Abstract
AbstractBackgroundDespite the recent creation of several birth weight-for-gestational age references and standards, none has proven superior. We identified birth weight-for-gestational age cut-offs, and corresponding United States population-based, Intergrowth 21st and World Health Organization centiles associated with higher risks of adverse neonatal outcomes, and evaluated their ability to predict serious neonatal morbidity and neonatal mortality (SNMM).Methods and findingsThe study population comprised singleton live births at 37-41 weeks’ gestation in the United States, 2003-2017. Birth weight-specific SNMM, which included 5-minute Apgar score<4, neonatal seizures, assisted ventilation and neonatal death, was modeled by gestational week using penalized B-splines. We estimated the birth weights at which SNMM odds was minimized (and higher by 10%, 50% and 100%), and identified the corresponding population, Intergrowth 21st and World Health Organization (WHO) centiles. We then evaluated the individual- and population-level performance of these cut-offs for predicting SNMM. The study included 40,179,663 live births at 37-41 weeks’ gestation and 991,486 SNMM cases. Among female singletons at 39 weeks’ gestation, SNMM odds was lowest at 3,203 g birth weight (population, Intergrowth and WHO centiles 40, 52 and 46, respectively), and 10% higher at 2,835 g and 3,685 g (population centiles 11th and 82nd, Intergrowth centiles 17th and 88th and WHO centiles 15th and 85th). SNMM odds were 50% higher at 2,495 g and 4,224 g and 100% higher at 2,268 g and 4,593 g. Birth weight cut-offs were poor predictors of SNMM. For example, the birth weight cut-off associated with 10% higher odds of SNMM among female singletons at 39 weeks’ gestation resulted in a sensitivity of 12.5%, specificity of 89.4% and population attributable fraction of 2.1%, while the cut-off associated with 50% higher odds resulted in a sensitivity of 2.9%, specificity of 98.4% and population attributable fraction of 1.3%.ConclusionsBirth weight-for-gestational age cut-offs and centiles perform poorly when used to predict adverse neonatal outcomes in individual infants, and the population impact associated with these cut-offs is also small.FundingCanadian Institutes of Health Research (MOP-67125 and PJT153439).Author summaryWhy was this study doneDespite the recent creation of several birth weight-for-gestational age references and standards, no method has proved superior for identifying small-for-gestational age (SGA), appropriate-for-gestational age (AGA) and large-for-gestational age (LGA) infants.For instance, infants classified as AGA by the Intergrowth Project 21st standard and SGA by national references have a higher risk of perinatal death compared with infants deemed AGA by both.What did the researchers do and find?Our study identified the birth weights at each gestational week at which the risk of serious neonatal morbidity and neonatal mortality (SNMM) was lowest and elevated to varying degrees, and showed that the corresponding Intergrowth and WHO centiles were right-shifted compared with population centiles.Outcome-based birth weight and centile cutoffs performed poorly for predicting serious neonatal morbidity and neonatal mortality (SNMM) at the individual level.The population attributable fractions associated with these Outcome-based birth weight and centile cutoffs cut-offs were also small.The birth weight distributions of live births and SNMM cases (at each gestational week) overlapped substantially, showing that birth weight-for-gestational age in isolation cannot serve as an accurate predictor of adverse neonatal outcomes, irrespective of the cut-off used to identify SGA and LGA infants.What do these findings mean?Using birth weight-for-gestational age cutoffs to identify SGA, AGA and LGA infants does not add significantly to individual- or population-level prediction of adverse neonatal outcomes.Birth weight-for-gestational age centiles are best suited for use in multivariable prognostic functions, in conjunction with other prognostic indicators of adverse perinatal outcomes.
Publisher
Cold Spring Harbor Laboratory