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Dynamic Risk-Stratification Models for Bronchopulmonary Dysplasia in Extremely Preterm Very Low Birth Weight Infants

Ting Zhao1, Ning An2, Yanping Zhu1

  • 1Department of Neonatology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.

Pediatric Pulmonology
|October 15, 2025
PubMed

Insights

Machine learning models accurately predict bronchopulmonary dysplasia (BPD) risk in extremely preterm (EP) infants. These tools enable early identification and targeted interventions for better neonatal outcomes.

Area of Science:

  • Neonatal Medicine
  • Computational Biology
  • Pediatric Pulmonology

Background:

  • Bronchopulmonary dysplasia (BPD) is a significant complication in extremely preterm (EP) or very low birth weight (VLBW) infants.
  • Early identification of BPD risk is crucial for timely intervention and improved outcomes.

Purpose of the Study:

  • To identify independent risk factors for BPD at multiple postnatal time points.
  • To develop and validate machine learning (ML)-based dynamic prediction models for BPD risk stratification in EP/VLBW infants.

Main Methods:

  • Retrospective and prospective data from EP/VLBW infants (gestational age < 32 weeks or birth weight < 1500g) were analyzed.
  • Four ML algorithms (logistic regression, random forest, XGBoost, gradient boosting decision tree) were trained using clinical data from postnatal days 1, 3, and 7.
  • Models were validated internally and externally.

Main Results:

  • Ordinal logistic regression identified key risk factors including gestational age, birth weight, prenatal steroids, and various clinical markers.
  • Logistic Regression (LR) and XGBoost (XGB) models showed high predictive performance (AUC: 0.810-0.837) for BPD stratification.
  • Models were successfully validated on independent prospective data.

Conclusions:

  • ML-based dynamic prediction models for BPD in EP/VLBW infants were successfully developed and validated.
  • These models enable early risk identification, facilitating targeted interventions.
  • The study supports the use of ML for improving neonatal care and outcomes in high-risk infants.
Abstract

Keywords:
bronchopulmonary dysplasiadynamic predictionmachine learningprediction modelpretermrisk factors

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