Predicting knee adduction moment response to gait retraining with minimal clinical data

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 regression model that uses minimal clinical data—a set of six features easily obtained in the clinic—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 a ground-truth dataset 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 standard deviation of the first peak KAM during baseline walking averaged across test subjects (0.306%BW*HT). This work demonstrates the feasibility of training predictive models with synthetic data and provides clinicians with a new tool to predict the outcome of patient-specific gait retraining without requiring gait lab instrumentation.Author summary: Gait retraining is a conservative intervention for knee osteoarthritis shown to reduce pain and improve function. Although customizing a treatment plan for each patient results in a better therapeutic response, customization cannot yet be performed outside of the gait laboratory, preventing research advances from becoming part of clinical practice. Our work aimed to build a model that accurately predicts whether a patient with knee osteoarthritis will benefit from non-invasive gait retraining using measures that can be easily collected in the clinic. To overcome the lack of large datasets required to train predictive models, we generated data synthetically (N = 138) based on limited ground-truth examples, and we provide experimental evidence for the model’s ability to generalize to real data (N = 15). Our results contribute toward a future in which clinicians can use data collected in the clinic to easily identify patients who would respond to therapeutic gait retraining.