Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics and serum pregnancy-associated plasma protein-A at 11-13 weeks' gestation.
Adult
Bayes Theorem
Birth Weight
Female
Gestational Age
Humans
Infant, Newborn
Infant, Small for Gestational Age
Likelihood Functions
Logistic Models
Maternal Serum Screening Tests
/ statistics & numerical data
Predictive Value of Tests
Pregnancy
Pregnancy Trimester, First
/ blood
Pregnancy-Associated Plasma Protein-A
/ analysis
Prospective Studies
Risk Assessment
/ methods
Ultrasonography, Prenatal
/ methods
Bayes' theorem
PAPP-A
SGA
fetal growth restriction
first-trimester screening
survival model
Journal
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
ISSN: 1469-0705
Titre abrégé: Ultrasound Obstet Gynecol
Pays: England
ID NLM: 9108340
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
23
06
2020
accepted:
24
07
2020
pubmed:
10
8
2020
medline:
15
12
2021
entrez:
10
8
2020
Statut:
ppublish
Résumé
To develop a continuous likelihood model for pregnancy-associated plasma protein-A (PAPP-A), in the context of a new competing-risks model for prediction of a small-for-gestational-age (SGA) neonate, and to compare the predictive performance of the new model for SGA to that of previous 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. The dataset was divided randomly into a training dataset and a test dataset. The training dataset was used for PAPP-A likelihood model development. We used Bayes' theorem to combine the previously developed prior model for the joint Gaussian distribution of gestational age (GA) at delivery and birth-weight Z-score with the PAPP-A likelihood to obtain a posterior distribution. This patient-specific posterior joint Gaussian distribution of GA at delivery and birth-weight Z-score allows risk calculation for SGA defined in terms of different birth-weight percentiles and GA. The new model was validated internally in the test dataset and we compared its predictive performance to that of the risk-scoring system of the UK National Institute for Health and Care Excellence (NICE) and that of logistic regression models for different SGA definitions. PAPP-A has a continuous association with both birth-weight Z-score and GA at delivery according to a folded-plane regression. The new model, with the addition of PAPP-A, was equal or superior to several logistic regression models. The new model performed well in terms of risk calibration and consistency across different GAs and birth-weight percentiles. In the test dataset, at a false-positive rate of about 30% using the criteria defined by NICE, the new model predicted 62.7%, 66.5%, 68.1% and 75.3% of cases of a SGA neonate with birth weight < 10 Using Bayes' theorem to combine PAPP-A measurement data with maternal characteristics improves the prediction of SGA and performs better than logistic regression or NICE guidelines, in the context of a new competing-risks model for the joint distribution of birth-weight Z-score and GA at delivery. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.
Substances chimiques
Pregnancy-Associated Plasma Protein-A
EC 3.4.24.-
PAPPA protein, human
EC 3.4.24.79
Types de publication
Journal Article
Observational Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
541-548Informations de copyright
© 2020 International Society of Ultrasound in Obstetrics and Gynecology.
Références
Pilliod RA, Cheng YW, Snowden JM, Doss AE, Caughey AB. The risk of intrauterine fetal death in the small-for-gestational-age fetus. Am J Obstet Gynecol 2012; 207: 318-416.
Trudell AS, Cahill AG, Tuuli MG, Macones GA, Odibo AO. Risk of stillbirth after 37 weeks in pregnancies complicated by small-for-gestational-age fetuses. Am J Obstet Gynecol 2013; 208: 376.e1-7.
Bukowski R, Burgett AD, Gei A, Saade GR, Hankins GD. Impairment of fetal growth potential and neonatal encephalopathy. Am J Obstet Gynecol 2003; 188: 1011-1015.
McIntyre S, Blair E, Badawi N, Keogh J, Nelson KB. Antecedents of cerebral palsy and perinatal death in term and late preterm singletons. Obstet Gynecol 2013; 122: 869-877.
Barker DJ. Adult consequences of fetal growth restriction. Clin Obstet Gynecol 2006; 49: 270-283.
McCowan LM, Figueras F, Anderson NH. Evidence-based national guidelines for the management of suspected fetal growth restriction: comparison, consensus, and controversy. Am J Obstet Gynecol 2018; 218: S855-S868.
Ciobanu A, Rouvali, A, Syngelaki A, Akolekar R, Nicolaides KH. Prediction of small for gestational age neonates: Screening by maternal factors, fetal biometry and biomarkers at 35-37 weeks' gestation. Am J Obstet Gynecol 2019; 220: 486.e1-11.
Royal College of Obstetricians and Gynaecologists. The Investigation and Management of the Small-for-Gestational-Age Fetus. Green-top guideline No. 31. RCOG, 2014.
Poon LC, Karagiannis G, Staboulidou I, Shafiei A, Nicolaides KH. Reference range of birth weight with gestation and first-trimester prediction of small-for-gestation neonates. Prenat Diagn 2011; 31: 58-65.
Karagiannis G, Akolekar R, Sarquis R, Wright D, Nicolaides KH. Prediction of small-for-gestation neonates from biophysical and biochemical markers at 11-13 weeks. Fetal Diagn Ther 2011; 29: 148-154.
Papastefanou I, Souka AP, Pilalis A, Eleftheriades M, Michalitsi V, Kassanos D. First trimester prediction of small and large for gestation neonates by an integrated model incorporating ultrasound parameters, biochemical indices and maternal characteristics. Acta Obstet Gynecol Scand 2012; 91: 104-111.
Crovetto F, Triunfo S, Crispi F, Rodriguez-Sureda V, Dominguez C, Figueras F, Gratacos E. Differential performance of first-trimester screening in predicting small-for-gestational-age neonate or fetal growth restriction. Ultrasound Obstet Gynecol 2017; 49: 349-356.
Wright D, Syngelaki A, Akolekar R, Poon LC, Nicolaides KH. Competing risks model in screening for preeclampsia by maternal characteristics and medical history. Am J Obstet Gynecol 2015; 213: 62.e1-10.
O'Gorman N, Wright D, Syngelaki A, Akolekar R, Wright A, Poon LC, Nicolaides KH. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11-13 weeks' gestation. Am J Obstet Gynecol 2016; 214: 103.e1-12.
Wright D, Tan MY, O'Gorman N, Poon LC, Syngelaki A, Wright A, Nicolaides KH. Predictive performance of the competing risk model in screening for preeclampsia. Am J Obstet Gynecol 2019; 220: 199.e1-13.
Tan MY, Syngelaki A, Poon LC, Rolnik DL, O'Gorman N, Delgado JL, Akolekar R, Konstantinidou L, Tsavdaridou M, Galeva S, Ajdacka U, Molina FS, Persico N, Jani JC, Plasencia W, Greco E, Papaioannou G, Wright A, Wright D, Nicolaides KH. Screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks' gestation. Ultrasound Obstet Gynecol 2018; 52: 186-195.
Papastefanou I, Wright D, Nicolaides KH. Competing-risks model for prediction of small-for-gestational-age neonate from maternal characteristics and medical history. Ultrasound Obstet Gynecol 2020; 56: 196-205.
Nicolaides KH. Screening for fetal aneuploidies at 11 to 13 weeks. Prenat Diagn 2011; 31: 7-15.
Robinson HP, Fleming JE. A critical evaluation of sonar crown rump length measurements. Br J Obstet Gynaecol 1975; 82: 702-710.
Nicolaides KH, Wright D, Syngelaki A, Wright A, Akolekar R. Fetal Medicine Foundation fetal and neonatal population weight charts. Ultrasound Obstet Gynecol 2018; 52: 44-51.
Gilks WR, Thomas A, Spiegelhalter DJ. A language and program for complex Bayesian modelling. The Statistician 1994; 43: 169-177.
R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/.