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Developing and validating an explainable digital mortality prediction tool for extremely preterm infants.

T'ng Chang Kwok1,2, Chao Chen3, Jayaprakash Veeravalli3

  • 1Centre for Perinatal Research, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, United Kingdom.

PLOS Digital Health
|December 10, 2025
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Summary
This summary is machine-generated.

A new online tool predicts mortality risk for extremely preterm infants, aiding perinatal decision-making. This explainable model uses electronic health records and shows superior performance to existing methods.

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Area of Science:

  • Neonatal Medicine
  • Medical Informatics
  • Biostatistics

Background:

  • Perinatal management of extremely preterm infants presents significant decision-making challenges.
  • Mortality prediction tools are essential for supporting clinical decisions in neonatal care.
  • Existing prediction models may not fully meet the needs for accuracy and utility in this high-risk population.

Purpose of the Study:

  • To develop and internally validate an explainable online tool for predicting mortality in extremely preterm infants before neonatal discharge.
  • To compare the performance of various machine learning approaches for mortality prediction in this cohort.
  • To assess the tool's calibration, discrimination, and clinical utility compared to previously published models.

Main Methods:

  • Utilized population-based electronic patient record data from 25,902 infants born between 23+0-27+6 weeks' gestation across 185 English and Welsh neonatal units (2010-2020).
  • Developed and internally validated a mortality prediction tool using stepwise backward logistic regression, selected from nine machine learning algorithms.
  • Externally validated the tool's performance in a multinational cohort of preterm infants.

Main Results:

  • The developed tool demonstrated good discrimination (AUC 0.746) and calibration, with superior net benefit across probability thresholds (10%-70%).
  • The tool outperformed previously published models in calibration and utility.
  • Acceptable performance was confirmed in the external validation cohort, indicating generalizability.

Conclusions:

  • An explainable, online mortality prediction tool for extremely preterm infants has been developed and validated.
  • This tool shows promising utility for supporting high-risk perinatal decision-making.
  • Further evaluation is recommended before widespread clinical adoption.