How reliably can algorithms identify eosinophilic asthma phenotypes using non-invasive biomarkers?

Background and Aims Asthma is a heterogeneous respiratory disease that encompasses different inflammatory and functional endophenotypes. Many non-invasive biomarkers has been investigated to its pathobiology. Heany et al proposed a clinical algorithm that classifies severe asthmatic patients into likely-eosinophilic phenotypes, based on accessible biomarkers: PBE, current treatment, FeNO, presence of nasal polyps (NP) and age of onset. Materials and Methods We assessed the concordance between the algorithm proposed by Heany et al. with sputum examination, the gold standard, in 145 asthmatic patients of the MEGA cohort with varying grades of severity. Results No correlation was found between both classifications 0.025 (CI = 0.013-0.037). Moreover, no relationship was found between sputum eosinophilia and peripheral blood eosinophilia count in the total studied population. Discussion and Conclusion In conclusion, our results suggest that grouping the biomarkers proposed by Heany et al. are insufficient to diagnose eosinophilic phenotypes in asthmatic patients. Sputum analysis remains the gold standard to assess airway inflammation.

Automated summary

The study found that using the algorithm proposed by Heany et al. to classify asthma patients is insufficient for diagnosing eosinophilic phenotypes, and sputum analysis remains the gold standard for assessing airway inflammation.