Highlights
- •Machine learning approach can identify the main predictors of severe asthma exacerbations requiring hospital admission.
- •Blood eosinophil count identifies a more severe pattern among those requiring hospital admission.
- •The prognostic role of blood eosinophil count in asthma exacerbations requiring hospital admission was independent from the allergic or non-allergic aetiology.
Abstract
Background
One of the main problems in poorly controlled asthma is the access to the Emergency
Department (ED). Using a machine learning (ML) approach, the aim of our study was
to identify the main predictors of severe asthma exacerbations requiring hospital
admission.
Methods
Consecutive patients with asthma exacerbation were screened for inclusion within 48
hours of ED discharge. A k-means clustering algorithm was implemented to evaluate
a potential distinction of different phenotypes. K-Nearest Neighbor (KNN) as instance-based
algorithm and Random Forest (RF) as tree-based algorithm were implemented in order
to classify patients, based on the presence of at least one additional access to the
ED in the previous 12 months.
Results
To train our model, we included 260 patients (31.5% males, mean age 47.6 years). Unsupervised
ML identified two groups, based on eosinophil count. A total of 86 patients with eosinophiles
≥370 cells/µL were significantly older, had a longer disease duration, more restrictions
to daily activities, and lower rate of treatment compared to 174 patients with eosinophiles
<370 cells/μL. In addition, they reported lower values of predicted FEV1 (64.8±12.3% vs. 83.9±17.3%) and FEV1/FVC (71.3±9.3 vs. 78.5±6.8), with a higher amount of exacerbations/year. In supervised ML, KNN achieved
the best performance in identifying frequent exacerbators (AUROC: 96.7%), confirming
the importance of spirometry parameters and eosinophil count, along with the number
of prior exacerbations and other clinical and demographic variables.
Conclusions
This study confirms the key prognostic value of eosinophiles in asthma, suggesting
the usefulness of ML in defining biological pathways that can help plan personalized
pharmacological and rehabilitation strategies.
Keywords
Abbreviations:
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Article info
Publication history
Published online: July 31, 2022
Accepted:
July 26,
2022
Received in revised form:
July 14,
2022
Received:
May 24,
2022
Identification
Copyright
© 2022 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
