# Determinants and Phenotypes of Poorly Controlled COPD Using the RADAR Score: A Cohort in Real-World Primary Care

**Authors:** Myriam Calle Rubio, Soha Esmaili, Juan Luis Rodríguez Hermosa, Imán Esmaili, María Carmen Antón Sanz, Norma Doria Carlin, Elías Ekech Mesa, Mónica González Álvarez, Patricia Privado Martínez, Alberto Serrano López De Las Hazas, José Artica García, María Teresa Marín Becerra, Rafael Sánchez-del Hoyo, Medardo Montenegro

PMC · DOI: 10.3390/jcm15031283 · Journal of Clinical Medicine · 2026-02-05

## TL;DR

This study finds that nearly half of COPD patients in primary care have poor disease control, often due to clinician misperception and modifiable factors like smoking and complex treatment regimens.

## Contribution

The study introduces the RADAR score to objectively assess COPD control and identifies distinct phenotypes of poorly controlled patients in real-world primary care settings.

## Key findings

- 45.7% of COPD patients on maintenance inhaled therapy had poor clinical control as measured by the RADAR score.
- Exacerbator phenotypes (eosinophilic and non-eosinophilic) were the strongest independent determinants of poor control.
- Five distinct clinical phenotypes of poorly controlled COPD were identified, differing in treatment complexity, comorbidities, and adherence.

## Abstract

Background: Poor clinical control in Chronic Obstructive Pulmonary Disease (COPD) is prevalent, yet the interplay of disease severity, modifiable factors, and clinician perception remains poorly understood. This study aimed to determine the frequency of poor control, identify its independent determinants, and characterize the heterogeneity of the poorly controlled population receiving maintenance inhaled therapy with various devices in primary care. Methods: In a multicenter, cross-sectional analysis of 988 patients from the Study SIMPLIFY, clinical control of COPD was classified using the objective RADAR score. We used multivariable logistic regression and Machine Learning (Random Forest with SHAP analysis) to identify determinants of poor control (RADAR ≥ 4) and k-medoids cluster analysis to characterize the poorly controlled subgroup (n = 452). Results: Nearly half the cohort (45.7%, n = 452) had poor clinical control. Agreement between physician-assessed control (five categories) and RADAR classification was 49.3%, with overestimation in 34.0% and underestimation in 16.7% of cases (Cohen’s κ = −0.081; weighted κ = −0.037). The strongest independent determinants were the exacerbator phenotypes (eosinophilic aOR 6.85; non-eosinophilic aOR 4.91). Key modifiable factors included active smoking (aOR 1.92), lower TAI-12 adherence score (per point; aOR 0.96), high dosing frequency (≥4 inhalations/day; aOR 1.54) and high inhaler burden (≥3 devices; aOR 1.84). Machine learning analysis identified clinical phenotype and adherence behavior as the top two scale-independent predictors of poor control. Cluster analysis of the poorly controlled group revealed five reproducible and clinically meaningful phenotypes (C0–C4), primarily separated by treatment complexity, comorbidities, and adherence. Conclusions: Poor clinical control is common and critically under-recognized in primary care patients with COPD on maintenance inhaled therapy. This is driven by a profound clinician perception gap and a failure to address key modifiable determinants, such as high dosing frequency, regimen complexity, and poor adherence, which likely drives therapeutic inertia. Our findings underscore the need to integrate objective tools to unmask poor control and highlight the importance of treatment simplification. The identification of distinct clinical phenotypes provides a roadmap toward a more personalized, evidence-based standard of care.

## Linked entities

- **Diseases:** Chronic Obstructive Pulmonary Disease (MONDO:0005002), COPD (MONDO:0005002)

## Full-text entities

- **Diseases:** COPD (MESH:D029424)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

40 references — full list in the complete paper: https://tomesphere.com/paper/PMC12898748/full.md

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Source: https://tomesphere.com/paper/PMC12898748