Abstract
OBJECTIVE: Variability exists in the degree of improvement patients experience following exercise-based interventions (EBIs) for knee osteoarthritis (KOA), but understanding of this heterogeneity is limited. Using a machine learning approach, this study leveraged data from two randomized controlled trials (RCTs) to identify patient characteristics contributing to differential treatment effects. DESIGN: The RCTs enrolled n = 621 patients and evaluated three EBIs (group-based physical therapy (PT), individual PT, and a Stepped Exercise Program) and an education control group. The primary outcome was change in total Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score from baseline to end of treatment. Predictors included 25 demographic, clinical, and psychosocial characteristics. Three metalearners with three machine learning algorithms each and a simple interpretable model-based regression tree were used to identify subgroups with differential treatment effects. Fit was evaluated with holdout/validation data using root mean square error and mean absolute error. RESULTS: The regression tree model outperformed all 9 metalearner models. Tree results suggested group-based PT yielded the largest improvement in mean WOMAC score. Only two subgroups were identified: baseline WOMAC score≤44 versus >44. Group-based PT was the optimal treatment regardless of baseline WOMAC score, but results were more ambiguous for patients with higher initial WOMAC score. For all 3 EBIs, patients with higher baseline WOMAC score made greater improvements. CONCLUSION: Results suggest individuals with moderate or greater KOA symptoms may benefit more from EBIs than those with less severe symptoms and that group-based PT is a promising approach for KOA.