Competing‐risks model for prediction of small‐for‐gestational‐age neonate from biophysical and biochemical markers at 11–13 weeks' gestation. (9th December 2020)

• Record Type: Journal Article
• Authors: Papastefanou, I.; Wright, D.; Syngelaki, A.; Souretis, K.; Chrysanthopoulou, E.; Nicolaides, K. H.
• Is Part Of: Ultrasound in obstetrics & gynecology. Volume 57:Number 1(2021)
• Page Start: 52
• Page End: 61
• Publication Date: 2020-12-09
• Subjects: Bayes' theorem -- fetal growth restriction -- first‐trimester screening -- likelihood -- mean arterial pressure -- placental growth factor -- pregnancy‐associated plasma protein‐A -- pyramid of prenatal care -- SGA -- survival model -- uterine artery Doppler; Ultrasonics in obstetrics -- Periodicals; Generative organs, Female -- Diseases -- Diagnosis -- Periodicals; Diagnosis, Ultrasonic -- Periodicals; Genital Diseases, Female -- ultrasonography -- Periodicals; Ultrasonography, Prenatal -- Periodicals; 618.047543
• DOI: 10.1002/uog.23523
• Languages: English
• ISSNs: 0960-7692

Abstract

ABSTRACT: Objective: To develop a new competing‐risks model for the prediction of a small‐for‐gestational‐age (SGA) neonate, based on maternal factors and biophysical and biochemical markers at 11–13 weeks' gestation. Methods: This was a prospective observational study in 60 875 women with singleton pregnancy undergoing routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation. All pregnancies had pregnancy‐associated plasma protein‐A and placental growth factor (PlGF) measurements, 59 001 had uterine artery pulsatility index (UtA‐PI) measurements and 58 479 had mean arterial pressure measurements; 57 131 cases had complete data for all biomarkers. We used a previously developed competing‐risks model for the joint distribution of gestational age (GA) at delivery and birth‐weight Z ‐score, according to maternal demographic characteristics and medical history. The likelihoods of the biophysical markers were developed by fitting folded‐plane regression models, a technique that has already been used in previous studies for the likelihoods of biochemical markers. The next step was to modify the prior distribution by the likelihood, according to Bayes' theorem, to obtain individualized distributions for GA at delivery and birth‐weight Z ‐score. We used the 57 131 cases with complete data to assess the discrimination and calibration of the model for predicting SGA with, without or independently of pre‐eclampsia, by different combinations of maternal factors and biomarkers. Results: The distribution of biomarkers, conditional to both GA at delivery and birth‐weight Z ‐score, was best described by folded‐plane regression models. These continuous two‐dimensional likelihoods update the joint distribution of birth‐weight Z ‐score and GA at delivery that has resulted from a competing‐risks approach; this method allows application of user‐defined cut‐offs. The best biophysical predictor of preterm SGA was UtA‐PI and the best biochemical marker was PlGF. The prediction of SGA was consistently better for increasing degree of prematurity, greater severity of smallness, coexistence of PE and increasing number of biomarkers. The combination of maternal factors with all biomarkers predicted 34.3%, 48.6% and 59.1% of all cases of a SGA neonate with birth weight < 10 th percentile delivered at ≥ 37, < 37 and < 32 weeks' gestation, at a 10% false‐positive rate. The respective values for birth weight < 3 rd percentile were 39.9%, 53.2% and 64.4%, and for birth weight < 3 rd percentile with pre‐eclampsia they were 46.3%, 66.8% and 80.4%. The new model was well calibrated. Conclusions: This study has presented a single continuous two‐dimensional model for prediction of SGA for any desired cut‐offs of smallness and GA at delivery, laying the ground for a personalized antenatal plan for predicting and managing SGA, in the milieu of a new inverted pyramid of prenatal care. © 2020 International Society of Ultrasound in Obstetrics and Gynecology