Interpretation of Movement during Stair Ascent for Predicting Severity and Prognosis of Knee Osteoarthritis in Elderly Women Using Support Vector Machine

Several studies have demonstrated that pathologic movement changes in knee osteoarthritis (OA) may contribute to disease progression. The aim of this study was to investigate the association between movement changes during stair ascent and pain, radiographic severity, and prognosis of knee OA in the elderly women using machine learning (ML) over a seven-year follow-up period. Eighteen elderly female patients with knee OA and 20 healthy controls were enrolled. Kinematic data for stair ascent were obtained using a 3D-motion analysis system at baseline. Kinematic factors were analyzed based on one of the popular ML methods, support vector machines (SVM). SVM was used to search kinematic predictors associated with pain, radiographic severity of knee OA, and unfavorable outcomes, which were defined as persistent knee pain as reported at the seven-year follow-up or as having undergone total knee replacement during the follow-up period. Six patients (46.2%) had unfavorable outcomes at the seven-year follow-up. SVM showed accuracy of detection of knee OA (97.4%), prediction of pain (83.3%), radiographic severity (83.3%), and unfavorable outcomes (69.2%). The predictors with SVM included the time of stair ascent, maximal anterior pelvis tilting, knee flexion at initial foot contact, and ankle dorsiflexion at initial foot contact. The interpretation of movement during stair ascent using ML may be helpful for physicians not only in detecting knee OA, but also in evaluating pain and radiographic severity.