Background:. The aim of this study was to investigate the outcome after ACL reconstruction between a group of patients receiving a standardized supervised physiotherapy guided rehabilitation program and a group of patients who followed an un-supervised, home-based rehabilitation program. Methods:. 40 patients with isolated anterior cruciate ligament injuries were allocated to either a supervised physiotherapy intervention group or home-based exercise group. Patients were investigated by an independent examiner pre-operative, 3, 6, 9 and 12 months post-surgery using the following outcome measures: Lysholm Score and Tegner Activity Scale, functional hopping tests, isometric and isokinetic strength assessments. Results:. Both groups improved significantly (p=0.01–0.04) following 12 months after surgery. The median Lysholm score increased from 57 (34–90) to 94 (90–100) in the supervised group and 60 (41–87) to 97 (95–100) in the unsupervised group. The median Tegner Activity Scale increased from 3 (2–8) to 6 (3–8) in the supervised group and 4 (2–8) to 5 (3–10) in the unsupervised group. The combined mean symmetry indices for the hopping tests improved from 77.3+ 18.7 to 86.8+11.1 (supervised) and from 78.1+30.5 to 88.3+10.9 (unsupervised). Isometric and isokinetic strength symmetry indices for knee extension improved from 68.9+23.5 to 82.8+11.9 resp. 63.7+22.8 to 82.7+15.1 in the supervised group and from 73.6+20.5 to 76.5+17.9 resp. 69.5+24.3 to 76.9+16.9 in the unsupervised group. Eccentric strength symmetry indices for knee extension improved from 67.9+27.7 to 87.8+6.8 in the supervised group and from 71.3+17.8 to 82.6+15.6 in the unsupervised group. Conclusion:. This study could not demonstrate a benefit in a rehabilitation program supervised by a physiotherapist in our population compared to an unsupervised cohort

There has been limited research examining the effect training of orthopaedic trainees may have on patient outcomes. This paper aims to determine if there is a difference in revision rate and functional outcomes of total hip joint replacement performed by consultants compared to those performed by supervised and unsupervised trainees. We reviewed all patient data since 2000 from the New Zealand National Joint Registry in patients undergoing total hip joint replacement (THJR) comparing the outcomes with the experience of the primary surgeon. The outcome measures were revision hip replacement and the Oxford Hip score at six months. We compared the reason for revision controlling for factors such as ASA, age and the index diagnosis. We also compared the six-month Oxford scores with the experience of the primary surgeon. There were 35415 patients who underwent elective THJR, 30344 of which were performed by a consultant, 2982 by a supervised registrar and 1067 by an unsupervised registrar. There was an overall revision rate (RR) of 0.77 per 100 component years. The RR was 0.75 (95% CI 0.67–0.82) for consultants, 0.97 (95% CI 0.72 – 1.28) for supervised trainees and 0.70 (95% CI 0.36 – 1.22) for unsupervised trainees. There was no significant difference in revision rates between consultants and supervised trainees (p<0.077) or unsupervised trainees (p< 0.30). The most common cause for revision surgery was dislocation, occurring in 39% of cases. This was more common in supervised and unsupervised trainees (48% and 50%) however there was no significant difference between the three groups (p-value 0.24). The other causes for revision were; loosening of the acetabular or femoral component, deep infection, pain and fracture with no significant difference between the three groups. The mean OHS was higher for consultants at 40.7 compared to 38.95 and 38.23 for supervised and unsupervised trainees respectively (p <0.001). The results of this study show no significant difference in the revision rate of THJR performed by trainees when compared to their consultants. Orthopaedic consultants do appear to have slightly better (1–2 points) OHS. These results are reassuring and show orthopaedic training does not adversely compromise patient outcomes

Giant cell tumors of bone (GCTs) are locally aggressive tumors with recurrence potential that represent up to 10% of primary tumors of the bone. GCTs pathogenesis is driven by neoplastic mononuclear stromal cells that overexpress receptor activator of nuclear factor kappa-B/ligand (RANKL). Treatment with specific anti-RANKL antibody (denosumab) was recently introduced, used either as a neo-adjuvant in resectable tumors or as a stand-alone treatment in unresectable tumors. While denosumab has been increasingly used, a percentage of patients do not improve after treatment. Here, we aim to determine molecular and histological patterns that would help predicting GCTs response to denosumab to improve personalized treatment. Nine pre-treatment biopsies of patients with spinal GCT were collected at 2 centres. In 4 patients denosumab was used as a neo-adjuvant, 3 as a stand-alone and 2 received denosumab as adjuvant treatment. Clinical data was extracted retrospectively. Total mRNA was extracted by using a formalin-fixed paraffin-embedded extraction kit and we determined the transcript profile of 730 immune-oncology related genes by using the Pan Cancer Immune Profiling panel (Nanostring). The gene expression was compared between patients with good and poor response to Denosumab treatment by using the nSolver Analysis Software (Nanostring). Immunohistochemistry was performed in the tissue slides to characterize cell populations and immune response in CGTs. Two out of 9 patients showed poor clinical response with tumor progression and metastasis. Our analysis using unsupervised hierarchical clustering determined differences in gene expression between poor responders and good responders before denosumab treatment. Poor responding lesions are characterized by increased expression of inflammatory cytokines as IL8, IL1, interferon a and g, among a myriad of cytokines and chemokines (CCL25, IL5, IL26, IL25, IL13, CCL20, IL24, IL22, etc.), while good responders are characterized by elevated expression of platelets (CD31 and PECAM), coagulation (CD74, F13A1), and complement classic pathway (C1QB, C1R, C1QBP, C1S, C2) markers, together with extracellular matrix proteins (COL3A1, FN1,. Interestingly the T-cell response is also different between groups. Poor responding lesions have increased Th1 and Th2 component, but good responders have an increased Th17 component. Interestingly, the checkpoint inhibitor of the immune response PD1 (PDCD1) is increased ~10 fold in poor responders. This preliminary study using a novel experimental approach revealed differences in the immune response in GCTs associated with clinical response to denosumab. The increased activity of checkpoint inhibitor PD1 in poor responders to denosumab treatment may have implications for therapy, raising the potential to investigate immunotherapy as is currently used in other neoplasms. Further validation using a larger independent cohort will be required but these results could potentially identify the patients who would most benefit from denosumab therapy

Background. While in vivo kinematics and forces in the knee have been studied extensively, these are typically measured during controlled activities conducted in an artificial laboratory environment and often do not reflect the natural day-to-day activities of typical patients. We have developed a novel algorithm that together with our electronic tibial component provide unsupervised simultaneous dynamic 3-D kinematics and forces in patients. Methods. An inverse finite element approach was used to compute knee kinematics from in vivo measured knee forces. In vitro pilot testing indicated that the accuracy of the algorithm was acceptable for all degrees of freedom except knee flexion angle. We therefore mounted an electrogoniometer on a knee sleeve to monitor knee flexion while simultaneously recording knee forces. A finite element model was constructed for each subject. The femur was flexed using the measured knee flexion angle and brought into contact with the fixed tibial insert using the three-component contact force vector applied as boundary conditions to the femoral component, which was free to translate in all directions. The relative femorotibial adduction-abduction and axial rotation were varied using an optimization program (iSIGHT, Simulia, Providence, RI) to minimize the difference between the resultant moments output by the model and the experimentally measured moments. Maximum absolute error was less than 1 mm in anteroposterior and mediolateral translation and was 1.2° for axial rotation and varus-valgus angulation. This accuracy is comparable to that reported for fluoroscopically measured kinematics. We miniaturized the external hardware and developed a wearable data acquisition system to monitor knee forces and kinematics outside the laboratory. Results. Knee forces were monitored in three subjects during unsupervised outdoor walking. The terrain included level ground, varying grade slopes, hiking trails, and hiking off-trail. In general knee forces were higher than those measured in the laboratory (2.2 xBW). Peak knee forces were highest (>3 xBW) when hiking up and down a 10° slope. One subject tripped and recorded over 5 x bodyweight. Conclusions. This method of obtaining combined kinematics and forces with minimal external hardware greatly increases our ability for capturing true kinematics and forces. Unsupervised activities outside the laboratory generated significantly different forces compared to in-laboratory measurements. Clinically relevant data can be obtained for preclinical testing of prostheses as well as for advising patients regarding postoperative rehabilitation and activities. We are now able to continuously monitor data over extended periods of time (days or weeks) and to record naturally occurring events (in contrast to choreographed activity). Since we compute tibiofemoral contact as part of the algorithm to determine the kinematics, the forces and kinematics are already integrated with contact analysis. These data can be used as input into damage and wear models to predict failure or for validation of biomechanical models of the knee, which predict knee forces and kinematics. Continuously monitoring in vivo knee forces and kinematics under daily conditions will identify weaknesses and potential areas of failure in current designs and will provide direction into enhancing the function and durability of total knee arthroplasty

Participation in a physical therapy program is considered one of the greatest predictors for successful conservative management of common shoulder disorders, however, adherence to standard exercise protocols is often poor (around 50%) and typically worse for unsupervised home exercise programs. Currently, there are limited tools available for objective measurement of adherence and performance of shoulder rehabilitation in the home setting. The goal of this study was to develop and evaluate the potential for performing home shoulder physiotherapy monitoring using a commercial smartwatch. We hypothesize that shoulder physiotherapy exercises can be classified by analyzing the temporal sequence of inertial sensor outputs from a smartwatch worn on the extremity performing the exercise. Twenty healthy adult subjects with no prior shoulder disorders performed seven exercises from a standard evidence-based rotator cuff physiotherapy protocol: pendulum, abduction, forward elevation, internal/external rotation and trapezius extension with a resistance band, and a weighted bent-over row. Each participant performed 20 repetitions of each exercise bilaterally under the supervision of an orthopaedic surgeon, while 6-axis inertial sensor data was collected at 50 Hz from an Apple Watch. Using the scikit-learn and keras platforms, four supervised learning algorithms were trained to classify the exercises: k-nearest neighbour (k-NN), random forest (RF), support vector machine classifier (SVC), and a deep convolutional recurrent neural network (CRNN). Algorithm performance was evaluated using 5-fold cross-validation stratified first temporally and then by subject. Categorical classification accuracy was above 94% for all algorithms on the temporally stratified cross validation, with the best performance achieved by the CRNN algorithm (99.4± 0.2%). The subject stratified cross validation, which evaluated classifier performance on unseen subjects, yielded lower accuracies scores again with CRNN performing best (88.9 ± 1.6%). This proof-of concept study demonstrates the feasibility of a smartwatch device and machine learning approach to more easily monitor and assess the at-home adherence of shoulder physiotherapy exercise protocols. Future work will focus on translation of this technology to the clinical setting and evaluating exercise classification in shoulder disorder populations

Objective. Emergence of low-cost wearable systems has permitted extended data collection for unsupervised subject monitoring. Recognizing individual activities performed during these sessions gives context to recorded data and is an important first step towards automated motion analysis. Convolutional neural networks (CNNs) have been used with great success to detect patterns of pixels in images for object detection and recognition in many different applications. This work proposes a novel image encoding scheme to create images from time-series activity data and uses CNNs to accurately classify 13 daily activities performed by instrumented subjects. Methods. Twenty healthy subjects were instrumented with a previously developed wearable sensor system consisting of four inertial sensors mounted above and below each knee. Each subject performed eight static and five dynamic activities: standing, sitting in a chair/cross-legged, kneeling on left/right/both knees, squatting, laying, walking/running, biking and ascending/descending stairs. Data from each sensor were synchronized, windowed, and encoded as images using a novel encoding scheme. Two CNNs were designed and trained to classify the encoded images of both static and dynamic activities separately. Network performance was evaluated using twenty iterations of a leave-one-out validation process where a single subject was left out for test data to estimate performance on future unseen subjects. Results. Using 19 subjects for training and a single subject left out for testing per iteration, the average accuracy observed when classifying the eight static activities was 98.0% ±2.9%. Accuracy dropped to 89.3% ±10.6% when classifying all dynamic activities using a separate model with the same evaluation process. Ascending/descending stairs, walking/running, and sitting on a chair/squatting were most commonly misclassified. Conclusions. Previous related work on activity recognition using accelerometer and/or gyroscope raw signals fails to provide sufficient data to distinguish static activities. The proposed method operating on lower limb orientations has classified eight static activities with exceptional accuracy when tested on unseen subject data. High accuracy was also observed when classifying dynamic activities despite the similarity of the activities performed and the expected variance of individuals’ gait. Accuracy reported in existing literature classifying comparable activities from other wearable sensor systems ranges between 27.84% to 84.52% when tested using a similar leave-one-subject-out validation strategy[1]. It is expected that incorporating these trained models into the previously developed wearable system will permit activity classification on unscripted instrumented activity data for more contextual motion analysis

Nutritional Status and Short-Term Outcomes Following THA; Initial Metal Ion Levels Predict Risk in MoM THA; THA Bearing Surface Trends in the US ‘07- ’14; Dislocation Following Two-Stage Revision THA; Timing of Primary THA Prior to or After Lumbar Spine Fusion; Failure Rate of Failed Constrained Liner Revision; ESR and CRP vs. Reinfection Risk in Two-Stage Revision?; Mechanical Complications of THA Based on Approach; Impaction Force and Taper-Trunnion Stability in THA; TKA in Patients Less Than 50 Years of Age; Post-operative Mechanical Axis and 20-year TKA Survival and Function; Return to Moderate to High-intensity Sports after UKA; “Running Two Rooms” and Patient Safety in TJA; Varus and Implant Migration and Contact Kinematics after TKA; Quadriceps Snips in 321 Revision TKAs; Tubercle Proximalization for Patella Infera in Revision TKA; Anterior Condylar Height and Flexion in TKA; Compression Bandage Following Primary TKA; Unsupervised Exercise vs. Traditional PT After Primary TKA and UKA

Knee mechanics - Knee forces during ADL and sports activities in TKA patients. Background. Tibiofemoral forces are important in the design and clinical outcomes of TKA. Knee forces and kinematics have been estimated using computer models or traditionally have been measured under laboratory conditions. Although this approach is useful for quantitative measurements and experimental studies, the extrapolation of results to clinical conditions may not always be valid. We therefore developed a tibial tray combining force transducers and a telemetry system to directly measure tibiofemoral compressive forces in vivo. Methods. Tibial forces were measured for activities of daily living, athletic and recreational activities, and with orthotics and braces, for 4 years postoperatively. Additional measurements included video motion analysis, EMG, fluoroscopic kinematic analysis, and ground reaction force measurement. A third-generation system was developed for continuous monitoring of knee forces and kinematics and for classifying and identifying unsupervised activities outside the laboratory using a wearable data acquisition hardware. Results. Peak forces measured for the following activities were: walking (2.6±0.2xBW); jogging (4.2±0.2)xBW; stationary bicycling (1.3±0.15)xBW; golfing (4.4±0.1)xBW; tennis (4.3±0.4)xBW; skiing (4.3±0.1)xBW; hiking(3.2±0.3)xBW; StairMaster exercise (3.3±0.3)xBW; Elliptical machine exercise (2.3±0.2)xBW; leg press machine (2.8±0.1)xBW; knee extension machine (1.5±0.03)xBW, rowing machine (0.9±0.1)xBW. Conclusions. In vivo measured knee forces can be used to enhance existing in vitro models and wear simulators and to improve prosthetic designs and biomaterials as well as guide physicians in their recommendations to patients of “safe” activities following TKA