Using machine learning to define mepolizumab treatment response at 2 years in patients with chronic rhinosinusitis with nasal polyps
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.
Authors
- Miriam del Carmen Marrero-Ramos (ORCID: https://orcid.org/0000-0002-6679-0557)
- María Sandra Domínguez-Sosa (ORCID: https://orcid.org/0000-0001-9445-4537)
- Teresa Carrillo (ORCID: https://orcid.org/0000-0002-3047-8908)
- Jesús Benitez-Rosario
- María Soledad Cabrera-Ramírez (ORCID: https://orcid.org/0000-0001-6164-107X)
- Carlos Cabrera-López
Institutions
- Universidad de Las Palmas de Gran Canaria (ES)
- Hospital Universitario de Gran Canaria Doctor Negrín (ES)
Publication Details
- Journal
- Frontiers in Allergy
- Published
- 2026-02-13
- DOI
- https://doi.org/10.3389/falgy.2026.1710163
- Primary Topic
- Sinusitis and nasal conditions
- Type
- article
- Field-Weighted Citation Impact
- 0.00