Prediction of postoperative gait speed change after bilateral primary total knee arthroplasty in female patients using a machine learning algorithm

Abstract

Background

An important aim of total knee arthroplasty is to achieve functional recovery, which includes post-operative increase in walking speed. Therefore, predicting whether a patient will walk faster or slower after surgery is important in TKA, which has not been studied in previous literatures. Who walks faster and who walks slower after TKA? Can we predict these kinds of patients before surgery?

Hypothesis

Whether or not a patient walk faster after total knee arthroplasty can be predicted with preoperative characteristics.

Patients and methods

In this retrospective cohort study, 128 female patients who underwent staged bilateral total knee arthroplasty were analyzed with gait analysis preoperatively and at postoperative two years. These patients were divided into three different groups according to the percentage of gait speed change after total knee arthroplasty: 1) V(+), more than 10% gait speed increase; 2) V(−), more than 10% gait speed decrease; and 3) V(0), those in-between. Gait parameters, mechanical axis angles, WOMAC pain score and Knee Society scores of the two groups (V(+) and V(−)) were compared. Furthermore, a classification model predicting whether a patient walks faster after total knee arthroplasty was designed using a machine learning algorithm.

Results

After total knee arthroplasty, average gait speed increased by 0.07 m/s from 0.87 m/s to 0.94 m/s (p < 0.001) and gait speed increased in 43.8% of the patients (n = 56). However, gait speed decreased in a significant number of patients (n = 17, 13.3%). When V(+) and V(−) groups were compared, gait speed, cadence, sagittal/coronal knee range of motion, and Knee Society Function score were lower in the V(+) group before surgery, but became higher after surgery. Gait speed change could be predicted using three variables (preoperative gait speed, age, and the magnitude of mechanical axis angle). The area under the receiver operating characteristic curve of the machine learning model was 0.86.

Discussion

After total knee arthroplasty, gait speed was maintained or increased in most patients. However, gait speed decreased in a significant number of patients. The machine learning classification model showed a good predictive performance, which could aid in the decision-making and the timing of total knee arthroplasty.

Level of evidence

III; retrospective cohort study.