Literature DB >> 22471679

Predicting large fetuses at birth: do multiple ultrasound examinations and longitudinal statistical modelling improve prediction?

Jun Zhang1, Sungduk Kim, Jagteshwar Grewal, Paul S Albert.   

Abstract

Predicting large fetuses at birth has long been a challenge in obstetric practice. We examined whether ultrasound examinations at multiple times during pregnancy improve the accuracy of prediction using repeated, longitudinal statistical modelling, and whether adding maternal characteristics improves the accuracy of prediction. We used data from a previous study conducted in Norway and Sweden from 1986 to 1989 in which each pregnant woman had four ultrasound examinations at around 17, 25, 33 and 37 weeks of gestation. At birth, infant size was classified as large-for-gestational age (LGA, >90th centile) and macrosomia (>4000 g) or not. We used a longitudinal random effects model with quadratic fixed and random effects to predict term LGA and macrosomia at birth. Receiver-operator curves and mean-squared error were used to measure accuracy of the prediction. Ultrasound examination around 37 weeks had the best accuracy in predicting LGA and macrosomia at birth. Adding multiple ultrasound examinations at earlier gestations did not improve the accuracy. Adjusting for maternal characteristics had limited impact on the accuracy of prediction. Thus, a single ultrasound examination at late gestation close to birth is the simplest method currently available to predict LGA and macrosomia.
© 2012 Blackwell Publishing Ltd.

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Year:  2012        PMID: 22471679      PMCID: PMC3324111          DOI: 10.1111/j.1365-3016.2012.01261.x

Source DB:  PubMed          Journal:  Paediatr Perinat Epidemiol        ISSN: 0269-5022            Impact factor:   3.980


  19 in total

1.  Accurate prediction of term birth weight from prospectively measurable maternal characteristics.

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2.  In utero analysis of fetal growth: a sonographic weight standard.

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3.  Preliminary results from the Collaborative Alabama and Scandinavian Study of Successive Small-for-Gestational Age Births.

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4.  A comparison of single versus multiple growth ultrasonographic examinations in predicting birth weight.

Authors:  H L Hedriana; T R Moore
Journal:  Am J Obstet Gynecol       Date:  1994-06       Impact factor: 8.661

5.  Estimation of fetal weight with the use of head, body, and femur measurements--a prospective study.

Authors:  F P Hadlock; R B Harrist; R S Sharman; R L Deter; S K Park
Journal:  Am J Obstet Gynecol       Date:  1985-02-01       Impact factor: 8.661

6.  Random-effects models for longitudinal data.

Authors:  N M Laird; J H Ware
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7.  Limited clinical utility of midtrimester fetal morphometric percentile rankings in screening for birth weight abnormalities.

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8.  A linear mixed model for predicting a binary event from longitudinal data under random effects misspecification.

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9.  Prediction of birth weight by ultrasound in the third trimester.

Authors:  E K Pressman; J L Bienstock; K J Blakemore; S A Martin; N A Callan
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10.  Prediction of birth weight by ultrasound-estimated fetal weight: a comparison between single and repeated estimates.

Authors:  T Larsen; G Greisen; S Petersen
Journal:  Eur J Obstet Gynecol Reprod Biol       Date:  1995-05       Impact factor: 2.435

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  9 in total

1.  A class of joint models for multivariate longitudinal measurements and a binary event.

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2.  Trajectories of maternal gestational weight gain and child cognition assessed at 5 years of age in a prospective cohort study.

Authors:  Stefanie N Hinkle; Paul S Albert; Lindsey A Sjaarda; Jagteshwar Grewal; Katherine L Grantz
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3.  Identifying subgroups of enhanced predictive accuracy from longitudinal biomarker data using tree-based approaches: applications to fetal growth.

Authors:  Jared C Foster; Danping Liu; Paul S Albert; Aiyi Liu
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4.  Discriminatory capacity of prenatal ultrasound measures for large-for-gestational-age birth: A Bayesian approach to ROC analysis using placement values.

Authors:  Soutik Ghosal; Zhen Chen
Journal:  Stat Biosci       Date:  2021-06-05

5.  Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study.

Authors:  Stefan Kuhle; Bryan Maguire; Hongqun Zhang; David Hamilton; Alexander C Allen; K S Joseph; Victoria M Allen
Journal:  BMC Pregnancy Childbirth       Date:  2018-08-15       Impact factor: 3.007

6.  Screening circulating proteins to identify biomarkers of fetal macrosomia.

Authors:  Tess Cruickshank; Tu'uhevaha J Kaitu'u-Lino; Ping Cannon; Alesia Harper; Tuong-Vi Nguyen; Kirsten M Dane; Anna L Middleton; Valerie P Kyritsis; Roxanne Hastie; Stephen Tong; Susan P Walker; Teresa M MacDonald
Journal:  BMC Res Notes       Date:  2019-09-18

7.  Ensemble Learning to Improve the Prediction of Fetal Macrosomia and Large-for-Gestational Age.

Authors:  Shangyuan Ye; Hui Zhang; Fuyan Shi; Jing Guo; Suzhen Wang; Bo Zhang
Journal:  J Clin Med       Date:  2020-01-31       Impact factor: 4.241

8.  Gestational exposure to endocrine disrupting chemicals in relation to infant birth weight: a Bayesian analysis of the HOME Study.

Authors:  Meghan M Woods; Bruce P Lanphear; Joseph M Braun; Lawrence C McCandless
Journal:  Environ Health       Date:  2017-10-27       Impact factor: 5.984

Review 9.  Fetal Growth Acceleration-Current Approach to the Big Baby Issue.

Authors:  Jan Modzelewski; Anna Kajdy; Katarzyna Muzyka-Placzyńska; Dorota Sys; Michał Rabijewski
Journal:  Medicina (Kaunas)       Date:  2021-03-02       Impact factor: 2.430

  9 in total

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