Machine learning reveals limited predictive value of clinical factors for asthma exacerbations.

ABSTRACT: While predictors of asthma exacerbation risk are generally well established, predictors of exacerbation severity remain largely undefined. Identifying robust clinical predictors of exacerbation severity is essential to support tailored management strategies and optimize resource allocation. This study leverages machine learning to evaluate the predictive value of clinical factors for exacerbation severity in a real-world emergency department setting. A retrospective cohort study was performed using medical records of 367 adults (644 exacerbations) who presented to the Amsterdam UMC emergency department between 2013 and 2020. Five severity outcomes were investigated: hospital admission, ICU admission, length of stay, oxygenation efficiency (SpO₂/FiO₂), and National Early Warning Score (NEWS

SUBMITTER: Duijvelaar E 

PROVIDER: S-EPMC12508110 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature