ArticlePloS one2016
Single and Serial Fetal Biometry to Detect Preterm and Term Small- and Large-for-Gestational-Age Neonates: A Longitudinal Cohort Study.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Amniotic fluid cell-free transcriptome: a glimpse into fetal development and placental cellular dynamics during normal pregnancy.BMC medical genomics · 2020Trial
- The plasma metabolome of women in early pregnancy differs from that of non-pregnant women.PloS one · 2019Trial
- Machine learning to improve the prediction of Large for Gestational Age (LGA) neonates: a cohort study.BMC pregnancy and childbirth · 2026Article
- Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2023Review
- Fetal weight projection model to define growth velocity and validation against pregnancy outcome in a cohort of serially scanned pregnancies.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2022Article
- ULTRASOUND DIAGNOSIS OF MACROSOMIA AMONG WOMEN WITH GESTATIONAL DIABETES - REVIEW OF THE LITERATURE.Acta clinica Croatica · 2022Review
- Reduced fetal growth velocity precedes antepartum fetal death.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2021Article
- Fetal Growth Acceleration-Current Approach to the Big Baby Issue.Medicina (Kaunas, Lithuania) · 2021Review
- Article
- Prediction of small-for-gestational age by fetal growth rate according to gestational age.PloS one · 2019Article
- Fetal Growth Restriction Prediction: How to Move beyond.TheScientificWorldJournal · 2019Review
- Metabolomics for predicting fetal growth restriction: protocol for a systematic review and meta-analysis.BMJ open · 2018Article
- Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study.BMC pregnancy and childbirth · 2018Article
- Selective versus universal third trimester ultrasound: Time for a rethink? An audit of current practices at a metropolitan Sydney hospital.Australasian journal of ultrasound in medicine · 2018Article
- A new customized fetal growth standard for African American women: the PRB/NICHD Detroit study.American journal of obstetrics and gynecology · 2018Article
- Individualized growth assessment: conceptual framework and practical implementation for the evaluation of fetal growth and neonatal growth outcome.American journal of obstetrics and gynecology · 2018Review
- Article
- Fetal ultrasound parameters: Reference values for a local perspective.The Indian journal of radiology & imagingArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectivesTo assess the value of single and serial fetal biometry for the prediction of small- (SGA) and large-for-gestational-age (LGA) neonates delivered preterm or at term.
methodsA cohort study of 3,971 women with singleton pregnancies was conducted from the first trimester until delivery with 3,440 pregnancies (17,334 scans) meeting the following inclusion criteria: 1) delivery of a live neonate after 33 gestational weeks and 2) two or more ultrasound examinations with fetal biometry parameters obtained at ≤36 weeks. Primary outcomes were SGA (<5th centile) and LGA (>95th centile) at birth based on INTERGROWTH-21st gender-specific standards. Fetus-specific estimated fetal weight (EFW) trajectories were calculated by linear mixed-effects models using data up to a fixed gestational age (GA) cutoff (28, 32, or 36 weeks) for fetuses having two or more measurements before the GA cutoff and not already delivered. A screen test positive for single biometry was based on Z-scores of EFW at the last scan before each GA cut-off so that the false positive rate (FPR) was 10%. Similarly, a screen test positive for the longitudinal analysis was based on the projected (extrapolated) EFW at 40 weeks from all available measurements before each cutoff for each fetus.
resultsFetal abdominal and head circumference measurements, as well as birth weights in the Detroit population, matched well to the INTERGROWTH-21st standards, yet this was not the case for biparietal diameter (BPD) and femur length (FL) (up to 9% and 10% discrepancy for mean and confidence intervals, respectively), mainly due to differences in the measurement technique. Single biometry based on EFW at the last scan at ≤32 weeks (GA IQR: 27.4-30.9 weeks) had a sensitivity of 50% and 53% (FPR = 10%) to detect preterm and term SGA and LGA neonates, respectively (AUC of 82% both). For the detection of LGA using data up to 32- and 36-week cutoffs, single biometry analysis had higher sensitivity than longitudinal analysis (52% vs 46% and 62% vs 52%, respectively; both p<0.05). Restricting the analysis to subjects with the last observation taken within two weeks from the cutoff, the sensitivity for detection of LGA, but not SGA, increased to 65% and 72% for single biometry at the 32- and 36-week cutoffs, respectively. SGA screening performance was higher for preterm (<37 weeks) than for term cases (73% vs 46% sensitivity; p<0.05) for single biometry at ≤32 weeks.
conclusionsWhen growth abnormalities are defined based on birth weight, growth velocity (captured in the longitudinal analysis) does not provide additional information when compared to the last measurement for predicting SGA and LGA neonates, with both approaches detecting one-half of the neonates (FPR = 10%) from data collected at ≤32 weeks. Unlike for SGA, LGA detection can be improved if ultrasound scans are scheduled as close as possible to the gestational-age cutoff when a decision regarding the clinical management of the patient needs to be made. Screening performance for SGA is higher for neonates that will be delivered preterm.
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