ArticleArchives of disease in childhood. Fetal and neonatal edition2022
Online clinical tool to estimate risk of bronchopulmonary dysplasia in extremely preterm infants.
Article in Archives of disease in childhood. Fetal and neonatal edition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00063063 (Generic Database), which is not on this map. Cited by 48 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Generic Database: A Survey of Morbidity and Mortality in Very Low Birth Weight Infants
Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.
- Early postnatal corticosteroids in preventing mortality and bronchopulmonary dysplasia: the pivotal importance of selecting an appropriate treated population.European journal of pediatrics · 2024Pooled it
- Severity of Bronchopulmonary Dysplasia in Infants Born Extremely Preterm and Randomized to Early Human Milk Fortification with a Donor Milk-Derived Fortifier for 2 Weeks.The Journal of pediatrics · 2025Trial
- Trial
- Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia.American journal of physiology. Lung cellular and molecular physiology · 2023Trial
- Neurally adjusted ventilatory assist versus conventional mechanical ventilation for congenital diaphragmatic hernia in neonates.The Cochrane database of systematic reviews · 2026Article
- Validation of a web-based clinical tool for predicting the risk of bronchopulmonary dysplasia in extremely preterm infants: a retrospective cohort study.BMJ paediatrics open · 2026Article
- "Right baby, right time: a multi-NICU quality improvement initiative using the 2022 NICHD BPD risk estimator to guide timely steroid therapy".Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Risk-Guided Personalized Care to Prevent Bronchopulmonary Dysplasia: A Real-World Implementation Study.Journal of personalized medicine · 2026Article
- Time-Series Machine Learning for Prediction of Bronchopulmonary Dysplasia.The Journal of pediatrics · 2026Article
- Observational
- Development and validation of the Prognostic Respiratory Intensity Scoring Metric (PRISM).BMC pediatrics · 2026Article
- Five-Minute Apgar Scores and Its Prognostic Value for Mortality and Severe Morbidity in Very Preterm Infants: A Multinational Cohort Study.BJOG : an international journal of obstetrics and gynaecology · 2026Observational
- Predicting pulmonary hypertension in infants with bronchopulmonary dysplasia.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- BPD prediction model for very preterm infants integrating chest X-ray scores within 7 days: a retrospective case-control study.BMJ open · 2026Article
- Article
- A Respiratory Intervention Intensity Index for Supplemental Oxygen Use at Postnatal Day 28 in Preterm Infants Using BWQS Regression: Incremental Stratification Beyond Gestational Age in a Single-Center Retrospective Cohort Study.Journal of multidisciplinary healthcare · 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
- Time to Full Enteral Feeds and Late-Onset Sepsis in Extremely Preterm Infants.JAMA network open · 2025Article
- Adding Early Postnatal Parameters of Ventilation to Prognostic Models for Pulmonary Outcome in Very Preterm Infants.Pediatric pulmonology · 2025Article
- Defining endotypes of bronchopulmonary dysplasia in preterm infants to improve precision-based therapies.JCI insight · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 8 institutions in 2 countries.
Funding
Abstract
objectiveDevelop an online estimator that accurately predicts bronchopulmonary dysplasia (BPD) severity or death using readily-available demographic and clinical data.
designRetrospective analysis of data entered into a prospective registry.
settingInfants cared for at centres of the United States Neonatal Research Network between 2011 and 2017. PATIENTS: Infants 501-1250 g birth weight and 23 0/7-28 6/7 weeks' gestation.
interventionsNone.
main outcome measuresSeparate multinomial regression models for postnatal days 1, 3, 7, 14 and 28 were developed to estimate the individual probabilities of death or BPD severity (no BPD, grade 1 BPD, grade 2 BPD, grade 3 BPD) defined according to the mode of respiratory support administered at 36 weeks' postmenstrual age.
resultsAmong 9181 included infants, birth weight was most predictive of death or BPD severity on postnatal day 1, while mode of respiratory support was the most predictive factor on days 3, 7, 14 and 28. The predictive accuracy of the models increased at each time period from postnatal day 1 (C-statistic: 0.674) to postnatal day 28 (C-statistic 0.741). We used these results to develop a web-based model that provides predicted estimates for BPD by postnatal day.
conclusionThe probability of BPD or death in extremely preterm infants can be estimated with reasonable accuracy using a limited amount of readily available clinical information. This tool may aid clinical prognostication, future research, and center-specific quality improvement surrounding BPD prevention. TRIAL REGISTRATION NUMBER: NCT00063063.
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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.