Background: Total Knee Replacement (TKR) surgery is being utilised in a younger, more active population with greater functional expectations. Understanding whether patient-perceived measures of function reflect objective biomechanical measures is critical in understanding whether functional limitations can be adequately captured within a clinical setting.
Research Question: Do changes in objective gait biomechanics measures reflect patient-reported outcome measures at approximately 12 months following TKR surgery?
Methods: Three-dimensional gait analysis was performed on 41 patients with OA who were scheduled for TKR surgery, 22 of which have returned for a (9–24 month) follow-up assessment. Principal Component Analysis was used to define features of variation between OA subjects and an additional 31 non-pathological control subjects. These were used to train the Cardiff Classifier, an objective classification technique, and subsequently quantify changes following TKR surgery. Patient-perceived changes were also assessed using the Oxford Knee Score (OKS), Knee Outcome Survey (KOS), and Pain Audit Collection System scores (PACS). Pearson and Spearman correlation coefficients were calculated to establish the relationship between changes in objectively-measured and perceived outcome.
Results: Objective measures of biomechanical change were strongly correlated to changes in OKS(r=-0.695, p < 0.001) and KOS(r=-.810, p < 0.001) assessed outcomes. Pain (PACS) was only related to biomechanical function post-operatively (r=-.623, p = 0.003).
Significance: In this biomechanics study, the relationship between changes in objective function and patientreported measures pre to post TKR surgery is stronger than in studies which did not include biomechanics metrics. Quality of movement may hold more significance for a patient’s perception of improvement than functional measures which consider only the time taken or distance travelled during functional activities.
The principal goal of Total Knee Replacement (TKR) in the treatment of knee osteoarthritis (OA) is to improve quality of life through the restoration of joint function, and reduction of pain. In recent years, there has been a dramatic rise in the utilisation of TKR to treat younger patients , and those with higher functional expectations [2,3]. Changes in physical function following surgery have most commonly been monitored using patient-reported outcome measures (PROMs). Recent evidence suggests PROMs fail to capture changes in performance-based measures following TKR surgery [4–7]. It has also been suggested that patients with severe OA have difficulty discriminating between functional limitation and pain when self-assessing their ability to perform activities of daily living .
Gait analysis provides an objective approach for assessing the apparent disparity between performance-based and perceived functional changes pre to post TKR surgery. Numerous studies have reported functional deficits in biomechanical parameters in TKR cohorts when compared to healthy subjects . Few studies, however, have discussed whether patients with the greatest perceived recovery also have the best biomechanical outcomes and vice versa.
Biomechanical gait analysis yields a wealth of information regarding joint kinematics and kinetics, but the interpretation of findings is complicated by interdependencies of the biomechanical variables .
As a consequence there has been growing interest in statistical techniques which objectively summarise pathological gait changes relative to normative population [9–11]. One of the challenges to summarising biomechanical data is the reduction of temporal waveforms into discrete metrics. One popular method is Principal Component Analysis (PCA), which reduces data into orthogonal components of variation. This method is objective, therefore requiring no subjective selection of target features such as waveforms peaks, and has been demonstrated in numerous studies to successfully identify subtle differences between movement patterns [12,13].
In our unit, the application of PCA has been combined with a classification method based on a Dempster-Shafer theory of evidence, termed the ‘Cardiff Classifier’. This method has been demonstrated to accurately characterise the biomechanical changes in late-stage OA subjects  as a basis for measuring recovery following subsequent TKR [15–17]. Applying these techniques to lower-limb biomechanics during level gait, disparities between the magnitude of subjective and objective functional recovery have been highlighted . Realistic goals following surgery may, however, differ between objective and subjective outcome measures. For example, the Oxford Knee Score (OKS) is designed specifically to be responsive to perceived changes following TKR surgery, meaning healthy subjects would fall into a narrow band within the outcome measure, generally achieving a perfect score of 0/48. Objective methods such as knee range of motion or gait classification, however, are not designed specifically to be responsive to changes following TKR, hence healthy subjects generally fall within a larger portion of the outcome measure.
This study aims to assess the relationship between patient-perceived outcome and objective biomechanical classification of level gait. The first objective is to compare the level of change in PROMs and of biomechanical classification of level gait following TKR surgery. The second objective is to address whether the assessment of functional gains following surgery is significantly altered using gait classification in comparison to using PROMs alone.
2.1. Study participants
The study was approved by the Research Ethics Committee for Wales and Cardiff and Vale University Health Board. Forty-one patients with knee OA who were listed for primary TKR surgery at Cardiff and Vale Orthopaedic Centre were recruited into the study. Subjects were excluded if they were unable to walk 10 m without a walking aid, were unable to give informed consent, had rheumatoid arthritis, or had an unrelated musculoskeletal, neurological or visual condition which might affect the way they move. Participants with bilateral OA were not excluded, nor were those whom had undergone previous arthroplasty in other lower limb joints. At the time of analysis, 22 subjects had undergone re-assessment at least 9 months post-operatively. Due to several practical issues, there was variability in the timing of follow-up visit – the median time was 13.2 months however this ranged between 9.3 and 22.8 months following surgery.
Thirty-one volunteers with no lower-limb pathology (NP) were also recruited from University staff, students and the wider community using poster and email advertisements. Subjects were excluded if they had a history of a lower-limb musculoskeletal condition which required medical treatment, had self-reported pain in the lower-limb or back, or had an inflammatory, neurological or visual condition which might affect the way they move.
2.2. Biomechanical analysis
Human motion analysis was performed during level gait at the motion analysis laboratory at Cardiff School of Engineering. A lowerlimb CAST marker set  was attached to subjects, while they walked barefoot at a self-selected pace along a 10 m walkway. Marker trajectories were collected using 8 Oqus (Qualisys, Sweden) cameras capturing at 60 Hz, and ground reaction forces were calculated from two force platforms (Bertec, USA) capturing at 1080 Hz. Hip, knee and ankle kinematics and kinetics were calculated within Visual 3D (CMotion, USA).