Early prediction of bronchopulmonary dysplasia: comparison of modelling methods, development and validation studies.

Journal: Pediatric research
Published Date: Jun 12, 2025

Abstract

BACKGROUND: Machine-learning methods are gaining in popularity to predict medical events but their added value to other methods is still to be determined. We compared performances of clinical prediction models for bronchopulmonary dysplasia (BPD) or death in very preterm infants using logistic regression and random forests methods.

Authors

  • Heloise Torchin
    Université Paris Cité, Epidemiology and Statistics Research Center/CRESS, INSERM, INRAE, Paris, France. heloise.torchin@inserm.fr.
  • Paula Dhiman
    Center for Statistics in Medicine, University of Oxford, Oxford, UK.
  • Pierre-Yves Ancel
    Université Paris Cité, Epidemiology and Statistics Research Center/CRESS, INSERM, INRAE, Paris, France.
  • Xavier Durrmeyer
    Université Paris Cité, Epidemiology and Statistics Research Center/CRESS, INSERM, INRAE, Paris, France.
  • Pierre-Henri Jarreau
    Université Paris Cité, Epidemiology and Statistics Research Center/CRESS, INSERM, INRAE, Paris, France.
  • Alexandra Nuytten
    Department of Neonatology, Saint Vincent de Paul Hospital, Lille, France.
  • Patrick Truffert
    Department of Neonatology, Jeanne de Flandres Hospital, Lille, France.
  • Jennifer Zeitlin
    Université Paris Cité, Epidemiology and Statistics Research Center/CRESS, INSERM, INRAE, Paris, France.
  • Gary S Collins
    Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford, OX3 7LD UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

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