Developing a prediction model for persistent airflow limitation in asthmatic children.

ABSTRACT:

Background

A small proportion of asthmatic children will develop persistent airflow limitation (PAL). The purpose of this study was to develop predictive models using classical and machine learning methods to identify asthmatic children at risk of PAL.

Methods

A total of 1,671 asthmatic children were enrolled between January 1, 2019, and December 31, 2020, to serve as training and internal validation sets. Temporal validation included 401 patients from January 1, 2021, to December 31, 2021. PAL was determined in the third year after enrollment, defined as a fixed forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) ratio below 0.75. Predictors included demographic and clinical data. Machine learning algorithms, including random forest (RF) and extreme grad

SUBMITTER: Xiong S 

PROVIDER: S-EPMC12603487 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature