Pre-operative sensor-based gait parameters predict functional outcome after total knee arthroplasty.

Area of Science:

• Orthopedics
• Biomechanics
• Medical Technology

Background:

• Total knee arthroplasty (TKA) shows varied gait function outcomes despite general patient satisfaction.
• Mobile sensor-based systems offer accurate, clinically integrated gait analysis.

Purpose of the Study:

• To assess the feasibility of using mobile sensor-based gait analysis for evaluating surgical outcomes in knee osteoarthritis patients post-TKA.
• To predict post-TKA functional outcomes using pre-operative gait parameters.

Main Methods:

• A foot-worn sensor system analyzed spatio-temporal gait parameters in 24 knee osteoarthritis patients before and after TKA.
• Patients were grouped into positive and negative responders based on gait improvement.
• Machine learning models predicted surgical outcomes using pre-operative gait data.

Main Results:

• Significant differences in gait function were observed between patients and controls.
• While patient-reported outcomes improved, global gait parameters showed no significant pre- to post-operative change.
• Pre-operative gait parameters predicted responder groups with up to 89% accuracy, with stride time and length being key predictors.
• Higher pre-operative gait function correlated with a higher likelihood of functional decrease post-surgery.

Conclusions:

• Mobile sensor-based gait analysis can accurately predict post-TKA functional outcomes.
• Early identification of outcomes aids in pre-operative patient counseling and post-operative management.
• Stride time and length are critical gait parameters for predicting TKA success.