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

## TL;DR

The paper introduces Seleida, an automated model for assessing COPD control and phenotyping using electronic health records, aiming to improve detection and treatment in primary care.

## Contribution

Seleida is a novel deterministic model that uses prescribing data to estimate COPD control and infer phenotypes without machine learning.

## Key findings

- Seleida showed perfect agreement between phenotyping systems (Cohen κ=1.00) and substantial concordance with clinician-assigned profiles (Cohen κ=0.70).
- The model enables individualized risk estimation and population-level case identification using high-frequency prescribing data.
- Seleida's transparent logic and low data requirements allow integration into diverse digital infrastructures, including resource-limited settings.

## Abstract

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.

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.

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.

Seleida showed perfect agreement between phenotyping systems (Cohen κ=1.00; P<.001) and substantial concordance with clinician-assigned profiles (Cohen κ=0.70; P<.001). The model operates transparently, without machine learning, and can be embedded into EHR platforms or applied manually using a visual framework. It enables individualized risk estimation, phenotype-driven treatment planning, and population-level case identification—particularly in settings with limited access to traditional diagnostic tools.

Seleida provides a reproducible and interpretable framework for COPD control monitoring using high-frequency prescribing data. Its transparent logic, low data burden, and interoperability enable integration across diverse digital infrastructures, including resource-limited settings. By supporting both individualized care and population-level risk stratification, Seleida bridges predictive analytics with real-world clinical decision-making. Ongoing multicenter validation will determine its generalizability, clinical impact, and cost-effectiveness at scale.