Machine learning predicts clinical response to platelet-rich plasma therapy in knee osteoarthritis

Objective: The purpose of the study is to predict clinical responses to platelet-rich plasma (PRP) therapy in patients with knee osteoarthritis (KOA) before treatment and to identify the crucial efficacy predictors. Methods: We reviewed the multidimensional clinical data of 102 KOA patients who underwent PRP therapy with a retrospective approach, including anthropometrics and blood indices. We defined the response to treatment as a reduction in the Numerical Rating Scale (NRS) pain score of ≥2 at the 6-month follow-up. After comprehensive data preprocessing, including imputation, Yeo-Johnson transformation, standardization, and balancing via SMOTETomek, 33 clinical features were retained. We then created and verified multiple machine learning models using 10-fold cross-validation (CV) on a 70% training cohort. The most effective model was subsequently validated on the 30% test cohort, and feature contributions were interpreted using SHapley Additive exPlanations (SHAP) analysis. Results: The Gradient Boosting Classifier showed the best overall performance among all tested models. The final model achieved a prediction accuracy of 90.3%, an area under the curve (AUC) of 0.862, and a Cohen's kappa coefficient of 0.611 on the test set, indicating high predictive consistency. The SHAP interpretability analysis revealed three biomarkers-osmotic pressure, lipoprotein(a) [Lp(a)], and uric acid-as the clinical factors most strongly associated with treatment response. Conclusion: Clinical character-based machine learning models are effective in predicting PRP therapy outcomes prior to treatment. Such results provide a strong basis for introducing individualized therapeutic modalities in the management of osteoarthritis.