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Data Validation

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Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
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External Validation of a Model for Predicting Outcomes in Preterm Newborns.

Laura Routier1,2, Sarah Touati1,3, Ghida Ghostine-Ramadan1,3

  • 1INSERM UMR 1105, Research Group on Multimodal Analysis of Brain Function, University of Picardie Jules Verne, Amiens, France.

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|July 31, 2025
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Summary

This study validated the PRETERM-POM model for predicting neurodevelopmental impairment in extremely preterm newborns. The model demonstrated high accuracy, confirming its clinical utility for guiding management and rehabilitation.

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Area of Science:

  • Neonatal Medicine
  • Developmental Pediatrics
  • Medical Informatics

Background:

  • Predicting outcomes in preterm newborns is essential for tailored therapeutic strategies and rehabilitation.
  • Prognostic models offer a promising avenue, but require robust validation for reliability and generalizability.

Purpose of the Study:

  • To externally validate the PRETERM-POM, a multimodal prognostic model designed to predict outcomes in extremely preterm infants at two years of age.

Main Methods:

  • A temporal validation group of preterm infants (23-28 weeks gestational age) born between 2018-2021 was used.
  • The PRETERM-POM model's parameters were applied to predict neurodevelopmental outcomes (favorable vs. adverse) assessed via the Denver Developmental Screening Test-II.
  • Statistical analyses included AUC, DeLong test, calibration-in-the-large, calibration curves, Hosmer-Lemeshow test, and Brier score.

Main Results:

  • The validation group included 104 infants (median gestational age 26.3 weeks).
  • The PRETERM-POM model achieved an AUC of 85.9% for outcome prediction, comparable to the development cohort.
  • The model demonstrated good fit but tended to underestimate the risk of adverse outcomes.

Conclusions:

  • External validation confirmed the PRETERM-POM model's high performance in predicting neurodevelopmental impairment in preterm infants.
  • This multimodal approach, with transparent risk factor contributions, supports its clinical application for timely management decisions.