Gait comparison of unicompartmental and total knee arthroplasties with healthy controls

Aims: To compare the gait of unicompartmental knee arthroplasty (UKA) and total knee arthroplasty (TKA) patients with healthy controls, using a machine learning approach. Patients: 145 participants (121 healthy controls, 12 cruciate retaining TKA patients, and 12 mobile bearing UKA patients) were recruited. The TKA and UKA patients were a minimum of 12 months post-op, and matched for pattern and severity of arthrosis, age, and BMI. Methods: Participants walked on an instrumented treadmill until their maximum walking speed was reached. Temporospatial gait parameters, and vertical ground reaction force data was captured at each speed. Oxford knee scores (OKS) were also collected. An ensemble of trees algorithm was used to analyse the data: 27 gait variables were used to train classification trees for each speed, with a binary output prediction of whether these variables were derived from a UKA or TKA patient. Healthy control gait data was then tested by the decision trees at each speed and a final classification (UKA or TKA) reached for each subject in a majority voting manner over all gait cycles and speeds. Top walking speed was also recorded. Results: 92% of the healthy controls were classified by the decision tree as a UKA, 5% as a TKA, and 3% were unclassified. There was no significant difference in OKS between the UKA and TKA patients (p=0.077). Top walking speed in TKA patients (1.6 m/s [1.3-2.1]) was significantly lower than that of both the UKA group (2.2 m/s [1.8-2.7]) and healthy controls (2.2 m/s [1.5-2.7]) (p