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<0.001).
Conclusion:
UKA results in a more physiological gait compared to TKA, and a higher top walking speed. This difference in function was not detected
by the OKS.