@Article{info:doi/10.2196/60162,
author="Rafiei, Mahdie
and Das, Supratim
and Bakhtiari, Mohammad
and Roos, Ewa Maria
and Skou, S{\o}ren T
and Gr{\o}nne, Dorte T
and Baumbach, Jan
and Baumbach, Linda",
title="Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study",
journal="JMIR Rehabil Assist Technol",
year="2025",
month="Mar",
day="21",
volume="12",
pages="e60162",
keywords="osteoarthritis; prediction; pain intensity; exercise therapy; machine learning",
abstract="Background: Knee osteoarthritis (OA) is a common chronic condition that impairs mobility and diminishes quality of life. Despite the proven benefits of exercise therapy and patient education in managing OA pain and functional limitations, these strategies are often underused. To motivate and enhance patient engagement, personalized outcome prediction models can be used. However, the accuracy of existing models in predicting changes in knee pain outcomes remains insufficiently examined. Objective: This study aims 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 using 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 variables, all 11 continuous variables, and the 6 most predictive variables, respectively. All three models performed similarly and were comparable to the existing model, with R2 values of 0.31‐0.32 and root-mean-squared errors of 18.65‐18.85---despite our increased sample size. Allowing a deviation of 15 (visual analog scale) points from the true change in pain, our concise model correctly estimated the change in pain in 58{\%} of cases, while using average values that resulted in 51{\%} accuracy. 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 using average pain improvement values. Neither the increase in sample size nor the inclusion of additional variables improved previous models. Based on current knowledge and available data, no better predictions are possible. Guidance is needed on when a model's performance is good enough for clinical practice use. ",
issn="2369-2529",
doi="10.2196/60162",
url="https://rehab.jmir.org/2025/1/e60162",
url="https://doi.org/10.2196/60162"
}