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Related Risk Factors That Predict Moderate to Severe Asthma Attack in Children: Analysis Based on Logistic Regression

Qianqian Li1, Yinghong Fan1, Ronghua Luo1

  • 1Pediatric Respiratory Medicine Department, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610000, People's Republic of China.

International Journal of General Medicine
|July 21, 2025
PubMed

Insights

Allergy history, medical history, Mycoplasma pneumoniae (MP) infection, and elevated neutrophil percentage (NEU%) are key risk factors for moderate to severe asthma attacks in children. These factors, identified through logistic regression and decision tree analysis, aid in predicting severe asthma exacerbations.

Area of Science:

  • Pediatric Pulmonology
  • Epidemiology
  • Medical Informatics

Background:

  • Asthma is a common chronic respiratory disease in children.
  • Identifying risk factors for severe asthma attacks is crucial for effective management and prevention.
  • Previous studies have explored various triggers, but comprehensive analysis using advanced statistical models is ongoing.

Purpose of the Study:

  • To analyze the risk factors associated with moderate to severe asthma attacks in pediatric patients.
  • To compare the predictive capabilities of logistic regression and decision tree models in identifying these risk factors.

Main Methods:

  • Retrospective analysis of clinical data from children with asthma attacks (January 2020 - August 2023).
  • Patients categorized into mild and moderate to severe attack groups.
  • Univariate and multivariate logistic regression, along with decision tree analysis, were employed to identify risk factors.

Main Results:

  • Significant risk factors identified by multivariate logistic regression include age (≥6 years), medical history, allergy history, family history, elevated neutrophil percentage (NEU%), Mycoplasma pneumoniae (MP) infection, and Rhinovirus (RV) infection.
  • The decision tree model highlighted MP infection, C-reactive protein (CRP), allergy history, NEU%, and medical history as key predictors.
  • Both multivariate logistic regression (AUC=0.733) and decision tree (AUC=0.694) demonstrated good prediction accuracy.

Conclusions:

  • Allergic history, medical history, MP infection, and increased NEU% are significant risk factors predicting moderate to severe asthma attacks in children.
  • Both multivariate logistic regression and decision tree models are effective tools for analyzing and predicting the risk of severe asthma attacks in pediatric populations.
Abstract

Keywords:
childhood asthmadecision treelogistic regressionmoderate to severe attackrisk factors

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