ArticleWorld journal of pediatrics : WJP2023
Risk factors and machine learning prediction models for bronchopulmonary dysplasia severity in the Chinese population.
Article in World journal of pediatrics : WJP, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review.Journal of medical Internet research · 2025Pooled it
- [A real-world study on the association between treatment of patent ductus arteriosus in extremely preterm infants and the risk of bronchopulmonary dysplasia].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Article
- Development and internal validation of a clinical nomogram for predicting bronchopulmonary dysplasia in preterm infants.Experimental and therapeutic medicine · 2026Article
- Clinical characteristics and respiratory management of bronchopulmonary dysplasia in preterm infants: a retrospective case-control study.Frontiers in pediatrics · 2026Article
- A nomogram for predicting the risk of bronchopulmonary dysplasia in preterm infants: a prospective multicenter study.Frontiers in pediatrics · 2026Article
- Development and validation of machine learning models for predicting bronchopulmonary dysplasia risk in preterm neonates based on antenatal determinants: a retrospective cohort study.BMC pediatrics · 2025Article
- Comparison of nSOFA and RSS for predicting bronchopulmonary dysplasia in preterm infants: a retrospective cohort study.BMJ paediatrics open · 2025Article
- Antenatal Corticosteroids and Bronchopulmonary Dysplasia in Very Preterm Infants.JAMA network open · 2025Article
- Prenatal particulate matter exposure and risk of bronchopulmonary dysplasia in very preterm infants: a retrospective cohort study.BMJ open · 2025Observational
- Advancements in biomarkers and machine learning for predicting of bronchopulmonary dysplasia and neonatal respiratory distress syndrome in preterm infants.Frontiers in pediatrics · 2025Review
- Machine learning predictive model for aspiration risk in early enteral nutrition patients with severe acute pancreatitis.Heliyon · 2024Article
- Association between early metabolic acidosis and bronchopulmonary dysplasia/death in preterm infants born at less than 28 weeks' gestation: an observational cohort study.BMC pediatrics · 2024Observational
- [Risk factors for bronchopulmonary dysplasia in twin preterm infants: a multicenter study].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2024Article
- Nutritional support during the first week for infants with bronchopulmonary dysplasia and respiratory distress: a multicenter cohort study in China.BMC pediatrics · 2024Article
- Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.Journal of perinatology : official journal of the California Perinatal Association · 2024Review
- Two-stage learning-based prediction of bronchopulmonary dysplasia in very low birth weight infants: a nationwide cohort study.Frontiers in pediatrics · 2023Article
- Machine learning predictive models for grading bronchopulmonary dysplasia: umbilical cord blood IL-6 as a biomarker.Frontiers in pediatrics · 2023Article
- Neonatal sequential organ failure assessment score within 72 h after delivery reliably predicts bronchopulmonary dysplasia in very preterm infants.Frontiers in pediatrics · 2023Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundBronchopulmonary dysplasia (BPD) is a common chronic lung disease in extremely preterm neonates. The outcome and clinical burden vary dramatically according to severity. Although some prediction tools for BPD exist, they seldom pay attention to disease severity and are based on populations in developed countries. This study aimed to develop machine learning prediction models for BPD severity based on selected clinical factors in a Chinese population.
methodsIn this retrospective, single-center study, we included patients with a gestational age < 32 weeks who were diagnosed with BPD in our neonatal intensive care unit from 2016 to 2020. We collected their clinical information during the maternal, birth and early postnatal periods. Risk factors were selected through univariable and ordinal logistic regression analyses. Prediction models based on logistic regression (LR), gradient boosting decision tree, XGBoost (XGB) and random forest (RF) models were implemented and assessed by the area under the receiver operating characteristic curve (AUC).
resultsWe ultimately included 471 patients (279 mild, 147 moderate, and 45 severe cases). On ordinal logistic regression, gestational diabetes mellitus, initial fraction of inspiration O
conclusionsWe found risk factors for BPD severity in our population and developed machine learning models based on them. The models have good performance and can be used to aid in predicting BPD severity in the Chinese population.
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