Related Experiment Video
Updated: Sep 15, 2025
Intratracheal Instillation of Stem Cells in Term Neonatal Rats
Published on: May 4, 2020
Machine learning-based risk prediction models for bronchopulmonary dysplasia in preterm infants: a high-altitude
Heng Zhang1, Fei Wang1, Ou Jiang1
1Faculty of Medicine of Kunming University of Science and Technology, Kunming, Yunnan Province, China.
Insights
This study identifies key risk factors for bronchopulmonary dysplasia (BPD) in high-altitude preterm infants. Machine learning models accurately predict BPD, enabling early interventions for better outcomes.
Area of Science:
- Neonatology
- Pediatric Respiratory Medicine
- High-Altitude Physiology
Background:
- Bronchopulmonary dysplasia (BPD) is a major cause of morbidity in preterm infants.
- BPD development and severity at high altitudes (>1500m) are poorly understood.
- This study addresses the knowledge gap by investigating altitude-specific risk factors.
Purpose of the Study:
- Identify altitude-specific risk factors for BPD in preterm infants.
- Develop and validate interpretable machine learning models for BPD prediction.
- Inform early interventions to improve outcomes for high-altitude preterm infants.
Main Methods:
- Retrospective matched cohort study of 378 preterm infants (<32 weeks gestation) at high altitude (1500m).
- Collected maternal, perinatal, and postnatal data for 189 BPD cases and 189 controls.
- Developed and evaluated XGBoost, logistic regression, and random forest models using SHAP analysis for interpretation.
Main Results:
- Key risk factors for BPD: maternal hypertension, initial oxygen >30%, and lack of breast milk feeding.
- Severe BPD associated with prolonged ventilation, elevated C-reactive protein, and PDA.
- XGBoost model achieved AUC 0.89, F1 0.82, MCC 0.73, balanced accuracy 0.85.
Conclusions:
- First comprehensive analysis of BPD risk factors at high altitude.
- Validated effective and interpretable machine learning models for BPD prediction.
- Emphasizes altitude-specific risk assessment and model-guided interventions for vulnerable infants.
Background:
Bronchopulmonary dysplasia (BPD) is a significant cause of morbidity in preterm infants, yet its development and severity at high altitudes (>1500 m) remain poorly understood. This study aimed to identify altitude-specific risk factors and develop robust, interpretable predictive models for BPD in this unique population.
Methods:
In this retrospective matched cohort study, 378 preterm infants (<32 weeks gestation, <1500 g birth weight) admitted to a high-altitude (1500 m) NICU(Neonatal Intensive Care Unit) between 2019 and 2023 were analysed. The cohort included 189 BPD cases (91 mild, 61 moderate, 37 severe) and 189 matched controls. Maternal, perinatal and postnatal data were collected. Machine learning models (XGBoost, logistic regression, random forest) were developed and rigorously evaluated using comprehensive performance metrics to predict BPD occurrence and severity. SHAP (SHapley Additive exPlanations) analysis was employed to interpret the best-performing model.
Results:
Key risk factors for BPD development included maternal hypertension (OR 2.31, 95% CI 1.56 to 3.42), initial oxygen requirement >30% (OR 3.15, 95% CI 2.13 to 4.65) and lack of exclusive breast milk feeding (OR 1.89, 95% CI 1.28 to 2.79). Severe BPD was independently associated with prolonged invasive ventilation (>7 days) (OR 4.12, 95% CI 2.78 to 6.11), elevated C reactive protein (>10 mg/L) (OR 2.87, 95% CI 1.93 to 4.26) and patent ductus arteriosus (OR 2.53, 95% CI 1.71 to 3.74). Machine learning models demonstrated strong predictive performance; the optimal XGBoost model achieved an area under the curve of 0.89 (95% CI 0.85 to 0.93), an F1 score of 0.82, a Matthews Correlation Coefficient of 0.73 and a balanced accuracy of 0.85. SHAP analysis identified initial FiO2 >30%, mechanical ventilation and maternal hypertension as the top three most influential predictors for the XGBoost model.
Conclusions:
This study provides the first comprehensive analysis of BPD risk factors at a specific high altitude and validates effective, interpretable machine learning models for its prediction. These findings highlight the critical importance of altitude-specific adjustments in risk assessment and emphasise the potential for model-guided early interventions to improve outcomes for this vulnerable population.
