Evidence map›Paper›PMID 40688461›Full record

ArticleInternational journal of general medicine2025

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

Qianqian Li, Yinghong Fan, Ronghua Luo, Jie Hu, Li Wang, Tao Ai

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Article in International journal of general medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Current understanding and future directions in severe asthma through artificial intelligence-integrated multi-omic approaches.European respiratory review : an official journal of the European Respiratory Society · 2026
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  3. Article
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qianqian LiPediatric 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.
Yinghong FanPediatric 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.
Ronghua LuoPediatric 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.
Jie HuPediatric 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.
Li Wang *Pediatric 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.
Tao Ai *Pediatric 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To analyse the related risk factors of moderate to severe asthma attack in children by logistic regression and decision tree. Patients and Methods: A retrospective analysis of clinical data of children diagnosed with asthma attacks in our hospital from January 2020 to August 2023 was conducted. The patients were divided into mild group (n=459, 57.02%) and moderate to severe group (n=346, 42.98%). Related risk factors of moderate to severe asthma attack in children were analyzed by univariate logistic regression, and then multivariate logistic regression and decision tree model were obtained. Results: The results of univariate logistic regression showed that there were significant differences between the two groups in age, medical history, allergy history, family history, C-reactive protein (CRP), neutrophil percentage (NEU%), Mycoplasma pneumoniae (MP) infection, Rhinovirus (RV) infection (all p < 0.05). The results of multivariate logistic regression showed that age (≥6 years) (OR=1.636, 95% CI=1.046-2.559), medical history (OR=1.460, 95% CI=1.063-2.006), allergy history (OR=2.387, 95% CI=1.733-3.288), family history (OR=2.564, 95% CI=1.619-4.058), NEU% (OR=1.020, 95% CI=1.009-1.031), MP infection (OR=2.140, 95% CI=1.571-2.916), RV infection (OR=4.546, 95% CI=2.274-9.089) were related risk factors of moderate to severe asthma attack in children (all p<0.05). The decision tree model showed that MP infection, CRP, allergy history, NEU%, and medical history were risk factors of moderate to severe asthma attacks in children, with importance levels of 0.41, 0.29, 0.134, 0.130, and 0.061, respectively. Multivariate logistic regression (AUC=0.733, 95% CI: 0.698~0.767) and decision tree (AUC=0.694, 95% CI: 0.658~0.731) both exhibited good prediction accuracy. Conclusion: Allergic history, medical history, MP infection, and increased NEU% were related risk factors that predict moderate to severe asthma attack in children. Multivariate logistic regression and decision tree both had a good predictive effect for analyzing the risk factors of moderate to severe asthma attack in children.

Indexed as

childhood asthmadecision treelogistic regressionmoderate to severe attackrisk factors

Identifiers

PMID40688461
PMCPMC12275991

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