Prenatal prediction of very late onset small-for-gestational age newborns in low-risk pregnancies

Abstract

Prompt identification and correct management of late onset small-for-gestational age newborns can reduce perinatal morbidity and mortality. Given the limitations of current monitoring methods, additional strategies are needed. Besides ultrasound to monitor fetal growth, third trimester Doppler and serum measurement of angiogenic biomarkers, such as soluble fms-like tyrosine kinase-1 and placental growth factor, have been proposed as promising predictors of late onset small-for-gestational age fetuses. Objective To find a multivariate model for predicting small-for-gestational age newborns at 36 weeks’ gestation by using clinical, biochemical and ultrasound measurements. Materials and Methods We evaluated 564 low-risk pregnant women and recorded maternal age, maternal body mass index, maternal mean blood pressure, soluble fms-like tyrosine kinase-1 (multiples of the median), placental growth factor (multiples of the median), soluble fms-like tyrosine kinase-1/placental growth factor ratio, estimated fetal weight centile and mean uterine artery pulsatility index at 36 weeks. Binary logistic regression was used. Statistical significance was set at 95% level (p < 0.05). Results We found three multivariate models showing relatively small differences in predictive capability. Model 1 only included estimated fetal weight centiles (area under the curve [AUC] 0.86; R2 = 0.42; p < 0.0001), Model 2 estimated fetal weight centiles and placental growth factor (multiples of the median) (AUC 0.87; R2 = 0.44; p < 0.0001) and Model 3 estimated fetal weight centiles, placental growth factor (multiples of the median) and mean uterine artery pulsatility index (AUC 0.88; R2 = 0.45; p < 0.0001). Conclusion Small-for-gestational age at delivery may be predicted by using a multivariate formula. The inclusion of parameters other than estimated fetal weight centile at 36 weeks’ gestation modestly improves the predictive capability of the model. Clinical decisions should consider whether or not these slight differences deserve a change in current strategies.