Paediatric and perinatal epidemiology · 2025
Do Birthweight-For-Gestational Age Centiles Predict Serious Neonatal Morbidity and Neonatal Mortality?
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John Sid, Joseph K S, Fahey John, Liu Shiliang, Lisonkova Sarka, Kramer Michael S
Аннотация
BACKGROUND: Studies show that foetal and birthweight-for-gestational age centiles are poor predictors of serious neonatal morbidity and neonatal mortality (SNMM) in univariable models.
OBJECTIVE: We assessed the predictive performance of multivariable SNMM models based on maternal/pregnancy characteristics, with and without birthweight centiles.
METHODS: The study was based on all live births in the United States, 2019-2021, with data obtained from the period live birth-infant death files of the National Center for Health Statistics. SNMM was defined as any one or more of the following: 5-minute Apgar score < 4, seizures, assisted ventilation for> 30 or neonatal death. SNMM was modelled by log-linear regression on maternal/pregnancy characteristics as predictors, with and without birthweight centiles. Models were developed for live births at 24-42 weeks' and 39 weeks' gestation to all women and those with hypertensive disorders or pre-existing diabetes. Model performance was assessed using area under the curve (AUC).
RESULTS: The study population included 10,487,243 live births and 221,728 SNMM cases (2.1 per 100 live births). The models with all live births at 24-42 weeks' gestation had AUCs of 0.83 (95% confidence interval [CI] 0.82, 0.83) based on maternal/pregnancy characteristics and 0.83 (95% CI 0.83, 0.84) based on maternal/pregnancy characteristics and birthweight centiles. However, AUCs of models based on all live births at 39 weeks' gestation were 0.66 (95% CI 0.64, 0.68) with maternal/pregnancy characteristics and 0.69 (95% CI 0.68, 0.71) with maternal/pregnancy characteristics and birthweight centiles. AUCs of the models with live births at 39 weeks' gestation to women with pre-existing diabetes were 0.69 (95% CI 0.66, 0.72) based on maternal/pregnancy characteristics, and 0.77 (95% CI 0.74, 0.79) with the addition of birthweight centiles.
CONCLUSIONS: Birthweight centiles improve multivariable SNMM predictive performance in specific subpopulations, although evaluation of decision thresholds is required to determine the clinical importance of improvement in predictive ability.
OBJECTIVE: We assessed the predictive performance of multivariable SNMM models based on maternal/pregnancy characteristics, with and without birthweight centiles.
METHODS: The study was based on all live births in the United States, 2019-2021, with data obtained from the period live birth-infant death files of the National Center for Health Statistics. SNMM was defined as any one or more of the following: 5-minute Apgar score < 4, seizures, assisted ventilation for> 30 or neonatal death. SNMM was modelled by log-linear regression on maternal/pregnancy characteristics as predictors, with and without birthweight centiles. Models were developed for live births at 24-42 weeks' and 39 weeks' gestation to all women and those with hypertensive disorders or pre-existing diabetes. Model performance was assessed using area under the curve (AUC).
RESULTS: The study population included 10,487,243 live births and 221,728 SNMM cases (2.1 per 100 live births). The models with all live births at 24-42 weeks' gestation had AUCs of 0.83 (95% confidence interval [CI] 0.82, 0.83) based on maternal/pregnancy characteristics and 0.83 (95% CI 0.83, 0.84) based on maternal/pregnancy characteristics and birthweight centiles. However, AUCs of models based on all live births at 39 weeks' gestation were 0.66 (95% CI 0.64, 0.68) with maternal/pregnancy characteristics and 0.69 (95% CI 0.68, 0.71) with maternal/pregnancy characteristics and birthweight centiles. AUCs of the models with live births at 39 weeks' gestation to women with pre-existing diabetes were 0.69 (95% CI 0.66, 0.72) based on maternal/pregnancy characteristics, and 0.77 (95% CI 0.74, 0.79) with the addition of birthweight centiles.
CONCLUSIONS: Birthweight centiles improve multivariable SNMM predictive performance in specific subpopulations, although evaluation of decision thresholds is required to determine the clinical importance of improvement in predictive ability.