Development of Explainable Machine Learning Models to Predict Outcomes After Platelet-Rich Plasma Injections for Knee Osteoarthritis.

Background: Platelet-rich plasma (PRP) has been increasingly used to treat knee osteoarthritis, but its efficacy remains unclear due to the variability of outcomes. Machine learning (ML) can improve the ability to predict responses to PRP treatment by identifying specific baseline characteristics of patients who may have greater clinical improvements. Purpose: To develop and evaluate an ML model predicting clinical outcomes after PRP injection for knee osteoarthritis. Study design: Cohort study (prognosis); Level of evidence, 2. Methods: This retrospective study utilized patient demographics and patient-reported outcome measures (PROMs) from 191 patients who received PRP injections for knee osteoarthritis. Patients were randomly split into a training set (80%) and a testing set (20%). The primary outcome was predicting the achievement of the minimal clinically important difference at 6 months after treatment, defined as a ≥10-point increase in the Knee injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) and a ≥20% decrease in the numeric pain rating scale for knee pain score. Ten preinjection variables, including demographics and baseline PROMs, were evaluated. Multiple ML algorithms were developed and evaluated on sensitivity, accuracy, precision, area under the receiver operating characteristic curve (AUC)-ROC, and Results: The Explainable Boosting Machine (EBM) algorithm was determined to be the best algorithm due to its greater explainability (AUC-ROC, 0.81 [95% CI, 0.65-0.94]; Conclusion: ML models effectively predicted a clinically meaningful improvement at 6 months after PRP injection for knee osteoarthritis. The EBM was the algorithm with the best performance, with the PROMIS Mental and Physical scores and the KOOS Jr score being the most influential predictors. Additional independent studies are needed to externally validate this model.