Diagnosing eosinophilic asthma using a multivariate prediction model based on blood granulocyte responsiveness

Abstract

Background: The identification of inflammatory asthma phenotypes, using sputum analysis, has proven its value in diagnosis and disease monitoring. However due to technical limitations of sputum analysis there is a strong need for fast and non-invasive diagnostics. This study included the activation state of eosinophils and neutrophils in peripheral blood to phenotype and monitor asthma. Objectives: To (1) construct a multivariable model using the activation state of blood granulocytes, (2) compare its diagnostic value with sputum eosinophilia as gold standard and (3) validate the model in an independent patient cohort. Methods: Clinical parameters, activation of blood granulocytes and sputum characteristics were assessed in 115 adult asthma patients (training cohort/Utrecht) and 34 patients (validation cohort/Oxford). Results: The combination of blood eosinophil count, FeNO, ACQ, medication use, nasal polyposis, aspirin sensitivity and neutrophil/eosinophil responsiveness upon stimulation with fMLF, was found to identify sputum eosinophilia with 90.5% sensitivity and 91.5% specificity in the training cohort and with 77% sensitivity and 71% specificity in the validation cohort (relatively high percentage on OCS). Conclusions: The proposed prediction model identifies eosinophilic asthma without the need for sputum induction. The model forms a non-invasive and externally validated test to assess eosinophilic asthma in patients not on OCS.