A computational framework to predict post-treatment outcome for gait-related disorders

Abstract

Clinicians often use intuitive models based on clinical experience or regression models based on population studies to plan treatment of gait-related disorders. Because such models are constructed using data collected from previous patients, the predicted clinical outcome for a particular patient may not be reliable. We propose a new approach that uses computational models based on engineering mechanics to predict post-treatment outcome from pre-treatment movement data. The approach utilizes a four-phase optimization process built around a dynamic, patient-specific gait model. The first three phases calibrate the model's joint, inertial, and control parameters, respectively, where the control parameters are weights in an optimization cost function that tracks the patient's pre-treatment gait motion and loads. The last phase predicts the patient's post-treatment gait pattern by performing a tracking optimization with the calibrated model modified to simulate the selected treatment. We demonstrate the approach by simulating how two treatments for knee osteoarthritis (OA) – gait modification and high tibial osteotomy (HTO) surgery – alter the external knee adduction torque for a specific patient. By performing multiple tracking optimizations, we calibrated the model's parameter values to reproduce the patient's knee adduction torque curve for a toe out gait motion. When we performed a tracking optimization with the calibrated model using a modified footpath to simulate an increased stance width, the predicted reduction in both adduction torque peaks matched experimental results to within 4.8% error. When we performed a tracking optimization with the same model using modified leg geometry to simulate HTO surgery, the predicted reductions were consistent with published data. The approach requires further evaluation with a larger number of patients to determine its effectiveness for planning the treatment of gait-related disorders on a patient-specific basis.