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Updated: Jan 8, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
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Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

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Prediction of Language Development in Neonates Born at Less than 32 Weeks of Gestation

Nadine Pointner1, Daniel Savran2, Johannes Mader3

  • 1Division of Neonatology, Intensive Care Medicine and Neuropediatrics, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria.

The Journal of Pediatrics
|December 19, 2025
PubMed

Insights

Multilingualism and lower maternal education are key predictors of language development issues in preterm infants. Early identification of these risk factors can guide tailored support for children born before 32 weeks gestation.

Area of Science:

  • Neonatal care and developmental pediatrics.
  • Computational linguistics and machine learning in healthcare.

Background:

  • Preterm birth (< 32 weeks gestation) poses risks for neurodevelopmental outcomes, including language.
  • Early identification of risk factors is crucial for timely intervention in preterm infants.

Purpose of the Study:

  • To identify neonatal risk factors for language development at age 3 in very preterm infants.
  • To assess the predictive power of these factors using advanced analytical methods.

Main Methods:

  • Retrospective analysis of hospital and assessment data from infants born < 32 weeks gestation (2012-2020).
  • Utilized Gradient Boost Decision Trees and SHapely Additive exPlanations (SHAP) values to analyze predictors.
  • Included children with follow-ups, German-speaking parents, and normal hearing.

Main Results:

  • Multilingualism (mean|SHAP|=3.93), lower maternal education (mean|SHAP|=2.51), and reduced birth weight (mean|SHAP|=1.92) were significant predictors.
  • Shorter gestational age, intraventricular hemorrhage, necrotizing enterocolitis, and retinopathy of prematurity also predicted language outcomes.
  • SHAP values quantified the additive impact of these diverse risk factors.

Conclusions:

  • SHAP-based analysis effectively identified neonatal and sociodemographic predictors of language impairment in preterm children.
  • Findings support tailored monitoring and early language support strategies for this vulnerable population.
  • Further validation in diverse cohorts is recommended.
Abstract

Keywords:
Bayley-IIImultilingualismneonatologyneurodevelopmentoutcome researchprematurityrisk factors

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