“Screening for small-for-gestational age neonates at early third trimester in a high-risk population for preeclampsia”

Background Strategies to improve prenatal detection of small-for-gestational age (SGA) neonates are necessary because its association with poorer perinatal outcome. This study evaluated, in pregnancies with first trimester high risk of early preeclampsia, the performance of a third trimester screening for SGA combining biophysical and biochemical markers. Methods This is a prospective longitudinal study on 378 singleton pregnancies identified at high risk of early preeclampsia according to a first trimester multiparametric algorithm with the cutoff corresponding to 15% false positive rate. This cohort included 50 cases that delivered SGA neonates with birthweight < 10th centile (13.2%) and 328 cases with normal birthweight (86.8%). At 27–30 weeks’ gestation, maternal weight, blood pressure, estimated fetal weight, mean uterine artery pulsatility index and maternal biochemical markers (placental growth factor and soluble FMS-Like Tyrosine Kinase-1) were assessed. Different predictive models were created to evaluate their performance to predict SGA neonates. Results For a 15% FPR, a model that combines maternal characteristics, estimated fetal weight, mean uterine artery pulsatility index and placental growth factor achieved a detection rate (DR) of 56% with a negative predictive value of 92.2%. The area under receiver operating characteristic curve (AUC) was 0.79 (95% confidence interval (CI), 0.72–0.86). The DR of a model including maternal characteristics, estimated fetal weight and mean uterine artery pulsatility index was 54% (AUC, 0.77 (95% CI, 0.70–0.84)). The DR of a model that includes maternal characteristics and placental growth factor achieved a similar performance (DR 56%, AUC 0.75, 95% CI (0.67–0.83)). Conclusions The performance of screening for SGA neonates at early third trimester combining biophysical and biochemical markers in a high-risk population is poor. However, a high negative predictive value could help in reducing maternal anxiety, avoid iatrogenic interventions and propose a specific plan for higher risk patients.