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Related Experiment Video

Updated: Jun 18, 2025

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

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Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning.

Colin Rogerson^1,2, L Nelson Sanchez-Pinto³, Benjamin Gaston¹

¹Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Pediatric Pulmonology

|July 29, 2024

Summary

This study identified two distinct clinical phenotypes in severe acute pediatric asthma. These phenotypes show different patient characteristics and clinical outcomes, paving the way for more targeted management.

Keywords:

asthma informatics machine learning pediatrics

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Published on: October 10, 2018

Area of Science:

Pediatric pulmonology
Clinical informatics
Biostatistics

Background:

Targeted management of severe acute pediatric asthma is crucial for improving patient outcomes.
Current treatment approaches may not adequately address the heterogeneity of severe asthma presentations in children.

Purpose of the Study:

To identify distinct clinical phenotypes of severe acute pediatric asthma.
To utilize early hospitalization data (within the first 12 hours) for phenotype identification.

Main Methods:

Retrospective cohort study (2014-2022) including children aged 2-18 years admitted for asthma.
Consensus k-means clustering applied to demographics, vital signs, diagnostics, and laboratory data from the first 12 hours of admission.
Study population of 683 encounters divided into derivation (80%) and validation (20%) sets.

Main Results:

Two distinct clusters (phenotypes) were identified.
Cluster 2 (32% of encounters) included older children, more males, Black race, and non-Hispanic ethnicity compared to Cluster 1.
Cluster 2 showed less improvement in vital signs, different neutrophil/lymphocyte percentages, higher rates of invasive mechanical ventilation, longer hospital stays, and higher mortality.

Conclusions:

Two distinct clinical phenotypes of severe acute pediatric asthma were identified.
These phenotypes are characterized by differing clinical features and patient outcomes.
Findings support the need for phenotype-specific management strategies in severe pediatric asthma.