Statistical analysis
According to their distribution, continuous variables were reported as
the means or medians using interquartile ranges (IQRs) or standard
deviations. Categorical variables are reported as percentages. First,
the birth weight percentile was calculated using
IG-21st software, and the calculation coefficients
derived from the WHO study were used to calculate the WHO birth weight
percentile. Then, for each growth chart (IG-21st and
WHO standards), we calculated the proportion of live births with a birth
weight below the <10th percentile (SGA) and
<3rd percentile (FGR). To evaluate the
relative validity of each reference growth chart, neonatal outcomes
(i.e., low Apgar rate, ponderal, and cephalization indexes) between the
”non-overlapping” populations were determined and compared with neonates
at or above the 10th percentile using the chi-squared
test. Finally, relative risk (RR) was calculated as the ratio of the
incidence of adverse perinatal outcomes among SGA and FGR neonates.
To account for a country-specific effect, we further evaluated the
association of SGA by different standards with the adverse outcome using
multilevel regression analyses, where the subjects were at the lower
level and countries at the upper level. The relationships between
patient-level and country-level variables and the adverse perinatal
outcomes were examined with multilevel linear regression using the R
‘lm4’ package. Fixed effects were estimated for maternal education and
nulliparity. The multilevel analysis was implemented in a stepwise
manner. First, an unconditional means model was used to determine the
attributable variance explained by the multilevel design. Second, using
a backward elimination approach, all selected variables for inclusion
were added to the unconditional means model as fixed effects, and
nonsignificant variables were removed sequentially until only
significant (i.e., p<0.05) variables remained. Finally,
diagnostic performance (sensitivity; specificity; positive and negative
likelihood ratio; and the diagnostic odds ratio) was estimated and used
to compare the accuracy of the two fetal growth standards to identify
neonates at risk of adverse perinatal outcomes. We compared the
likelihood and diagnostic odds ratios by bootstrapping 2000 replicates
with replacement. The receiver-operating characteristics (ROC) curve
analysis determined the performance for predicting a low APGAR score and
ponderal index by each fetal growth standard was determined by the
receiver–operating characteristics (ROC) curve analysis. The resulting
areas under the ROC curves (AUCs) were compared using the DeLong method,
and a p-value <0.05 was considered statistically significant.
Data processing was performed using R software. A value of p
<0.05 was considered statistically significant.