JMIR Publications

ABSTRACT

Background:

Knee osteoarthritis (OA) is a widespread chronic condition that impairs mobility and diminishes quality of life. Despite the proven benefits of exercise therapy and patient education in managing the OA symptoms pain and functional limitations, these strategies are often underutilized. To motivate and enhance patient engagement personalized outcome prediction models can be utilized. However, the accuracy of existing models in predicting changes in knee pain outcomes remains insufficiently examined.

Objective:

To validate existing models and introduce a concise personalized model predicting changes in knee pain from before to after participating in a supervised patient education and exercise therapy program (GLA:D®) among patients with knee OA.

Methods:

Our prediction models leverage self-reported patient information and functional measures. To refine the number of variables, we evaluated the variable importance and applied clinical reasoning. We trained random forest regression models and compared the rate of true predictions of our models with those utilizing average values. In supplementary analyses, we additionally considered recently added variables to the GLA:D® registry.

Results:

We evaluated the performance of a full, continuous, and concise model including all 34, all eleven continuous, and the six most predictive variables respectively. All three models performed similarly and were comparable to the existing model, with R-squares of 0.31-0.32 and RMSEs of 18.65-18.85 – despite our increased sample size. Allowing a deviation of 15 VAS points from the true change in pain, our concise model and utilizing the average values estimated the change in pain at 58% and 51% correctly, respectively. Our supplementary analysis led to similar outcomes.

Conclusions:

Our concise personalized prediction model provides more often accurate predictions for changes in knee pain after the GLA:D® program than utilizing average pain improvement values. Neither the increase in sample size nor the inclusion of additional variables improved previous models. To improve predictions, variables beyond those identified in the literature and collected as part of GLA:D® are required.