm and 0.0mm for the asymptomatic cohort (p << 0.01). Impingement significantly predicted the probability of pain in logistic regression models and the simulation had a sensitivity of 92%, specificity of 91%, and an AUC ROC curve of 0.95.

Using a case-controlled investigation, we demonstrated that our novel simulation could detect iliopsoas impingement and differentiate between the symptomatic and asymptomatic cohorts. Interestingly, the HRA patients demonstrated less impingement than the THA patients, despite greater cup prominence. In conclusion, this tool has the potential to be used preoperatively, to guide decisions about optimal cup placement, and postoperatively, to assist in the diagnosis of iliopsoas tendonitis.

Iliopsoas impingement occurs in between 5–30% of patients after hip arthroplasty and has been thought to only be caused by an oversized cup, cup malpositioning, or the depth of the psoas valley. However, no study has associated the relationship between preoperative measurements with the risk of impingement. This study sought to assess impingement between the iliopsoas and acetabular cup using a novel validated model to determine the risk factors for iliopsoas impingement.

413 patients received lower limb CT scans and lateral x-rays that were segmented, landmarked, and measured using a validated preoperative planning protocol. Implants were positioned according to the preference of ten experienced surgeons. The segmented bones were transformed to the standing reference frame and simulated with a novel computational model that detects impingement between the iliopsoas and acetabular cup. Definitions of patients at-risk and not at-risk of impingement were defined from a previous validation study of the simulation. At-risk patients were propensity score matched to not at-risk patients.

21% of patients were assessed as being at-risk of iliopsoas impingement. Significant differences between at-risk patients and not at-risk patients were observed in standing pelvic tilt (p << 0.01), standing femoral internal rotation (p << 0.01), medio-lateral centre-of-rotation (COR) change (p << 0.01), supine cup anteversion (p << 0.01), pre- to postoperative cup offset change (p << 0.001), postoperative gross offset (p = 0.009), and supero-inferior COR change (p = 0.02).

Impingement between the iliopsoas and acetabular cup is under-studied and may be more common than is published in the literature. Previously it has been thought to only be related to cup size or positioning. However, we have observed significant differences between at-risk and not at-risk patients in additional measurements. This indicates that its occurrence is more complex than simply being related to cup position.

In 2021, Vigdorchik et al. published a large multicentre study validating their simple Hip-Spine Classification for determining patient-specific acetabular component positioning in total hip arthroplasty (THA). The purpose of our study was to apply this Hip-Spine Classification to a sample of Australian patients undergoing THA surgery to determine the local acetabular component positioning requirements. Additionally, we propose a modified algorithm for adjusting cup anteversion requirements.

790 patients who underwent THA surgery between January 2021 and June 2022 were assessed for anterior pelvic plane tilt (APPt) and sacral slope (SS) in standing and relaxed seated positions and categorized according to their spinal stiffness and flatback deformity. Spinal stiffness was measured using pelvic mobility (PM); the ΔSS between standing and relaxed seated. Flatback deformity was defined by APPt <-13° in standing. As in Vigdorchik et al., PM of <10° was considered a stiff spine. For our algorithm, PM of <20° indicated the need for increased cup anteversion. Using this approach, patient-specific cup anteversion is increased by 1° for every degree the patient's PM is <20°.

According to the Vigdorchik simple Hip-Spine classification groups, we found: 73% Group 1A, 19% Group 1B, 5% Group 2A, and 3% Group 2B. Therefore, under this classification, 27% of Australian THA patients would have an elevated risk of dislocation due to spinal deformity and/or stiffness. Under our modified definition, 52% patients would require increased cup anteversion to address spinal stiffness.

The Hip-Spine Classification is a simple algorithm that has been shown to indicate to surgeons when adjustments to acetabular cup anteversion are required to account for spinal stiffness or flatback deformity. We investigated this algorithm in an Australian population of patients undergoing THA and propose a modified approach: increasing cup anteversion by 1° for every degree the patient's PM is <20°.

A primary goal of revision Total Knee Arthroplasty (rTKA) is restoration of the Joint Line (JL) and Posterior Condylar Offsets (PCO). The presence of a native contralateral joint allows JL and PCO to be inferred in a way that could account for patient-specific anatomical variations more accurately than current techniques. This study assesses bilateral distal femoral symmetry in the context of defining targets for restoration of JL and PCO in rTKA.

566 pre-operative CTs for bilateral TKAs were segmented and landmarked by two engineers. Landmarks were taken on both femurs at the medial and lateral epicondyles, distal and posterior condyles and hip and femoral centres. These landmarks were used to calculate the distal and posterior offsets on the medial and lateral sides (MDO, MPO, LDO, LPO respectively), the lateral distal femoral angle (LDFA), TEA to PCA angle (TEAtoPCA) and anatomic to mechanical axis angle (AAtoMA). Mean bilateral differences in these measures were calculated and cases were categorised according to the amount of asymmetry.

The database analysed included 54.9% (311) females with a mean population age of 68.8 (±7.8) years. The mean bilateral difference for each measure was: LDFA 1.4° (±1.0), TEAtoPCA 1.3° (±0.9), AAtoMA 0.5° (±0.5), MDO 1.4mm (±1.1), MPO 1.0mm (±0.8). The categorisation of asymmetry for each measure was: LDFA had 39.9% of cases with <1° bilateral difference and 92.4% with <3° bilateral difference, TEAtoPCA had 45.8% <1° and 96.6% <3°, AAtoMA had 85.7% <1° and 99.8% <3°, MDO had 46.2% <1mm and 90.3% <3mm, MPO had 57.0% <1mm and 97.9% <3mm.

This study presents evidence supporting bilateral distal femoral symmetry. Using the contralateral anatomy to obtain estimates for JL and PCO in rTKA may result in improvements in intraoperative accuracy compared to current techniques and a more patient specific solution to operative planning.

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.

Introduction & aims

Patient specific instrumentation (PSI) is a useful tool to execute pre-operatively planned surgical cuts and reduce the number of trays in surgery. Debate currently exists around improved accuracy, efficacy and patient outcomes when using PSI cutting guides compared to conventional instruments. Unicompartmental Knee Arthroplasty (UKA) revision to Total Knee Arthroplasty (TKA) represents a complex scenario in which traditional bone landmarks, and patient specific axes that are routinely utilised for component placement may no longer be easily identifiable with either conventional instruments or navigation. PSI guides are uniquely placed to solve this issue by allowing detailed analysis of the patient morphology outside the operating theatre. Here we present a tibia and femur PSI guide for TKA on patients with UKA.

Introduction

Knee ligament laxity and soft tissue balance are important pre- and intra- operative balancing factors in total knee arthroplasty (TKA). Laxity can be measured pre-operatively from short-leg radiographs using a stress device to apply a reproducible force to the knee, whereas intra-operative laxity is routinely measured using a navigation system in which a variable surgeon-applied force is applied. The relationship between these two methods and TKA outcome however, has not been investigated. This study aims to determine how intra-operative assessments of laxity relate to functional radiographic assessments performed on pre-operatively. We also investigate how laxity relates to short-term patient-reported outcomes.

Introduction

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.

Introduction & aims

Resurfacing of the patella is an important part of most TKA operations, usually using an onlay technique. One common practice is to medialise the patellar button and aim to recreate the patellar offset, but most systems do not well control alignment of the patella button. This study aimed to investigate for relationships between placement and outcomes and report on the accuracy of patella placement achieved with the aid of a patella Patient Specific Guide (PSG).

Introduction

Provision of prehabilitation prior to total knee arthroplasty (TKA) through a digital mobile application is a novel concept. The primary aim of our research is to determine whether provision of prehabilitation through a mobile digital application impacts length of stay (LOS), requirement for inpatient rehabilitation and hospital-associated costs after TKA. Our study hypothesis is that a mobile digital application provides a low resource, cost effective method of delivering prehabilitation prior to TKA.

Introduction

Dissatisfaction rates after TKA are reported to be between 15 – 25%, with unmet outcome expectations being a key contributor. Shared decision making tools (SDMT) are designed to align a patient's and surgeon's expectations. This study demonstrates clinical validation of a patient specific shared decision making tool.

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.

Provision of prehabilitation prior to total knee arthroplasty (TKA) through a digital mobile application is a novel concept. Our research evaluates a resource effective and cost effective method of delivering prehabilitation. The primary aim of our research is to determine whether provision of prehabilitation through a mobile digital application impacts inpatient LOS after TKA. The secondary objective is to understand the effect of digital prehabilitation on hospital costs.

An observational, retrospective analysis was performed on a consecutive case series of 64 patients who underwent TKA by a single surgeon over a 21 month period. Exercise provision varied from 3 months to 2 weeks prior to TKA. The outcomes of rehabilitation length of stay, total length of stay and total hospital costs were statistically significantly at p=0.5. The rehabilitation length of stay was 3.79 days in the experimental and 7.33 days in the control group (p = 0.045), the total length of stay was 12.00 days in the control and 8.04 days in the experimental group (p=0.03) and the total cost of the hospital stay was $6357.35AUD for the control and $4343.22AUD for the experimental group (p=0.029).

Our research shows a cost saving with this intervention, as measured by a reduction in rehabilitation length of stay. To our knowledge, this is the first piece of research that analyses the impact of the use of a digital mobile application providing prehabilitation prior to TKA.

Introduction

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.

Introduction

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.

Introduction

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.

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.