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.