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Exploring clinical, echocardiographic and molecular biomarkers to predict bronchopulmonary dysplasia

Maria Alvarez-Fuente1, Laura Moreno2, Paloma Lopez-Ortego3

  • 1Pediatric Cardiology Department, Ramón y Cajal University Hospital, Madrid, Spain.

Plos One
|March 7, 2019
PubMed

Insights

Predicting moderate or severe bronchopulmonary dysplasia (BPD) in extremely preterm infants is crucial. A model using mechanical ventilation and echocardiographic signs of pulmonary hypertension (PH) is practical for identifying BPD risk.

Area of Science:

  • Neonatology
  • Pediatric Pulmonology
  • Biomarker Research

Background:

  • Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease in childhood, linked to prematurity.
  • Moderate to severe BPD is associated with worse outcomes and pulmonary hypertension (PH).
  • Early prediction of BPD is vital for implementing preventive strategies.

Purpose of the Study:

  • To explore the predictive ability of clinical, echocardiographic, and molecular variables for moderate or severe BPD in extremely preterm infants.
  • To compare the efficacy of different predictive models for BPD development.

Main Methods:

  • Prospective longitudinal study of preterm newborns (gestational age <28 weeks, weight ≤ 1250 grams).
  • Weekly recording of clinical and echocardiographic variables.
  • Analysis of molecular biomarkers (e.g., IL-6, endothelin-1) from blood and tracheal aspirates up to 36 weeks postmenstrual age.

Main Results:

  • 35 out of 50 infants (74.5%) developed BPD (15 moderate, 6 severe).
  • A two-variable model (mechanical ventilation and echocardiographic signs of PH) showed predictive value.
  • A model including mechanical ventilation, echocardiographic signs of PH, and endothelin-1 (ET-1) was also evaluated.

Conclusions:

  • Clinical and echocardiographic variables effectively predict BPD risk.
  • A two-variable model is practical for clinical and research use in BPD prediction.
  • Further research should investigate the role of endothelin-1 (ET-1) in BPD prediction.
Abstract

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