Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic

ABSTRACT: Abstract

Background

During the coronavirus disease 2019 (COVID‐19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin‐tolerant asthma.

Methods

We used a prospective database of 135 patients with non‐steroidal anti‐inflammatory drug (NSAID)–exacerbated respiratory disease (NERD) and 81 NSAID‐tolerant (NTA) patients w

SUBMITTER: Gawlewicz‐Mroczka A 

PROVIDER: S-EPMC9579891 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature