Competing‐risks model for prediction of small‐for‐gestational‐age neonate from estimated fetal weight at 19–24 weeks' gestation. (5th May 2021)

• Record Type: Journal Article
• Authors: Papastefanou, I.; Nowacka, U.; Syngelaki, A.; Dragoi, V.; Karamanis, G.; Wright, D.; Nicolaides, K. H.
• Is Part Of: Ultrasound in obstetrics & gynecology. Volume 57:Number 6(2021)
• Page Start: 917
• Page End: 924
• Publication Date: 2021-05-05
• Subjects: Bayes' theorem -- estimated fetal weight -- fetal growth restriction -- likelihood -- pyramid of prenatal care -- second‐trimester screening -- small‐for‐gestational age -- survival model; 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.23593
• Languages: English
• ISSNs: 0960-7692

Abstract

ABSTRACT: Objective: To develop further a new competing‐risks model for the prediction of a small‐for‐gestational‐age (SGA) neonate, by including second‐trimester ultrasonographic estimated fetal weight (EFW). Methods: This was a prospective observational study in 96 678 women with singleton pregnancy undergoing routine ultrasound examination at 19–24 weeks' gestation. All pregnancies had ultrasound biometry assessment, and EFW was calculated according to the Hadlock formula. We refitted in this large dataset a previously described 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, to obtain the prior distribution. The continuous likelihood of the EFW was fitted conditionally to GA at delivery and birth‐weight Z ‐score and modified the prior distribution, according to Bayes' theorem, to obtain individualized distributions for GA at delivery and birth‐weight Z ‐score and therefore patient‐specific risks for any cut‐offs for GA at delivery and birth‐weight Z ‐score. We assessed the discriminative ability of the model for predicting SGA with, without or independently of pre‐eclampsia occurrence. A calibration study was carried out. Performance of screening was evaluated for SGA defined according to the Fetal Medicine Foundation birth‐weight charts. Results: The distribution of EFW, conditional to both GA at delivery and birth‐weight Z ‐score, was best described by a regression model. For earlier gestations, the association between EFW and birth weight was steeper. The prediction of SGA by maternal factors and EFW improved for increasing degree of prematurity and greater severity of smallness but not for coexistence of pre‐eclampsia. Screening by maternal factors predicted 31%, 34% and 39% of SGA neonates with birth weight < 10 th percentile delivered at ≥ 37, < 37 and < 30 weeks' gestation, respectively, at a 10% false‐positive rate, and, after addition of EFW, these rates increased to 38%, 43% and 59%, respectively; the respective rates for birth weight < 3 rd percentile were 43%, 50% and 64%. The addition of EFW improved the calibration of the model. Conclusion: In the competing‐risks model for prediction of SGA, the performance of screening by maternal characteristics and medical history is improved by the addition of second‐trimester EFW. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.