Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Postnatal Risk Factors

Li Ding1, Huawei Wang1, Haifeng Geng1

  • 1Department of Neonatology, Children's Hospital of Soochow University, Suzhou, China.

Insights

This study identified key postnatal risk factors for bronchopulmonary dysplasia (BPD) in preterm infants. A predictive model combining biomarkers like sB7-H3 and IL-18 with clinical factors offers high accuracy for early BPD detection.

Area of Science:

  • Neonatology
  • Pediatric Pulmonology
  • Critical Care Medicine

Background:

  • Bronchopulmonary dysplasia (BPD) is a significant complication in preterm infants.
  • Identifying reliable predictors of BPD is crucial for timely intervention.

Purpose of the Study:

  • To identify postnatal risk factors for BPD in preterm infants (gestational age ≤32 weeks).
  • To develop and evaluate an early predictive model for BPD occurrence.

Main Methods:

  • Prospective longitudinal study of 72 preterm infants (30 with BPD, 42 controls).
  • Collected perinatal data, neonatal critical illness score (NCIS), serum soluble B7-H3 (sB7-H3), and interleukin-18 (IL-18) levels.
  • Utilized multiple logistic regression and ROC curve analysis to establish and assess a predictive model.

Main Results:

  • Postnatal risk factors identified include electrolyte disturbances, hemodynamically significant patent ductus arteriosus (hs-PDA), and delayed enteral feeding.
  • Elevated serum sB7-H3, IL-18, and NCIS were observed in the BPD group.
  • A predictive model combining sB7-H3 (day 7), IL-18 (day 14), NCIS, and clinical factors demonstrated high predictive accuracy (AUC 0.960).

Conclusions:

  • BPD development is multifactorial, influenced by several postnatal clinical and biological factors.
  • The combined predictive model offers a promising tool for early identification of infants at high risk for BPD.

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