Predicting risk for bronchopulmonary dysplasia: selection criteria for clinical trials

R A Sinkin1, C Cox, D L Phelps

  • 1Department of Pediatrics (Neonatology), University of Rochester Medical Center, New York 14642.

Pediatrics
|November 1, 1990
PubMed

Insights

Early identification of infants at risk for bronchopulmonary dysplasia is crucial for clinical trial enrollment. Predictive models using birth weight and respiratory support accurately identify neonates needing oxygen at 28 days.

Area of Science:

  • Neonatology
  • Pediatric Pulmonology
  • Clinical Trial Design

Background:

  • Bronchopulmonary dysplasia (BPD) is a significant complication in premature infants.
  • Early identification of neonates at high risk for BPD is essential for targeted interventions and clinical trial enrollment.

Purpose of the Study:

  • To develop and validate predictive models for identifying neonates likely to require supplemental oxygen at 28 days.
  • To stratify neonates into risk groups (low, moderate, high) for BPD development.

Main Methods:

  • Logistic regression models were developed using data from 160 neonatal intensive care unit survivors.
  • Predictors included birth weight, gestational age, Apgar score, and respiratory support parameters (peak inspiratory pressure, mean airway pressure).
  • Models were prospectively validated on three independent datasets totaling 238 neonates.

Main Results:

  • A 12-hour model predicted oxygen requirement using birth weight, gestational age, 5-minute Apgar score, and peak inspiratory pressure.
  • A 10-day model used birth weight, gestational age, peak inspiratory pressure, and mean airway pressure.
  • Both models accurately classified neonates into risk categories, with low-risk groups showing <10% incidence of 28-day oxygen requirement.

Conclusions:

  • Logistic regression models effectively predict the need for supplemental oxygen at 28 days in neonates.
  • These models facilitate early risk stratification for bronchopulmonary dysplasia, aiding clinical trial recruitment.
  • Validated predictive tools can optimize resource allocation and therapeutic strategies in neonatal care.

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