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Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model.

So Jin Yoon1, Donghyun Kim2,3, Sook Hyun Park1

  • 1Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.

Diagnostics (Basel, Switzerland)
|December 22, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a machine learning model to predict postnatal growth failure in very low birth weight infants. The model achieved good early detection accuracy, aiding in timely interventions and improved infant health outcomes.

Keywords:
machine learningperformancepostnatal growth failureprediction

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Area of Science:

  • Neonatal Medicine
  • Machine Learning in Healthcare
  • Pediatric Growth Research

Background:

  • Postnatal growth failure (PGF) impacts very low birth weight (VLBW) infants, necessitating early detection for intervention.
  • Accurate prediction models are crucial for proactive management and improved VLBW infant outcomes.

Purpose of the Study:

  • To develop and evaluate a machine learning model for predicting PGF in VLBW infants at discharge.
  • To identify key predictive features for PGF using extreme gradient boosting.

Main Methods:

  • Utilized extreme gradient boosting on data from 729 VLBW infants across four hospitals (2013-2017).
  • Defined PGF as a z-score decrease >1.28 between birth and discharge.
  • Performed feature selection and addition at multiple time points (0, 7, 14, 28 days) to optimize prediction accuracy.

Main Results:

  • An initial model with 12 features achieved an AUROC of 0.78 at 7 days.
  • Adding weight change improved the AUROC to 0.84 at 7 days.
  • Identified key predictors including sex, gestational age, birth weight, SGA, maternal hypertension, RDS, ventilation duration, PDA, sepsis, PN, and FEN.

Conclusions:

  • The developed machine learning model demonstrates strong early detection capabilities for PGF in VLBW infants.
  • This predictive tool holds potential as a supplemental clinical aid to reduce PGF and enhance infant health.
  • Early identification facilitates timely interventions, potentially mitigating long-term growth complications.