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

ABSTRACT:

Rationale

More targeted management of severe acute pediatric asthma could improve clinical outcomes.

Objectives

To identify distinct clinical phenotypes of severe acute pediatric asthma using variables obtained in the first 12 h of hospitalization.

Methods

We conducted a retrospective cohort study in a quaternary care children's hospital from 2014 to 2022. Encounters for children ages 2-18 years admitted to the hospital for asthma were included. We used consensus k means clustering with patient demographics, vital signs, diagnostics, and laboratory data obtained in the first 12 h of hospitalization.

Measurements and main results

The study population included 683 encounters divided into derivation (80%) and validation (20%) sets, and two distinct clusters were i

SUBMITTER: Rogerson C 

PROVIDER: S-EPMC11601023 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature