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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

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Serological markers, pulmonary function, and prognosis in pediatric asthma: predictive model development and

Lifang Liu1, Youyou Feng1

  • 1Pulmonary Function Room of Zhejiang Hospital Hangzhou 310000, Zhejiang, China.

American Journal of Translational Research
|July 17, 2025
PubMed

Insights

Integrating blood markers and lung function tests improves prediction of pediatric asthma outcomes. This approach aids in developing personalized treatment plans for better disease management.

Area of Science:

  • Pulmonology
  • Immunology
  • Biomarker Research

Background:

  • Pediatric asthma is a complex respiratory condition with unpredictable exacerbations and outcomes.
  • Accurate prognostic prediction is crucial for effective management of childhood asthma.

Purpose of the Study:

  • To enhance prognostic prediction in pediatric asthma.
  • To integrate serological markers with pulmonary function parameters for improved forecasting.

Main Methods:

  • Retrospective analysis of 318 pediatric asthma patients, with external validation on 283 patients.
  • Measurement of serological markers (WBC, eosinophils, interleukins, 14-3-3β, IgE) and pulmonary function (FEV1, FVC).
  • Statistical analysis including logistic regression and ROC curve analysis to develop and validate a predictive model.

Main Results:

  • Elevated WBC, eosinophils, 14-3-3β, IgE, IL-4, and IL-7 were linked to poorer prognosis.
  • Reduced IL-10 and higher FEV1/FVC values correlated with better outcomes.
  • The integrated model showed strong predictive performance (AUC 0.818-0.874).

Conclusions:

  • Integrating serological biomarkers and pulmonary function indices offers a robust method for predicting pediatric asthma prognosis.
  • This integrated approach supports the development of individualized management strategies for pediatric asthma.
Abstract

Keywords:
Pediatric asthmapersonalized treatmentpredictive modelprognosispulmonary functionserological markers

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