Using machine learning to define mepolizumab treatment response at 2 years in patients with chronic rhinosinusitis with nasal polyps.

Introduction: Using machine learning to identify clinical biomarkers for determining optimal response to mepolizumab in chronic rhinosinusitis with nasal polyps. Methods: Single center retrospective observational study with 84 CRSwNP patients treated with mepolizumab. We evaluated 4 machine learning algorithms: Decision Tree, Logistic Regression, K-Nearest Neighbors and Extreme Gradient Boosting. K-Fold cross-validation incorporating hyperparameter optimization in the process was used to ensure robustness and prevent overfitting. Results: After 6, 12 and 24 months, SNOT-22, VAS overall symptom score, VAS-smell, asthma control test (ACT) and nasal polyp score (NPS) significantly improved ( Conclusions: Machine learning models, particularly XGBoost, can predict real-world super-response to mepolizumab in severe CRSwNP by identifying key predictors like high baseline BEC, high baseline BNC and AERD comorbidity. These insights have the potential to refine CRSwNP treatment strategies and support clinical decision-making, ultimately enhancing patient outcomes by predicting treatment response prior to starting medication.