Online clinical tool to estimate risk of bronchopulmonary dysplasia in extremely preterm infants.

Journal Article (Journal Article)

OBJECTIVE: Develop an online estimator that accurately predicts bronchopulmonary dysplasia (BPD) severity or death using readily-available demographic and clinical data. DESIGN: Retrospective analysis of data entered into a prospective registry. SETTING: Infants 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. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Separate 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. RESULTS: Among 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. CONCLUSION: The 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.

Full Text

Duke Authors

Cited Authors

  • Greenberg, RG; McDonald, SA; Laughon, MM; Tanaka, D; Jensen, E; Van Meurs, K; Eichenwald, E; Brumbaugh, JE; Duncan, A; Walsh, M; Das, A; Cotten, CM; Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network,

Published Date

  • June 21, 2022

Published In

PubMed ID

  • 35728925

Electronic International Standard Serial Number (EISSN)

  • 1468-2052

Digital Object Identifier (DOI)

  • 10.1136/archdischild-2021-323573

Language

  • eng

Conference Location

  • England