ArticleBMC pediatrics2024
Development of a simplified model and nomogram for the prediction of pulmonary hemorrhage in respiratory distress syndrome in extremely preterm infants.
Article in BMC pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Does "POWS" pack a powerful enough punch to predict pulmonary hemorrhage in preterm infants?Pediatric research · 2026Article
- Research progress on biomarkers associated with neonatal respiratory distress syndrome.Frontiers in pediatrics · 2026Review
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Interpretable machine learning model for predicting low birth weight in singleton pregnancies: a retrospective cohort study.BMC pregnancy and childbirth · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
backgroundPulmonary hemorrhage (PH) in respiratory distress syndrome (RDS) in extremely preterm infants exhibits a high mortality rate and poor long-term outcomes. The aim of the present study was to develop a machine learning (ML) predictive model for RDS with PH in extremely preterm infants.
methodsWe performed a retrospective analysis of extremely preterm infants with RDS at the Children's Hospital of Soochow University between January 2015 and January 2021. We applied three ML algorithms-logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost)-to evaluate the performance of each model using the area under the curve (AUC), and developed a predictive model based on the optimal model. We calculated SHapley Additive exPlanations (SHAP) values to determine variables importance and show visualization results, and constructed a nomogram for individualized prediction.
resultsA total of 309 patients with RDS were enrolled, including 48 (15.5%) with PH. A total of 29 variables were collected, including demographic and clinical characteristics, laboratory data, and image classification. According to the AUC values, the RF model performed best (AUC = 0.868). Based on the SHAP values, the top five important variables in the RF model were gestational age, PaO
conclusionsOur study showed that the RF model could be used to predict the risk of PH in RDS in extremely preterm infants. The nomogram provides clinicians with an effective tool for early warning and timely management.
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