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Risk Prediction for Stillbirth and Neonatal Mortality in Low-resource Settings.

Vivek V Shukla1, Waldemar A Carlo1

  • 1Division of Neonatology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.

Newborn (Clarksville, Md.)
|December 21, 2022
PubMed
Summary
This summary is machine-generated.

Predicting stillbirth and neonatal death in low-resource settings is challenging. Machine learning models show promise using delivery and post-delivery data, with birth weight being a key factor.

Keywords:
Low- and middle-income countriesMortality fetalMortality neonatalNeonatalNewborn infantPerinatal mortalityPreterm infantsResuscitationStillbirth

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Area of Science:

  • Public Health
  • Perinatal Epidemiology
  • Machine Learning in Healthcare

Background:

  • High rates of stillbirth and neonatal mortality persist globally, especially in low- and middle-income countries (LMICs).
  • Despite decades of effort, quality intrapartum and neonatal care remains inaccessible in many low-resource settings.
  • Existing research on risk factors for adverse perinatal outcomes in LMICs is limited.

Purpose of the Study:

  • To review the evidence on risk prediction models for stillbirth and neonatal death in LMICs.
  • To evaluate the predictive accuracy of machine learning models using various data points.
  • To identify key predictors of neonatal mortality in resource-limited environments.

Main Methods:

  • Systematic review of studies utilizing advanced machine-learning statistical models.
  • Analysis of models developed using data from low-resource settings in LMICs.
  • Assessment of predictive accuracy based on prenatal, pre-delivery, delivery, and post-delivery data.

Main Results:

  • Machine learning models using prenatal and pre-delivery data demonstrate low predictive accuracy for intrapartum stillbirth and neonatal mortality.
  • Models incorporating delivery and post-delivery data achieve good predictive accuracy for neonatal mortality risk.
  • Birth weight emerges as the most significant predictor of neonatal mortality.

Conclusions:

  • Risk prediction for stillbirth and neonatal death in LMICs requires further development and validation.
  • Machine learning models show potential, particularly when utilizing data collected during and after delivery.
  • Future research should focus on validating these models in diverse settings and developing targeted interventions.