Individualized predictions of changes in knee pain, quality of life and walking speed following patient education and exercise therapy in patients with knee osteoarthritis – a prognostic model study

Objective: 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. Methods: 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 R². Lastly, we validated and compared our models with the average improvements via the mean differences in an independent validation data set from the GLA:D® registry (n = 2,896) collected 1 year later than the data used to build the models. Results: Validating our models including the best performing variables yielded R²s of 0.34 for pain intensity, 0.18 for knee-related QoL, and 0.07 for walking speed. The absolute mean differences between model predictions and the true outcomes were 14.65 mm, 10.32 points, and 0.14 m/s, respectively, and similar to the absolute mean differences of 17.64, 11.28 and 0.14 observed when we subtracted the average improvements from the true outcomes. Conclusion: 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.