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Online clinical tool to estimate risk of bronchopulmonary dysplasia in extremely preterm infants
Rachel G Greenberg1, Scott A McDonald2, Matthew M Laughon3
1Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA rachel.greenberg@duke.edu.
Insights
An online estimator predicts bronchopulmonary dysplasia (BPD) or death in preterm infants using clinical data. This tool aids prognostication and BPD prevention efforts.
Area of Science:
- Neonatology
- Pediatric Pulmonology
- Clinical Informatics
Background:
- Bronchopulmonary dysplasia (BPD) is a significant complication in extremely preterm infants.
- Accurate prediction of BPD severity and mortality is crucial for clinical management and research.
- Existing predictive models may not fully utilize readily available clinical data.
Purpose of the Study:
- To develop and validate an online estimator for predicting BPD severity or death in preterm infants.
- To identify key demographic and clinical factors influencing BPD risk.
- To create a tool for improved clinical prognostication and quality improvement initiatives.
Main Methods:
- Retrospective analysis of a prospective registry of infants (501-1250g birth weight, 23-28 weeks' gestation) from the US Neonatal Research Network (2011-2017).
- Development of multinomial regression models to estimate probabilities of death or BPD (grades 1-3) at specific postnatal days (1, 3, 7, 14, 28).
- Definition of BPD based on respiratory support at 36 weeks' postmenstrual age.
Main Results:
- Birth weight was the primary predictor of BPD/death on postnatal day 1.
- Mode of respiratory support became the most predictive factor from postnatal day 3 to 28.
- Predictive accuracy increased over time, with C-statistics ranging from 0.674 on day 1 to 0.741 on day 28.
- A web-based model was developed using these findings.
Conclusions:
- Readily available clinical data can accurately estimate the probability of BPD or death in extremely preterm infants.
- The developed online tool can assist in clinical prognostication and quality improvement for BPD prevention.
- This predictive model offers a valuable resource for clinicians and researchers in managing BPD.
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.
