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Updated: Jan 18, 2026

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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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Do Birthweight-For-Gestational Age Centiles Predict Serious Neonatal Morbidity and Neonatal Mortality?
Sid John1,2, K S Joseph1,2,3, John Fahey4
1Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, British Columbia, Canada.
Paediatric and Perinatal Epidemiology
|September 9, 2025
Summary
Birthweight centiles improve predictions of serious neonatal morbidity and mortality (SNMM) in multivariable models for specific high-risk groups. Further evaluation is needed to determine clinical significance.
Area of Science:
- Neonatal Health
- Predictive Modeling
- Perinatal Epidemiology
Background:
- Foetal and birthweight-for-gestational age centiles are insufficient predictors of serious neonatal morbidity and mortality (SNMM) in univariable analyses.
- Existing models often fail to accurately identify neonates at high risk.
Purpose of the Study:
- To evaluate the predictive performance of multivariable SNMM models.
- To assess the impact of including birthweight centiles in these models.
Main Methods:
- Utilized US live birth data (2019-2021) from the National Center for Health Statistics.
- Defined SNMM by Apgar score, seizures, assisted ventilation, or neonatal death.
- Developed log-linear regression models using maternal/pregnancy characteristics, with and without birthweight centiles, for various gestational ages and maternal conditions.
- Assessed model performance using the area under the curve (AUC).
Main Results:
- Models for all live births (24-42 weeks) showed AUCs of 0.83, with minimal improvement from birthweight centiles.
- For 39-week gestation births, AUCs were 0.66 (without centiles) and 0.69 (with centiles).
- For births to mothers with pre-existing diabetes at 39 weeks, AUCs increased from 0.69 to 0.77 with birthweight centiles.
Conclusions:
- Birthweight centiles enhance the predictive accuracy of multivariable SNMM models in specific subpopulations, such as those with pre-existing diabetes.
- Clinical utility depends on evaluating decision thresholds to ascertain the importance of these predictive improvements.
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