Which osteoarthritic gait features recover following Total Knee Replacement surgery?

Abstract

299]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. MethodsGait 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. ResultsThe 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. ConclusionsThis 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.

Are there functional biomechanical differences in robotic arm-assisted bi-unicompartmental knee arthroplasty compared with conventional total knee arthroplasty? A prospective, randomized controlled trial

“…Often historical compensations of neighbouring joints to the pain and other deficits in the knee remain postoperatively. 18 This study replicated these findings, with preoperative biphasic knee moment significantly predicting postoperative biphasic gait. This suggests that while there were significant gains of normal gait patterns in both groups (Figure 3), a majority of patients would maintain their preoperative gait pattern postoperatively.…”

“…17 The use of motion capture systems and force plates offer an accurate, objective, and non-invasive method of measuring the knee's preoperative and postoperative biomechanical performance. 18 Prosthetic knee designs aim to recreate normal gait kinematics, and gait analysis studies with ground-to-foot force data can measure whether restoration has been achieved. 19 Normal sagittal plane knee moments show a biphasic (both flexion and extension) curve during the stance phase of gait (Figure 1; blue line).…”

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

“…In the present study, the three most robustly discriminatory gait features included reduced sagittal plane range of motion in the hip and pelvis throughout the gait cycle, and reduced knee flexion and extension moment peaks (Appendix ). In patients with knee OA, the corresponding gait features included reduced vertical and anterior‐posterior peak ground reaction forces, and avoidance of knee extension moment 44 . Whether or not a different cohort of patients with hip OA would display similar or the same discriminatory gait features remains to be answered.…”

“…Both indices are two-stage techniques which can briefly be summarized as (i) a reduced-order approximation of gait data is determined using matrix factorization, resulting in a smaller set of multivariate gait "features" (ii) individual or group differences in "scores" for these features are mathematically In patients with knee OA, the corresponding gait features included reduced vertical and anteriorposterior peak ground reaction forces, and avoidance of knee extension moment. 44 Whether or not a different cohort of patients with hip OA would display similar or the same discriminatory gait features remains to be answered. However, the most discriminatory gait features are in accordance with previous studies evaluating gait patterns in patients with hip OA and THA, reporting reduced sagittal and frontal plane kinematics, 6,14,45,46 and altered gait to increase medio-lateral stability in an effort to diminish the demand on the hip abductors.…”

Gait biomechanics phenotypes among total knee arthroplasty candidates by machine learning cluster analysis

“…5,6 Understanding these patient phenotypes is relevant to intervention strategies 8 as treatment targeting individual manifestations or deficiencies might yield greater self-reported improvements, 5,6 and optimize the number of patients whose gait function is restored to healthy norms. 9,10 To date, joint-level biomechanical variability during gait among TKA candidates has primarily been assessed through a priori group definition, stratifying OA patients by sex, 11,12 frontal plane alignment, 13 obesity, 12 or patient-reported symptoms before, 14,15 and after arthroplasty. 5,6 While helpful in understanding how specific patient groups respond to standard of care surgery, these studies provide little evidence into how patient demographic and joint-level features naturally separate.…”