Discussion
LGA
REGRESSION, PERINATAL MORBIDITY, SHOULDER DYSTOCIA
We performed an in-depth examination of the statistical assumptions of the Gardosi customization method [The quantile regression model seems to be a natural choice for modeling quantiles when standard assumptions of normal distribution models are suspect. Quantile regression was used to create the WHO fetal growth charts and also for a customized fetal growth reference in an African-American population [Our investigation into the statistical assumptions of customization methods of proportional standard deviation across birthweight values is novel. In addition, the effect of the covariates on fetal growth across gestation had also been assumed to be fixed, but we found the effect of pre-pregnancy weight on EFW was both nonconstant and non-linear, and in the heteroscedastic model, maternal pre-pregnancy weight significantly affected the variance. Maternal height and parity were also associated with increased EFW starting at the beginning of the third trimester, with little influence in the first and second trimesters. Some of the other customization variables showed some non-constant influence on the distribution of EFW, although these findings were not statistically significant which could have been due to limited power. Also, the quadratic or cubic terms for maternal height and weight were not statistically significant in either the Gardosi or heteroscedastic model, indicating that a linear term may be sufficient. Removal of maternal weight from the customization model has previously been found to identify a greater proportion of LGA neonates with deliveries complicated by shoulder dystocia, NICU admission and neonatal respiratory problems that were not identified by a population based definition of LGA, although that analysis used the outcome of birthweight [Paternal factors have not traditionally been included in customization charts. We found that increasing paternal height and weight had a positive, independent influence on fetal growth, although maternal height and weight had a stronger effect. These findings are similar to findings from the Generation R cohort of EFW in the Netherlands [While the six customization characteristics (gestational age, maternal pre-pregnancy weight, height, race, parity, infant sex) are known to influence fetal growth, it is unclear whether the changes in fetal growth in relation to these characteristics are a normal physiologic adaptation or associated with increased risk for perinatal morbidity and mortality. Since shorter and lighter women would be expected to have smaller neonates than taller and heavier women, taking maternal height and weight into account should help identify fetuses that are more likely to be constitutionally small or overgrown instead of being erroneously labeled as not aligned with their growth potential [Our study only found incremental improvements in detection of rates of neonatal morbidity and mortality at term with SGA and LGA defined by all three customization models compared to population based birthweight reference, with no difference in predictive ability (i.e., similar c-statistics across the models) which may have been due to smaller numbers of adverse outcomes in a healthier population initially recruited for the primary study goals to create a fetal growth standard [The concept of considering maternal and fetal characteristics is appealing as a personalized medicine approach, although there is controversy on whether customization for maternal and fetal factors improves clinically useful detection of SGA and LGA [