To facilitate shared decision-making for patients with knee osteoarthritis (OA), we aimed at building clinically applicable models to predict the individual change in pain intensity (VAS scale 0-100), knee-related quality of life (QoL) (KOOS QoL score 0-100) and walking speed (m/sec) immediately following two educational and 12 supervised exercise therapy sessions.
We used data from patients with knee OA from the 'Good Life with osteoArthritis in Denmark' (GLA:D®) registry (n = 6,767). From 51 patient characteristics, we selected the best performing variables to predict the outcomes via random forest regression. We evaluated model performance via R
Validating our models including the best performing variables yielded R
Despite including 51 potential predictors, we were unable to predict changes in individuals' pain intensity, knee-related QoL and walking speed with clinically relevant greater precision than the respective group average outcomes. Therefore, average prediction values can be used to inform patients about expected outcomes.