Predicting Knee Adduction Moment Response to Gait Retraining with Minimal Clinical Data

Abstract

Knee osteoarthritis is a progressive disease mediated by high joint loads. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Although changes to the foot progression angle are overall beneficial, KAM reductions are not consistent across patients. Moreover, customized interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a model that uses minimal clinical data to predict the extent of first peak KAM reduction after toe-in gait retraining. For such a model to generalize, the training data must be large and variable. Given the lack of large public datasets that contain different gaits for the same patient, we generated this dataset synthetically. Insights learned from ground-truth datasets with both baseline and toe-in gait trials (N=12) enabled the creation of a large (N=138) synthetic dataset for training the predictive model. On a test set of data collected by a separate research group (N=15), the first peak KAM reduction was predicted with a mean absolute error of 0.134% body weight * height (%BW*HT). This error is smaller than the test set’s subject average standard deviation of the first peak during baseline walking (0.306%BW*HT). This work demonstrates the feasibility of training predictive models with synthetic data and may provide clinicians with a streamlined pathway to identify a patient-specific gait retraining outcome without requiring gait lab instrumentation.