ArticleBMC pediatrics2025
Development and validation of machine learning models for predicting bronchopulmonary dysplasia risk in preterm neonates based on antenatal determinants: a retrospective cohort study.
Article in BMC pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Risk prediction of PLA2R-Ab-negative membranous nephropathy: an interpretable multicenter machine learning model.Frontiers in immunology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundBronchopulmonary dysplasia (BPD) remains one of the most common and serious complications in preterm infants. This study aimed to develop and validate machine learning (ML) algorithms to predict the risk of BPD in preterm neonates based on potential antenatal risk factors.
methodsThis retrospective study included 644 preterm infants with gestational age (GA) < 32 weeks who were hospitalized between January 2019 and December 2022. Of these, 450 and 194 infants were assigned to the training and internal validation cohorts, respectively. An independent external validation external validation cohort comprising 141 infants born between January 2023 and December 2023 was also included. Clinical data, including maternal prenatal history and early neonatal parameters, were obtained from the electronic medical records system. Least absolute shrinkage and selection operator was used for feature selection. Ten ML models were developed using the selected variables. Model performance was assessed based on discrimination (AUROC), calibration, and discrimination were evaluated in both internal and external validation cohorts.
resultsThe overall incidence of BPD among preterm infants with GA < 32 weeks was 35.09%. Univariate logistic analysis identified 13 statistically significant predictors of BPD (p < 0.05). Multivariable logistic regression analysis confirmed GA, gravidity, and prenatal immunosuppressants use as independent risk factors. Among the ten ML models evaluated, LR algorithm demonstrated the best overall performance, with the highest F1 score and consistent results across both internal and external validation cohorts.
conclusionsGA, gravidity, and prenatal immunosuppressants use are independent predictors of BPD in preterm infants. The LR model showed superior predictive performance and robustness across datasets, suggesting its potential utility in clinical decision-making for early BPD risk stratification.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.