Prediction of small for gestational age neonates: screening by maternal factors, fetal biometry, and biomarkers at 35-37 weeks' gestation.

BACKGROUND: Small for gestational age (SGA) neonates are at increased risk for perinatal mortality and morbidity; however, the risks can be substantially reduced if the condition is identified prenatally, because in such cases close monitoring and appropriate timing of delivery and prompt neonatal care can be undertaken. The traditional approach of identifying pregnancies with SGA fetuses is maternal abdominal palpation and serial measurements of symphysial-fundal height, but the detection rate of this approach is less than 30%. A higher performance of screening for SGA is achieved by sonographic fetal biometry during the third trimester; screening at 30-34 weeks' gestation identifies about 80% of SGA neonates delivering preterm but only 50% of those delivering at term, at a screen-positive rate of 10%. There is some evidence that routine ultrasound examination at 36 weeks' gestation is more effective than that at 32 weeks in predicting birth of SGA neonates.

OBJECTIVE: To investigate the potential value of maternal characteristics and medical history, sonographically estimated fetal weight (EFW) and biomarkers of impaired placentation at 35+0 - 36+6 weeks' gestation in the prediction of delivery of SGA neonates.

MATERIALS AND METHODS: A dataset of 19,209 singleton pregnancies undergoing screening at 35+0 -36+6 weeks' gestation was divided into a training set and a validation set. The training dataset was used to develop models from multivariable logistic regression analysis to determine whether the addition of uterine artery pulsatility index (UtA-PI), umbilical artery PI (UA-PI), fetal middle cerebral artery PI (MCA-PI), maternal serum placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFLT) would improve the performance of maternal factors and EFW in the prediction of delivery of SGA neonates. The models were then tested in the validation dataset to assess performance of screening.

RESULTS: First, in the training dataset, in the SGA group, compared to those with birthweight in ≥10th percentile, the median multiple of the median (MoM) values of PlGF and MCA-PI were reduced, whereas UtA-PI, UA-PI, and sFLT were increased. Second, multivariable regression analysis demonstrated that in the prediction of SGA in <10th percentile there were significant contributions from maternal factors, EFW Z-score, UtA-PI MoM, MCA-PI MoM, and PlGF MoM. Third, in the validation dataset, prediction of 90% of SGA neonates delivering within 2 weeks of assessment was achieved by a screen-positive rate of 67% (95% confidence interval [CI], 64-70%) in screening by maternal factors, 23% (95% CI, 20-26%) by maternal factors, and EFW and 21% (95% CI, 19-24%) by the addition of biomarkers. Fourth, prediction of 90% of SGA neonates delivering at any stage after assessment was achieved by a screen-positive rate of 66% (95% CI, 65-67%) in screening by maternal factors, 32% (95% CI, 31-33%) by maternal factors and EFW and 30% (95% CI, 29-31%) by the addition of biomarkers.

CONCLUSION: The addition of biomarkers of impaired placentation only marginally improves the predictive performance for delivery of SGA neonates achieved by maternal factors and fetal biometry at 35+0 -36+6 weeks' gestation.