Automated Chronic Obstructive Pulmonary Disease Phenotyping and Control Assessment in Primary Care: Retrospective Multicenter Study Using the Seleida Model

Abstract

Background: Chronic obstructive pulmonary disease (COPD) remains a leading global health burden. In primary care, the inconsistent availability of spirometry and symptom scores limits the detection of patients with poor disease control. There is a pressing need for scalable, data-driven tools that leverage routinely collected clinical information to support timely, equitable, and guideline-concordant interventions. Objective: This study aims to validate the performance of Seleida—a fully automated, deterministic, and bijective model for COPD control assessment and phenotyping—using real-world primary care data and to evaluate its feasibility for integration into electronic health record (EHR)–based informatics systems. Methods: Seleida estimates the probability of poor control (Pr) using two objective EHR variables: (1) annual dispensations of short-acting bronchodilators—specifically short-acting β2-agonists (SABA), short-acting muscarinic antagonists (SAMA), or both, and (2) number of dispensed antibiotic courses for bronchitis or COPD exacerbations. Its bijective structure supports both forward risk estimation and reverse phenotype inference. In a retrospective cohort of 106 patients, agreement was assessed between 2 phenotyping systems (a 126-combination model and a streamlined 21-combination version) and with clinician-assigned classifications. Due to sample size limitations, a provisional risk threshold of Pr>.50 was adopted for internal stratification. Results: Seleida showed perfect agreement between phenotyping systems (Cohen κ=1.00; P