Which osteoarthritic gait features recover following total knee replacement surgery?

Background Gait analysis can be used to measure variations in joint function in patients with knee osteoarthritis (OA), and is useful when observing longitudinal biomechanical changes following Total Knee Replacement (TKR) surgery. The Cardiff Classifier is an objective classification tool applied previously to examine the extent of biomechanical recovery following TKR. In this study, it is further developed to reveal the salient features that contribute to recovery towards healthy function. Methods Gait analysis was performed on 30 patients before and after TKR surgery, and 30 healthy controls. Median TKR follow-up time was 13 months. The combined application of principal component analysis (PCA) and the Cardiff Classifier defined 18 biomechanical features that discriminated OA from healthy gait. Statistical analysis tested whether these features were affected by TKR surgery and, if so, whether they recovered to values found for the controls. Results The Cardiff Classifier successfully discriminated between OA and healthy gait in all 60 cases. Of the 18 discriminatory features, only six (33%) were significantly affected by surgery, including features in all three planes of the ground reaction force (p<0.001), ankle dorsiflexion moment (p<0.001), hip adduction moment (p = 0.003), and transverse hip angle (p = 0.007). All but two (89%) of these features remained significantly different to those of the control group after surgery. Conclusions This approach was able to discriminate gait biomechanics associated with knee OA. The ground reaction force provided the strongest discriminatory features. Despite increased gait velocity and improvements in self-reported pain and function, which would normally be clinical indicators of recovery, the majority of features were not affected by TKR surgery. This TKR cohort retained pre-operative gait patterns; reduced sagittal hip and knee moments, decreased knee flexion, increased hip flexion, and reduced hip adduction. The changes that were associated with surgery were predominantly found at the ankle and hip, rather than at the knee

The Complexity of Human Walking: A Knee Osteoarthritis Study

This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space

Abnormal loading and functional deficits are present in both limbs before and after unilateral knee arthroplasty

Abstract Unilateral knee replacement is often followed by a contralateral replacement in time and the biomechanics of the other knee before and after knee replacement remains poorly understood. The aim of this paper is to distinguish the features of arthritic gait in the affected and unaffected legs relative to a normal population and to assess the objective recovery of gait function post-operatively, with the aim of defining patients at risk of poor post-operative function. Twenty patients with severe knee OA but no pain or deformity in any other lower limb joint were compared to twenty healthy subjects of the same age. Gait analysis was performed and quadriceps and hamstrings co-contraction was measured. Fifteen subjects returned 1 year following knee arthroplasty. Moments and impulses were calculated, principal component analysis was used to analyse the waveforms and a classification technique (the Cardiff Classifier) was used to select the most discriminant data and define functional performance. Comparing pre-operative function to healthy function, classification accuracies for the affected and unaffected knees were 95% and 92.5% respectively. Post-operatively, the affected limb returned to the normal half of the classifier in 8 patients, and 7 of those patients returned to normal function in the unaffected limb. Recovery of normal gait could be correctly predicted 13 out of 15 times at the affected knee, and 12 out of 15 times at the unaffected knee based on pre-operative gait function. Focused rehabilitation prior to surgery may be beneficial to optimise outcomes and protect the other joints following knee arthroplasty

Assessing functional recovery following total knee replacement surgery using objective classification of level gait data and patient-reported outcome measures

Background Patient recovery can be quantified objectively, via gait analysis, or subjectively, using patient reported outcome measures. Association between these measures would explain the level of disability reported in patient reported outcome measures and could assist with therapeutic decisions. Methods Total knee replacement outcome was assessed using objective classification and patient-reported outcome measures (Knee Outcome Survey and Oxford Knee Scores). A classifier was trained to distinguish between healthy and osteoarthritic characteristics using knee kinematics, ground reaction force and temporal gait data, combined with anthropometric data from 32 healthy and 32 osteoarthritis knees. For the osteoarthritic cohort, classification of 20 subjects quantified changes at up to 3 timepoints post-surgery. Findings Osteoarthritic classification was reduced for 17 subjects when comparing pre- to post-operative assessments, however only 6 participants achieved non-pathological classification and only 4 of these were classified as non-pathological at 12 months. In 15 cases, the level of osteoarthritic classification did not decrease between every post-operative assessment. For an individual's recovery, classification outputs correlated (r > 0.5) with knee outcome survey for 75% of patients and oxford knee score for 78% of patients (based on 20 and 9 subjects respectively). Classifier outputs from all visits of the combined total knee replacement sample correlated moderately with knee outcome survey (r > 0.4) and strongly with oxford knee score (r > 0.6). Interpretation Biomechanical deficits existed in most subjects despite improvements in Patient Reported Outcome Measures, with larger changes reported subjectively as compared to measured objectively. Objective Classification provides additional insight alongside Patient Reported Outcomes when reporting recovered outcomes

Development of a novel method for the classification of osteoarthritic and normal knee function

Advances in our understanding of human locomotion can be futile if no practical use is made of them. For the long-term benefit of patients in a clinical setting, scientists and engineers need to forge stronger links with orthopaedic surgeons to make the most use of the recent developments in motion analysis technology. With this requirement as a driving-force, an objective classification tool was developed that uses motion analysis for an application to clinical diagnostics and monitoring, namely knee osteoarthritis (OA) progression and total knee replacement (TKR) recovery. The classification tool is based around the Dempster-Shafer (DS) theory, and as such is built upon the sound foundations of Bayesian statistics. The tool expands on the work of Safranek et al. (1990) and Gerig et al. (2000) who developed and used parts of the classification method in the areas of vision and medical image analysis respectively. Using the data collected during a clinical knee trial, this novel approach enables the objective classification of subjects into an OA or normal group. Each piece of data is transformed into a set of belief values: a level of belief that a subject has OA knee function, a level of belief that a subject has NL knee function and an associated level of uncertainty. The belief values are then represented on a simplex plot, which enables the final classification of a subject, and the level of benefit achieved by TKR surgery to be visualised. The DS method can be used as a fully or partially automated tool. The input variables and control parameters, which are an intrinsic part of the tool, can be chosen by an expert or an optimisation approach. Using a leave-one-out (LOO) approach, the tool was able to classify new subjects with an accuracy of 97.62%. This compares with the 63.89% and 95.24% LOO accuracies of two well-established methods---the Artificial Neural Network and the Linear Discriminant Analysis classifiers respectively. The tool also provides an objective indication of the variables that are the most influential in distinguishing OA and NL knee function. In this case, the variables identified by the tool as important are often cited as clinically relevant variables, which enhances the appeal of the tool to the clinical community and allows for more effective comparison with clinical approaches to diagnosis. Using Simulated Annealing to select the control parameters reduced the LOO accuracy to 95.24%. Automated feature selection using a Genetic Algorithm and Sequential Forward Selection increased the LOO accuracy to 100%. However, further work is required to improve the effect of this process on the overall level of uncertainty in the classification. Initial studies have demonstrated a practical and visual approach that can discriminate between the characteristics of NL and OA knee function with a high level of accuracy. Further development will enable the tool to assist orthopaedic surgeons and therapists in making clinical decisions, and thus promote increased confidence in a patient's medical care.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Classification of biomechanical changes in gait following total knee replacement: an objective, multi-feature analysis

Incidence of osteoarthritis (OA) is steadily increasing amongst the developed world, with the knee being the most commonly affected joint. Knee OA is a complex, progressive and multifactorial disease which can result in severe disability, pain, and reduced quality of life. Numerous biomechanical changes have been associated with OA disease progression within both the affected and unaffected joints. Total knee replacement (TKR) is a common surgical intervention which aims to replace the degenerated articular surfaces. As longevity of the prostheses have improved, TKR surgery is being recommended to an increasingly younger population. There is, however, a growing body of evidence to suggest a proportion of patients exhibit several functional limitations following surgery. Measuring functional changes is challenging, and numerous studies suggest patient-reported changes in physical function aren’t reflective of objectively measured changes. This study builds upon techniques to objectively assess biomechanical function during level gait using three-dimensional stereophotogrammetry, with an aim to quantify biomechanical changes that occur as a result of late-stage OA, and measure and summarise functional changes following TKR surgery. Firstly, the appropriateness of principal component analysis (PCA) and the Cardiff Dempster-Shafer Theory (DST) classifier to reduce and summarise level gait biomechanics is investigated within a cohort of 85 OA and 38 non-pathological (NP) subjects. The validity of previously adopted rules for retaining principal components (PCs) is assessed; namely the application of Kaiser’s rule, and a factor loading threshold of ±0.71. Through the reconstruction of biomechanical waveforms using individual PCs, it is demonstrated that this rule discards biomechanical features which can accurately distinguish between OA and NP gait biomechanics. The currently accepted definitions of two control parameters of the DST classifier, which define the shape of the sigmoid activation function, are shown to introduce a bias under certain conditions. New definitions are proposed and tested, which result in an increase in classification accuracy. The robustness of the leave-one-out (LOO) cross-validation algorithm to assess the performance of the classification is investigated, and findings suggest little benefit of retaining larger cohorts within the cross-validation set. Training bodies of different sizes are investigated, and their ability to classify the remaining data is evaluated. Results indicated that a training body of ten subjects in each group resulted in high classification accuracy (92% ± 2.5%), and improvements in accuracy then began to steadily plateau. The techniques developed thus far are then adopted to classify the hip, knee and ankle biomechanics of 41 OA and 31 NP subjects, to describe the biomechanical characteristics of late-stage OA. There were numerous methodological changes within this section of the study, and it was proved necessary to recalculate new PCs using this cohort. These new PCs were contextualised and used to classify OA biomechanics, resulting in a LOO classification accuracy of 98.6%. Anecdotally, the single misclassified subject had late-stage OA, but reported only mild functional impairments. The biomechanical features which consistently distinguished OA gait are ranked and discussed. The trained DST classifier was used to quantify the biomechanical function of 22 subjects pre and 12-months post-TKR surgery. In contrast to previous findings using the DST technique, biomechanical improvements varied, with no clear group of improvers. Five subjects were classified as NP post-operatively, seven were classified as “non-dominant OA”, and ten as “dominant OA”. Objectively measured function was significantly correlated with two out of nine patient-reported outcome measures both before surgery, and in all nine post-operatively. This might explain discrepancies in the literature between patient-reported and objectively measured changes. A retrospective analysis explored pre-operative predictors highlighted knee and ankle coronal plane angulation at heel strike, ankle range of motion, and timing of peak knee flexion as potential predictors of post-operative function

Gait function improvements, using Cardiff Classifier, are related to patient-reported function and pain following hip arthroplasty

Summarizing results of three-dimensional (3D) gait analysis into a comprehensive measure of overall gait function is valuable to discern to what extent gait function is affected, and later recovered after surgery and rehabilitation. This study aimed to investigate whether preoperative gait function, quantified and summarized using the Cardiff Classifier, can predict improvements in postoperative patient-reported activities of daily living, and overall gait function 1 year after total hip arthroplasty (THA). Secondly, to explore relationships between pre-to-post surgical change in gait function versus changes in patient-reported and performance-based function. Thirty-two patients scheduled for THA and 25 nonpathological individuals were included in this prospective cohort study. Patients were evaluated before THA and 1 year postoperatively using 3D gait analysis, patient-reported outcomes, and performance-based tests. Kinematic and kinetic gait parameters, derived from 3D gait analysis, were quantified using the Cardiff Classifier. Linear regressions investigated the predictive value of preoperative gait function on postoperative outcomes of function, and univariate correlations explored relationships between pre-to-post surgical changes in outcome measures. Preoperative gait function, by means of Cardiff Classifier, explained 35% and 30% of the total variance in change in patient-reported activities of daily living, and in gait function, respectively. Moderate-to-strong correlations were found between change in gait function and change in patient-reported function and pain, while no correlations were found between change in gait function and performance-based function. Clinical significance: Preoperative gait function predicts postsurgical function to a moderate degree, while improvements in gait function after surgery are more closely related to how patients perceive function than their maximal performance of functional tests

Development of a novel method for the classification of osteoarthritic and normal knee function

Advances in our understanding of human locomotion can be futile if no practical use is made of them. For the long-term benefit of patients in a clinical setting, scientists and engineers need to forge stronger links with orthopaedic surgeons to make the most use of the recent developments in motion analysis technology. With this requirement as a driving-force, an objective classification tool was developed that uses motion analysis for an application to clinical diagnostics and monitoring, namely knee osteoarthritis (OA) progression and total knee replacement (TKR) recovery. The classification tool is based around the Dempster-Shafer (DS) theory, and as such is built upon the sound foundations of Bayesian statistics. The tool expands on the work of Safranek et al. (1990) and Gerig et al. (2000) who developed and used parts of the classification method in the areas of vision and medical image analysis respectively. Using the data collected during a clinical knee trial, this novel approach enables the objective classification of subjects into an OA or normal group. Each piece of data is transformed into a set of belief values: a level of belief that a subject has OA knee function, a level of belief that a subject has NL knee function and an associated level of uncertainty. The belief values are then represented on a simplex plot, which enables the final classification of a subject, and the level of benefit achieved by TKR surgery to be visualised. The DS method can be used as a fully or partially automated tool. The input variables and control parameters, which are an intrinsic part of the tool, can be chosen by an expert or an optimisation approach. Using a leave-one-out (LOO) approach, the tool was able to classify new subjects with an accuracy of 97.62%. This compares with the 63.89% and 95.24% LOO accuracies of two well-established methods---the Artificial Neural Network and the Linear Discriminant Analysis classifiers respectively. The tool also provides an objective indication of the variables that are the most influential in distinguishing OA and NL knee function. In this case, the variables identified by the tool as important are often cited as clinically relevant variables, which enhances the appeal of the tool to the clinical community and allows for more effective comparison with clinical approaches to diagnosis. Using Simulated Annealing to select the control parameters reduced the LOO accuracy to 95.24%. Automated feature selection using a Genetic Algorithm and Sequential Forward Selection increased the LOO accuracy to 100%. However, further work is required to improve the effect of this process on the overall level of uncertainty in the classification. Initial studies have demonstrated a practical and visual approach that can discriminate between the characteristics of NL and OA knee function with a high level of accuracy. Further development will enable the tool to assist orthopaedic surgeons and therapists in making clinical decisions, and thus promote increased confidence in a patient's medical care