Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children.

ABSTRACT: Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment. By applying machine learning algorithms assessed with respect to their accuracy in predicting treatment outcome, we have successfully identified 4 distinct clusters in a pediatric asthma cohort with specific treatment outcome patterns according to changes in lung function (FEV₁ and MEF₅₀), airway inflammation (FENO) and disease control likely affected by discrete phenotypes at initial disease presentation, differing in the type and level of inflammation, age of onset, comorbidities, certain genetic and other physiologic traits. The smallest and the largest of t

SUBMITTER: Banic I 

PROVIDER: S-EPMC8330019 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature