Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics, serum pregnancy-associated plasma protein-A and placental growth factor at 11–13 weeks' gestation

Abstract

Objectives: To expand a new competing-risks model for prediction of a small-for-gestational-age (SGA) neonate, by the addition of pregnancy-associated plasma protein-A (PAPP-A) and placental growth factor (PlGF), and to evaluate and compare PAPP-A and PlGF in predicting SGA. Methods: This was a prospective observational study of 60 875 women with singleton pregnancy undergoing routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation. We fitted a folded-plane regression model for the PAPP-A and PlGF likelihoods. A previously developed maternal history model and the likelihood models were combined, according to Bayes' theorem, to obtain individualized distributions for gestational age (GA) at delivery and birth-weight Z-score. We assessed the discrimination and calibration of the model. McNemar's test was used to compare the detection rates for SGA with, without or independently of pre-eclampsia (PE) occurrence, of different combinations of maternal history, PAPP-A and PlGF, for a fixed false-positive rate. Results: The distributions of PAPP-A and PlGF depend on both GA at delivery and birth-weight Z-score, in the same continuous likelihood, according to a folded-plane regression model. The new approach offers the capability for risk computation for any desired birth-weight Z-score and GA at delivery cut-off. PlGF was consistently and significantly better than PAPP-A in predicting SGA delivered before 37 weeks, especially in cases with co-existence of PE. PAPP-A had similar performance to PlGF for the prediction of SGA without PE. At a fixed false-positive rate of 10%, the combination of maternal history, PlGF and PAPP-A predicted 33.8%, 43.8% and 48.4% of all cases of a SGA neonate with birth weight < 10th percentile delivered at ≥ 37, < 37 and < 32 weeks' gestation, respectively. The respective values for birth weight < 3rd percentile were 38.6%, 48.7% and 51.0%. The new model performed well in terms of risk calibration. Conclusions: The combination of PAPP-A and PlGF values with maternal characteristics, according to Bayes' theorem, improves prediction of SGA. PlGF is a better predictor of SGA than PAPP-A, especially when PE is present. The new competing-risks model for SGA can be tailored to each pregnancy and to the relevant clinical requirements. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.