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      Single and Serial Fetal Biometry to Detect Preterm and Term Small- and Large-for-Gestational-Age Neonates: A Longitudinal Cohort Study

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          Abstract

          Objectives

          To 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.

          Methods

          A 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 (<5 th centile) and LGA (>95 th centile) at birth based on INTERGROWTH-21 st 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.

          Results

          Fetal abdominal and head circumference measurements, as well as birth weights in the Detroit population, matched well to the INTERGROWTH-21 st 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.

          Conclusions

          When 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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          Most cited references33

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          The macrosomic fetus: a challenge in current obstetrics.

          There has been a rise in the prevalence of large newborns over a few decades in many parts of the world. There is ample evidence that fetal macrosomia is associated with increased risk of complications both for the mother and the newborn. In current obstetrics, the macrosomic fetus represents a frequent clinical challenge. Evidence is emerging that being born macrosomic is also associated with future health risks. To provide a review of causes and risks, prevention, prediction and clinical management of suspected large fetus/fetal macrosomia, primarily aimed at clinical obstetricians. Medline and EMBASE were searched between 1980 and 2007 by combining either 'fetal macrosomia' or 'large for gestational age' with other relevant terms. The Cochrane Database of Systematic Reviews was searched for the term 'fetal macrosomia'. Although the causes of high birthweight include both genetic and environmental factors, the rapid increase in the prevalence of large newborns has environmental causes. The evidence is extensive that maternal overweight and associated metabolic changes, including type 2 and gestational diabetes, play a central role. There is a paucity of studies of the effect of intervention before and/or during pregnancy on the risk of having an 'overweight newborn'. It appears rational, however, that preventive measures should primarily be implemented before pregnancy and should include guidance about nutrition and physical activity in order to reduce the prevalence of overweight. In pregnancy, limited weight gain, especially in obese women, seems to reduce the risk of macrosomia, as do good control of plasma glucose among those with diabetes. Prediction of fetal macrosomia remains an inaccurate task even with modern ultrasound equipment. There is little evidence that routine elective delivery (induction or caesarean section) for the mere reason of suspected macrosomia should be employed in a general population. Vaginal delivery of a macrosomic fetus requires considered attention by an experienced obstetrician and preparedness for operative delivery, shoulder dystocia and newborn asphyxia.
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            Does antenatal identification of small-for-gestational age fetuses significantly improve their outcome?

            Most obstetric clinics have a program for the identification of small-for-gestational age (SGA) fetuses because of the increased risk of fetal complications that they present. We have a structured model for the identification and follow-up of SGA pregnancies. We aimed to determine whether the recognition of SGA antepartum improves fetal outcome. All pregnancies at Malmö University Hospital from 1990 to 1998 (n = 26 968) were reviewed. SGA fetuses identified prior to delivery (n = 681) were compared with those not identified (n = 573). Also, all pregnancies with SGA fetuses were compared with those appropriate-for-gestational age (AGA) (n = 24 585). The risk of serious fetal complications (hypoxic encephalopathy grade 2 or 3, intracranial hemorrhage, Apgar score <4 at 5 min, neonatal convulsions, umbilical pH <7.0, cerebral palsy, mental retardation, stillbirth, intrapartum or infant death) was assessed with cross-tabulation and logistic regression analysis, adjusted for gestational age and degree of SGA. When compared with SGA fetuses identified before delivery (54%), SGA fetuses not identified before delivery were characterized by a four-fold increased risk of adverse fetal outcome (odds ratio, 4.1; 95% CI, 2.5-6.8). Similarly, compared with AGA fetuses, SGA fetuses were associated with a four-fold increased risk of serious fetal complications. A structured antenatal surveillance program for fetuses identified as SGA results in a lower risk of adverse fetal outcome, compared with cases of SGA fetuses not identified antepartum.
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              Novel biomarkers for predicting intrauterine growth restriction: a systematic review and meta-analysis.

              Several biomarkers for predicting intrauterine growth restriction (IUGR) have been proposed in recent years. However, the predictive performance of these biomarkers has not been systematically evaluated. To determine the predictive accuracy of novel biomarkers for IUGR in women with singleton gestations. Electronic databases, reference list checking and conference proceedings. Observational studies that evaluated the accuracy of novel biomarkers proposed for predicting IUGR. Data were extracted on characteristics, quality and predictive accuracy from each study to construct 2×2 tables. Summary receiver operating characteristic curves, sensitivities, specificities and likelihood ratios (LRs) were generated. A total of 53 studies, including 39,974 women and evaluating 37 novel biomarkers, fulfilled the inclusion criteria. Overall, the predictive accuracy of angiogenic factors for IUGR was minimal (median pooled positive and negative LRs of 1.7, range 1.0-19.8; and 0.8, range 0.0-1.0, respectively). Two small case-control studies reported high predictive values for placental growth factor and angiopoietin-2 only when IUGR was defined as birthweight centile with clinical or pathological evidence of fetal growth restriction. Biomarkers related to endothelial function/oxidative stress, placental protein/hormone, and others such as serum levels of vitamin D, urinary albumin:creatinine ratio, thyroid function tests and metabolomic profile had low predictive accuracy. None of the novel biomarkers evaluated in this review are sufficiently accurate to recommend their use as predictors of IUGR in routine clinical practice. However, the use of biomarkers in combination with biophysical parameters and maternal characteristics could be more useful and merits further research. © 2013 The Authors BJOG An International Journal of Obstetrics and Gynaecology © 2013 RCOG.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                1 November 2016
                2016
                : 11
                : 11
                : e0164161
                Affiliations
                [1 ]Perinatology Research Branch, NICHD/NIH/DHHS, Bethesda, MD, and Detroit, Michigan, United States of America
                [2 ]Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, United States of America
                [3 ]Department of Computer Science, Wayne State University College of Engineering, Detroit, Michigan, United States of America
                [4 ]Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, United States of America
                [5 ]Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, Michigan, United States of America
                [6 ]Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan, United States of America
                Johns Hopkins University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: ALT EHA HA TC SSH LY RR.

                • Data curation: ALT EHA HA MG HS TC SSH LY RR.

                • Formal analysis: ALT EHA.

                • Funding acquisition: ALT EHA SJK HS TC SSH LY RR.

                • Investigation: EHA HA MG SJK HS TC SSH LY RR.

                • Methodology: ALT EHA SJK HS TC SSH LY RR.

                • Project administration: ALT EHA SSH RR.

                • Resources: ALT EHA SSH LY RR.

                • Software: ALT.

                • Supervision: ALT EHA SSH RR.

                • Validation: ALT EHA.

                • Visualization: ALT EHA ZX.

                • Writing – original draft: ALT EHA LY RR.

                • Writing – review & editing: ALT EHA HA MG SJK HS TC SSH LY RR.

                Author information
                http://orcid.org/0000-0003-1712-7588
                Article
                PONE-D-16-29509
                10.1371/journal.pone.0164161
                5089737
                27802270
                633cf49c-aebd-4d4a-b9e1-9bfb0beb0569

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 23 July 2016
                : 20 September 2016
                Page count
                Figures: 4, Tables: 2, Pages: 16
                Funding
                Funded by: NICHD
                Award ID: HHSN275201300006C
                This research was supported, in part, by the Perinatology Research Branch, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services (NICHD/NIH/DHHS), and, in part, with Federal funds from NICHD/NIH/DHHS under Contract No. HHSN275201300006C. ALT and SJK were supported by the Perinatal Initiative of the Wayne State University School of Medicine (WSUSOM), and the Department of Obstetrics and Gynecology of the WSUSOM.
                Categories
                Research Article
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Biostatistics
                Biology and Life Sciences
                Developmental Biology
                Neonates
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Pregnancy
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Pregnancy
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Birth Weight
                Medicine and Health Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Birth Weight
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Birth
                Labor and Delivery
                Medicine and Health Sciences
                Women's Health
                Obstetrics and Gynecology
                Birth
                Labor and Delivery
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Ultrasound Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Ultrasound Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Ultrasound Imaging
                Biology and Life Sciences
                Developmental Biology
                Embryology
                Fetuses
                Research and Analysis Methods
                Computational Techniques
                Biometrics
                Custom metadata
                The patients enrolled in this study did not agree with broad sharing of their ultrasound data. The authors do not have IRB approval to share the anonymized data with whoever asks for it. According to the data sharing policy of the NICHD branch that generated these data, upon request from the authors, data required to reproduce the main findings of this study may be shared with approved outside collaborators under appropriate agreements.

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