Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern machine learning methods, where large, complex datasets can be used to identify patterns in the data. Using these data-driven approaches, it could be possible to better predict the optimal interventions for patients at an early stage, potentially avoiding pain and inappropriate surgery or rehabilitation. In this project we developed a prototype database platform for combining and sharing biomechanics datasets. The database includes methods for importing and standardising data and associated variables, to create a seamless, searchable combined dataset of both healthy and knee OA biomechanics. Data was curated through calls to members of the OATech Network+ (Abstract
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Component mal-positioning in total hip replacement (THR) and total knee replacement (TKR) can increase the risk of revision for various reasons. Compared to conventional surgery, relatively improved accuracy of implant positioning can be achieved using computer assisted technologies including navigation, patient-specific jigs, and robotic systems. However, it is not known whether application of these technologies has improved prosthesis survival in the real-world. This study aimed to compare risk of revision for all-causes following primary THR and TKR, and revision for dislocation following primary THR performed using computer assisted technologies compared to conventional technique. We performed an observational study using National Joint Registry data. All adult patients undergoing primary THR and TKR for osteoarthritis between 01/04/2003 to 31/12/2020 were eligible. Patients who received metal-on-metal bearing THR were excluded. We generated propensity score weights, using Sturmer weight trimming, based on: age, gender, ASA grade, side, operation funding, year of surgery, approach, and fixation. Specific additional variables included position and bearing for THR and patellar resurfacing for TKR. For THR, effective sample sizes and duration of follow up for conventional versus computer-guided and robotic-assisted analyses were 9,379 and 10,600 procedures, and approximately 18 and 4 years, respectively. For TKR, effective sample sizes and durations of follow up for conventional versus computer-guided, patient-specific jigs, and robotic-assisted groups were 92,579 procedures over 18 years, 11,665 procedures over 8 years, and 644 procedures over 3 years, respectively. Outcomes were assessed using Kaplan-Meier analysis and expressed using hazard ratios (HR) and 95% confidence intervals (CI).Abstract
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Magnetic resonance imaging (MRI) is one of the most widely used investigations for knee pain as it provides detailed assessment of the bone and soft tissues. The aim of this study was to report the frequency of each diagnosis identified on MRI scans of the knee and explore the relationship between MRI results and onward treatment. Consecutive MRI reports from a large NHS trust performed in 2017 were included in this study. The hospital electronic system was consulted to identify whether a patient underwent x-ray prior to the MRI, attended an outpatient appointment or underwent surgery.Abstract
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One of the main surgical goals when performing a total knee replacement (TKR) is to ensure the implants are properly aligned and correctly sized; however, understanding the effect of alignment and rotation on the biomechanics of the knee during functional activities is limited. Cardiff University has unique access to a group of local patients who have relatively high frequency of poor alignment, and early failure. This provides a rare insight into how malalignment of TKR's can affect patients from a clinical and biomechanical point of view to determine how to best align a TKR. This study aims to explore relationship clinical surgical measurements of Implant alignment with in-vivo joint kinematics. 28 patient volunteers (with 32 Kinemax (Stryker) TKR's were recruited. Patients undertook single plane video fluoroscopy of the knee during a step-up and step-down task to determine TKR in-vivo kinematics and centre of rotation (COR). Joint Track image registration software (University of Florida, USA) was used to match CAD models of the implant to the x-ray images. Hip-Knee-Ankle (HKA) was measured using long-leg radiographs to determine frontal plane alignment. Posterior tibial slope angle was calculated using radiographs. An independent sample t-test was used to explore differences between neutral (HKA:-2° to 2°), varus (≥2°) and valgus alignment (≤-2°) groups. Other measures were explored across the whole cohort using Pearson's correlations (SPSS V23). There was found to be no statistical difference between groups or correlations for HKA. The exploratory analysis found that tibial slope correlated with Superior/Inferior translation ROM during step up (r=−0.601, p<0.001) and step down (r=−.512, p=0.03) the position of the COR heading towards the lateral (r=−.479, p=0.006) during step down. Initial results suggest no relationship between frontal plane alignment and in-vivo. Exploratory analyses have found other relationships that are worthy of further research and may be important in optimizing function.
High tibial Osteotomy (HTO) realigns the forces in the knee to slow the progression of osteoarthritis. This study relates the changes in knee joint biomechanics during level gait to glutamate signalling in the subchondral bone of patients pre and post HTO. Glutamate transmits mechanical signals in bone and activates glutamate receptors to influence inflammation, degeneration and nociception in arthritic joints. Thus glutamate signalling is a mechanism whereby mechanical load can directly modulate joint pathology and pain. 3D motion analysis was used to assess level gait prior to HTO (n=5) and postoperatively (n=2). A biomechanical model of each subject was created in Visual3D (C-motion. Inc) and used for biomechanical analysis. Gene expression was analysed by RT-PCR from bone cores from anterior and posterior drill holes, subdivided according to medial or lateral proximal tibia from HTO patients (n=5).BACKGROUND
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Patients with knee osteoarthritis (OA) often tell us that they put extra load on the joints of the opposite leg as they walk. Multiple joint OA is common and has previously been related to gait changes due to hip OA (Shakoor et al 2002). The aim of this study was to determine whether patients with medial compartment knee OA have abnormal biomechanics of the unaffected knee and both hips during normal level gait. Twenty patients (11 male, 9 female), with severe medial compartment knee OA and no other joint pain were recruited. The control group comprised 20 adults without musculoskeletal pain. Patients were reviewed, x-rays were examined and WOMAC and Oxford knee scores were completed. A 12 camera Vicon (Vicon, Oxford) system was used to collect kinematic data (100Hz) on level walking and the ground reaction force was recorded using three AMTI force plates (1000Hz). Surface electrodes were placed over medial and lateral quadriceps and hamstrings bilaterally to record EMG data (1000Hz). Kinematics and kinetics were calculated using the Vicon ‘plug-in-gait’ model. A co-contraction index was calculated for the EMG signals on each side of the knee, representing the magnitude of the combined readings relative to their maximum contraction during the gait cycle. Statistical comparisons were performed using t-tests with Bonferroni's correction for two variables and ANOVA for more than two variables (SPSS v16).Introduction
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