Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous condition, and a subset of patients exhibits a type-2 inflammatory phenotype characterized by elevated eosinophils, IgE, or allergic comorbidities. This phenotype is clinically important because it is linked to frequent exacerbations and has a distinct response to inhaled corticosteroids and targeted biologic therapies. However, type-2 inflammation is often under-recognized in routine practice due to the lack of simple, low-cost screening tools based on routinely available clinical indicators.
The research question is: Can a predictive screening model derived from UK Biobank data reliably identify COPD patients with a likely type-2 inflammatory phenotype?
This project aims to:
1.Identify a COPD cohort within UK Biobank using spirometry and clinical criteria.
2.Develop a multivariable model using routinely collected data-including eosinophil counts, demographics, smoking history, comorbidities, and environmental or occupational exposures-to predict the probability of type-2 inflammation.
3.Assess model discrimination and calibration through internal validation and describe the distribution and characteristics of type-2 inflammation within the UKB COPD population.
A subsequent planned phase will externally validate the model using a prospective COPD cohort from our hospital, enabling evaluation of generalizability and clinical applicability.
