Advertisement

A new customized fetal growth standard for African American women: the PRB/NICHD Detroit study

  • Adi L. Tarca
    Affiliations
    Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health/US Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI

    Department of Computer Science, Wayne State University College of Engineering, Detroit, MI
    Search for articles by this author
  • Roberto Romero
    Correspondence
    Corresponding author: Roberto Romero, MD, DMedSci.
    Affiliations
    Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health/US Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI

    Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI

    Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI
    Search for articles by this author
  • Dereje W. Gudicha
    Affiliations
    Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health/US Department of Health and Human Services, Bethesda, MD, and Detroit, MI
    Search for articles by this author
  • Offer Erez
    Affiliations
    Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health/US Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
    Search for articles by this author
  • Edgar Hernandez-Andrade
    Affiliations
    Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health/US Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
    Search for articles by this author
  • Lami Yeo
    Affiliations
    Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health/US Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
    Search for articles by this author
  • Gaurav Bhatti
    Affiliations
    Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health/US Department of Health and Human Services, Bethesda, MD, and Detroit, MI
    Search for articles by this author
  • Percy Pacora
    Affiliations
    Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health/US Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
    Search for articles by this author
  • Eli Maymon
    Affiliations
    Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health/US Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI
    Search for articles by this author
  • Sonia S. Hassan
    Affiliations
    Perinatology Research Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health/US Department of Health and Human Services, Bethesda, MD, and Detroit, MI

    Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI

    Department of Physiology, Wayne State University School of Medicine, Detroit, MI
    Search for articles by this author

      Background

      The assessment of fetal growth disorders requires a standard. Current nomograms for the assessment of fetal growth in African American women have been derived either from neonatal (rather than fetal) biometry data or have not been customized for maternal ethnicity, weight, height, and parity and fetal sex.

      Objective

      We sought to (1) develop a new customized fetal growth standard for African American mothers; and (2) compare such a standard to 3 existing standards for the classification of fetuses as small (SGA) or large (LGA) for gestational age.

      Study Design

      A retrospective cohort study included 4183 women (4001 African American and 182 Caucasian) from the Detroit metropolitan area who underwent ultrasound examinations between 14-40 weeks of gestation (the median number of scans per pregnancy was 5, interquartile range 3-7) and for whom relevant covariate data were available. Longitudinal quantile regression was used to build models defining the “normal” estimated fetal weight (EFW) centiles for gestational age in African American women, adjusted for maternal height, weight, and parity and fetal sex, and excluding pathologic factors with a significant effect on fetal weight. The resulting Perinatology Research Branch/Eunice Kennedy Shriver National Institute of Child Health and Human Development (hereinafter, PRB/NICHD) growth standard was compared to 3 other existing standards--the customized gestation-related optimal weight (GROW) standard; the Eunice Kennedy Shriver National Institute of Child Health and Human Development (hereinafter, NICHD) African American standard; and the multinational World Health Organization (WHO) standard--utilized to screen fetuses for SGA (<10th centile) or LGA (>90th centile) based on the last available ultrasound examination for each pregnancy.

      Results

      First, the mean birthweight at 40 weeks was 133 g higher for neonates born to Caucasian than to African American mothers and 150 g higher for male than female neonates; maternal weight, height, and parity had a positive effect on birthweight. Second, analysis of longitudinal EFW revealed the following features of fetal growth: (1) all weight centiles were about 2% higher for male than for female fetuses; (2) maternal height had a positive effect on EFW, with larger fetuses being affected more (2% increase in the 95th centile of weight for each 10-cm increase in height); and (3) maternal weight and parity had a positive effect on EFW that increased with gestation and varied among the weight centiles. Third, the screen-positive rate for SGA was 7.2% for the NICHD African American standard, 12.3% for the GROW standard, 13% for the WHO standard customized by fetal sex, and 14.4% for the PRB/NICHD customized standard. For all standards, the screen-positive rate for SGA was at least 2-fold higher among fetuses delivered preterm than at term. Fourth, the screen-positive rate for LGA was 8.7% for the GROW standard, 9.2% for the PRB/NICHD customized standard, 10.8% for the WHO standard customized by fetal sex, and 12.3% for the NICHD African American standard. Finally, the highest overall agreement among standards was between the GROW and PRB/NICHD customized standards (Cohen's interrater agreement, kappa = 0.85).

      Conclusion

      We developed a novel customized PRB/NICHD fetal growth standard from fetal data in an African American population without assuming proportionality of the effects of covariates, and without assuming that these effects are equal on all centiles of weight; we also provide an easy-to-use centile calculator. This standard classified more fetuses as being at risk for SGA compared to existing standards, especially among fetuses delivered preterm, but classified about the same number of LGA. The comparison among the 4 growth standards also revealed that the most important factor determining agreement among standards is whether they account for the same factors known to affect fetal growth.

      Key words

      Introduction

      Growth is a time-dependent change of bodily dimensions.
      • Jeanty P.
      • Cantraine F.
      • Romero R.
      • Cousaert E.
      • Hobbins J.C.
      A longitudinal study of fetal weight growth.
      The human fetus grows at a particularly rapid rate,
      • Lampl M.
      • Jeanty P.
      Timing is everything: a reconsideration of fetal growth velocity patterns identifies the importance of individual and sex differences.
      • Tanner J.M.
      Fetus into man.
      and this is important because a principle of developmental biology is that organisms are more susceptible to injury during periods of fast growth.
      • Bornstein M.H.
      • Arterberry M.E.
      • Lamb M.E.
      Development in infancy: a contemporary introduction.
      Birthweight has been used extensively as a parameter to characterize the appropriateness of fetal growth
      • Battaglia F.C.
      • Lubchenco L.O.
      A practical classification of newborn infants by weight and gestational age.
      and, to date, remains the most frequently used index to assess size as a proxy to growth. Therefore, in clinical practice, many obstetricians rely on the assessment of sonographic estimation of fetal weight to evaluate fetal size and growth.
      • Gaccioli F.
      • Aye I.
      • Sovio U.
      • Charnock-Jones D.S.
      • Smith G.C.S.
      Screening for fetal growth restriction using fetal biometry combined with maternal biomarkers.
      • Kalafat E.
      • Morales-Rosello J.
      • Thilaganathan B.
      • Tahera F.
      • Khalil A.
      Risk of operative delivery for intrapartum fetal compromise in small-for-gestational-age fetuses at term: an internally validated prediction model.
      • McEwen E.C.
      • Guthridge S.L.
      • He V.Y.
      • McKenzie J.W.
      • Boulton T.J.
      • Smith R.
      What birthweight percentile is associated with optimal perinatal mortality and childhood education outcomes?.
      • Mendez-Figueroa H.
      • Truong V.T.
      • Pedroza C.
      • Khan A.M.
      • Chauhan S.P.
      Small-for-gestational-age infants among uncomplicated pregnancies at term: a secondary analysis of 9 Maternal-Fetal Medicine Units Network studies.
      • Costantine M.M.
      • Mele L.
      • Landon M.B.
      • et al.
      Customized versus population approach for evaluation of fetal overgrowth.
      • Anderson N.H.
      • Sadler L.C.
      • McKinlay C.J.
      • McCowan L.M.
      INTERGROWTH-21st vs customized birthweight standards for identification of perinatal mortality and morbidity.
      • Chauhan S.P.
      • Beydoun H.
      • Chang E.
      • et al.
      Prenatal detection of fetal growth restriction in newborns classified as small for gestational age: correlates and risk of neonatal morbidity.
      Although the terms “fetal size” and “fetal growth” are not synonymous, there is a relationship between the two, and this is why “fetal size charts” have been referred to as “fetal growth charts.”
      Fetal weight is estimated from ultrasound measurements of fetal biometric parameters (eg, biparietal diameter [BPD], abdominal circumference [AC], femur length [FL], and head circumference [HC]) using 1 of many mathematical formulas.
      • Hadlock F.P.
      • Deter R.L.
      • Harrist R.B.
      • Park S.K.
      Estimating fetal age: computer-assisted analysis of multiple fetal growth parameters.
      • Hadlock F.P.
      • Harrist R.B.
      • Sharman R.S.
      • Deter R.L.
      • Park S.K.
      Estimation of fetal weight with the use of head, body, and femur measurements–a prospective study.
      • Villar J.
      • Knight H.E.
      • de Onis M.
      • et al.
      Conceptual issues related to the construction of prescriptive standards for the evaluation of postnatal growth of preterm infants.
      • Papageorghiou A.T.
      • Ohuma E.O.
      • Altman D.G.
      • et al.
      International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st project.
      One widely used equation for estimated fetal weight (EFW) is that proposed by Hadlock et al,
      • Hadlock F.P.
      • Harrist R.B.
      • Sharman R.S.
      • Deter R.L.
      • Park S.K.
      Estimation of fetal weight with the use of head, body, and femur measurements–a prospective study.
      which includes HC, AC, and FL. Assessment of the appropriateness of fetal size is performed by comparing the observed EFW to a standard. Yet, which standard should be used is a subject of debate.
      One issue is whether the same standard, referred to as “population-based,” should be used for all fetuses,
      • Papageorghiou A.T.
      • Ohuma E.O.
      • Altman D.G.
      • et al.
      International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st project.
      or whether the standard should be customized for physiologic and constitutional factors known to affect neonatal size at birth
      • Gardosi J.
      • Chang A.
      • Kalyan B.
      • Sahota D.
      • Symonds E.M.
      Customized antenatal growth charts.
      • Gardosi J.
      • Mongelli M.
      • Wilcox M.
      • Chang A.
      An adjustable fetal weight standard.
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      as well as EFW.
      • Kiserud T.
      • Piaggio G.
      • Carroli G.
      • et al.
      The World Health Organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight.
      • Buck Louis G.M.
      • Grewal J.
      • Albert P.S.
      • et al.
      Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
      One of the most widely used population-based growth charts was proposed by Hadlock et al
      • Hadlock F.P.
      • Harrist R.B.
      • Martinez-Poyer J.
      In utero analysis of fetal growth: a sonographic weight standard.
      based on data collected from 392 Caucasian women in the United States. The same investigators suggested using the 10th and 90th centiles of the EFW to evaluate fetal size and growth–adopting the concepts of Battaglia and Lubchenco,
      • Battaglia F.C.
      • Lubchenco L.O.
      A practical classification of newborn infants by weight and gestational age.
      who classified neonates with a birthweight <10th centile as small for gestational age (SGA) and those >90th centile as large for gestational age (LGA). However, fetuses with an EFW <10th or >90th centile are a heterogeneous group: some SGA fetuses have growth deceleration, and others are constitutionally small. Growth-restricted fetuses are those that have deviated from their growth potential, unlike those who are constitutionally small. Similar concepts apply to LGA fetuses, which could either experience fetal growth acceleration or be constitutionally large.
      • Vrachnis N.
      • Botsis D.
      • Iliodromiti Z.
      The fetus that is small for gestational age.
      To address the need for distinguishing between constitutionally small or large fetuses and those affected by growth disorders, Gardosi et al
      • Gardosi J.
      • Chang A.
      • Kalyan B.
      • Sahota D.
      • Symonds E.M.
      Customized antenatal growth charts.
      • Gardosi J.
      • Mongelli M.
      • Wilcox M.
      • Chang A.
      An adjustable fetal weight standard.
      proposed to customize the chart of Hadlock et al
      • Hadlock F.P.
      • Harrist R.B.
      • Martinez-Poyer J.
      In utero analysis of fetal growth: a sonographic weight standard.
      by shifting the normal EFW centiles proportionally up or down so that the mean weight at 40 weeks matches “term optimal weight.” Term optimal weight is personalized for each fetus based on maternal ethnicity, height, weight, and parity and fetal sex, and excludes pathological factors known to affect birthweight, such as smoking. This approach, referred to as gestation-related optimal weight (GROW), derives customization coefficients for nonpathologic maternal characteristics and fetal sex by analyzing birthweight data in local populations.
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      • Figueras F.
      • Meler E.
      • Iraola A.
      • et al.
      Customized birthweight standards for a Spanish population.
      • Unterscheider J.
      • Geary M.P.
      • Daly S.
      • et al.
      The customized fetal growth potential: a standard for Ireland.
      Other approaches to the customization of growth charts include the individualized growth assessment
      • Deter R.L.
      Individualized growth assessment: evaluation of growth using each fetus as its own control.
      • Deter R.L.
      • Lee W.
      • Sangi-Haghpeykar H.
      • Tarca A.L.
      • Yeo L.
      • Romero R.
      Individualized fetal growth assessment: critical evaluation of key concepts in the specification of third trimester size trajectories.
      • Barker E.D.
      • McAuliffe F.M.
      • Alderdice F.
      • et al.
      The role of growth trajectories in classifying fetal growth restriction.
      that assumes all relevant factors that determine the growth potential of a fetus are captured in the rate of growth during the second trimester. The importance of considering longitudinal measurements to derive fetus-specific growth velocity was also highlighted by Sovio et al,
      • Sovio U.
      • White I.R.
      • Dacey A.
      • Pasupathy D.
      • Smith G.C.
      Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the pregnancy outcome prediction (POP) study: a prospective cohort study.
      who found that the SGA fetuses identified based on the chart of Hadlock et al
      • Hadlock F.P.
      • Harrist R.B.
      • Martinez-Poyer J.
      In utero analysis of fetal growth: a sonographic weight standard.
      were at risk for neonatal morbidity only if their fetal AC growth velocity was in the lowest decile.
      • Sovio U.
      • White I.R.
      • Dacey A.
      • Pasupathy D.
      • Smith G.C.
      Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the pregnancy outcome prediction (POP) study: a prospective cohort study.
      • Romero R.
      • Deter R.
      Should serial fetal biometry be used in all pregnancies?.
      Although several studies suggest that estimates for the association between adverse neonatal outcomes and abnormal birthweight are higher for customized than noncustomized (population-based) standards,
      • McCowan L.M.
      • Harding J.E.
      • Stewart A.W.
      Customized birthweight centiles predict SGA pregnancies with perinatal morbidity.
      • Johnsen S.L.
      • Rasmussen S.
      • Wilsgaard T.
      • Sollien R.
      • Kiserud T.
      Longitudinal reference ranges for estimated fetal weight.
      • Odibo A.O.
      • Francis A.
      • Cahill A.G.
      • Macones G.A.
      • Crane J.P.
      • Gardosi J.
      Association between pregnancy complications and small-for-gestational-age birth weight defined by customized fetal growth standard versus a population-based standard.
      • Cha H.H.
      • Kim J.Y.
      • Choi S.J.
      • Oh S.Y.
      • Roh C.R.
      • Kim J.H.
      Can a customized standard for large for gestational age identify women at risk of operative delivery and shoulder dystocia?.
      • Kase B.A.
      • Carreno C.A.
      • Blackwell S.C.
      Customized estimated fetal weight: a novel antenatal tool to diagnose abnormal fetal growth.
      • Sovio U.
      • Smith G.C.S.
      The effect of customization and use of a fetal growth standard on the association between birthweight percentile and adverse perinatal outcome.
      • Simcox L.E.
      • Myers J.E.
      • Cole T.J.
      • Johnstone E.D.
      Fractional fetal thigh volume in the prediction of normal and abnormal fetal growth during the third trimester of pregnancy.
      recent initiatives undertaken to develop growth standards proposed either population-based or only partially customized standards. For example, the INTERGROWTH-21st study
      • Papageorghiou A.T.
      • Ohuma E.O.
      • Altman D.G.
      • et al.
      International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st project.
      • Altman D.G.
      • Ohuma E.O.
      International Fetal and Newborn Growth Consortium for the 21st Century
      Statistical considerations for the development of prescriptive fetal and newborn growth standards in the INTERGROWTH-21st project.
      • Cheikh Ismail L.
      • Knight H.E.
      • Ohuma E.O.
      • Hoch L.
      • Chumlea W.C.
      Anthropometric standardization and quality control protocols for the construction of new, international, fetal and newborn growth standards: the INTERGROWTH-21st project.
      • Villar J.
      • Altman D.G.
      • Purwar M.
      • et al.
      The objectives, design and implementation of the INTERGROWTH-21st project.
      proposed a one-size-fits-all standard derived from a multiethnic population. By contrast, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) fetal growth studies
      • Buck Louis G.M.
      • Grewal J.
      • Albert P.S.
      • et al.
      Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
      reported standards specific to 4 different ethnic-racial groups (non-Hispanic White, Hispanic, African American, and Asian),
      • Buck Louis G.M.
      • Grewal J.
      • Albert P.S.
      • et al.
      Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
      yet customization by factors other than race was not provided. Recently, a study sponsored by the World Health Organization (WHO)
      • Kiserud T.
      • Piaggio G.
      • Carroli G.
      • et al.
      The World Health Organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight.
      • Merialdi M.
      • Widmer M.
      • Gulmezoglu A.M.
      • et al.
      WHO multicenter study for the development of growth standards from fetal life to childhood: the fetal component.
      proposed a multiethnic growth standard customized only by fetal sex, despite the observation that other factors (eg, country of origin, maternal age, height, and parity) had independent effects on EFW. Of interest, by using quantile regression to model EFW data (an approach that does not rely on assuming normal distribution of the data), the investigators reported that the effects of several factors (eg, maternal height and weight, fetal sex) were graded among the centiles of weight distribution. For example, maternal weight had a higher effect on larger fetuses than on smaller fetuses.
      • Kiserud T.
      • Piaggio G.
      • Carroli G.
      • et al.
      The World Health Organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight.
      The most widely adopted customization approach is that of Gardosi et al,
      • Gardosi J.
      • Mongelli M.
      • Wilcox M.
      • Chang A.
      An adjustable fetal weight standard.
      which is based on birthweight data and assumes that the effects of covariates are proportional during gestation (eg, fetuses of parous mothers will have a higher EFW than those of nulliparous mothers by the same proportion at all gestational ages). However, the assumption of proportionality has not been tested thus far using longitudinal fetal data. Our study is based on a cohort of pregnant women who attended our center in Detroit, MI, where the predominant ethnic group is African American based on self-reporting. The objectives of this study were to (1) develop a new customized fetal growth standard for African American women; and (2) compare the standard derived from our population to 3 existing standards for the classification of fetuses as SGA and LGA.

      Materials and Methods

      Study population

      This retrospective longitudinal cohort study was conducted at the Center for Advanced Obstetrical Care and Research of the Perinatology Research Branch (PRB), NICHD, National Institutes of Health, US Department of Health and Human Services. The Center is housed at Hutzel Women’s Hospital in partnership with the Wayne State University School of Medicine in Detroit, MI. All patients included in this study provided written informed consent for ultrasound examinations and were enrolled in research protocols approved by the Human Investigation Committee of Wayne State University and the Institutional Review Board of NICHD.
      From 2002 through 2016, 4681 pregnant women were enrolled and had ultrasound examinations performed by a maternal-fetal specialist or a senior sonographer with >3 years of experience who performs a minimum of 300 ultrasound scans per year. More than 95% of women were actually enrolled from 2006 through 2015, at an average enrollment of 445 per year, which represents about 25% of the yearly enrollment at our clinic. Women self-reported as African American, 4239 (90.6%); Caucasian, 197 (4.2%); Hispanic, 31 (0.7%); Asian, 31 (0.7%); and 183 (3.9%) either as other or unknown race or ethnicity. African American and Caucasian women were included in this study, regardless of pregnancy outcome, if they met the following criteria: (1) age 18-40 years; and (2) had at least 1 ultrasound examination performed between 14-40 weeks of gestation with available measurements of the AC, HC, FL, BPD, and gestational age at each examination. Of the 4143 African American and 188 Caucasian women who met these inclusion criteria, 4 were excluded because of outlier fetal biometric measurements, and 144 (3.3%) were excluded because of missing data on maternal weight, height, and parity or fetal sex, resulting in 4001 African American and 182 Caucasian women (Supplementary Figure 1).

      Ultrasound examinations

      Ultrasound studies were performed using the General Electric Voluson Expert and Voluson E8 (GE Healthcare, Milwaukee, WI) ultrasound systems and 5- to 2-MHz probes. Biometric measurements were obtained using methods previously described by Chitty et al
      • Chitty L.S.
      • Altman D.G.
      • Henderson A.
      • Campbell S.
      Charts of fetal size: 4. Femur length.
      • Chitty L.S.
      • Altman D.G.
      • Henderson A.
      • Campbell S.
      Charts of fetal size: 3. Abdominal measurements.
      • Chitty L.S.
      • Altman D.G.
      • Henderson A.
      • Campbell S.
      Charts of fetal size: 2. Head measurements.
      and Altman and Chitty,
      • Altman D.G.
      • Chitty L.S.
      Design and analysis of studies to derive charts of fetal size.
      which are consistent with recommendations of the International Society of Ultrasound in Obstetrics and Gynecology
      • Salomon L.J.
      • Alfirevic Z.
      • Berghella V.
      • et al.
      Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan.
      and the American Institute of Ultrasound in Medicine.
      American Institute of Ultrasound in Medicine
      AIUM practice guideline for the performance of obstetric ultrasound examinations.
      Fetal biometric parameters included: (1) BPD (outer edge to inner edge of the calvarium); (2) HC (ellipse around the outside of the calvarium); (3) AC (ellipse placed at the outer surface of the skin); and (4) FL (calipers placed at the ends of the ossified diaphysis). EFW was computed from the AC, HC, and FL measurements using the formula of Hadlock et al
      • Hadlock F.P.
      • Harrist R.B.
      • Sharman R.S.
      • Deter R.L.
      • Park S.K.
      Estimation of fetal weight with the use of head, body, and femur measurements–a prospective study.
      to enable direct comparison to previous standards. The indices of proportionality (HC/AC, FL/AC, and BPD/FL) were also determined. The median number of ultrasound examinations per pregnancy was 5 (interquartile range [IQR] 4-7). Gestational age was determined based on the last menstrual period and validated during the first ultrasound examination either by crown-rump length or BPD measurement.

      Statistical analysis

      Effect of covariates on birthweight

      We used multilinear regression with backward elimination as described by Gardosi and Francis
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      to assess the effect of covariates on birthweight at 40 weeks of gestation (280 days). The birthweight of neonates born ≥37 gestational weeks was regressed on self-reported ethnicity, height and weight, parity, fetal sex, and gestational age at delivery as well as the following pathologic factors: extremely low or high body mass index (BMI) (defined as <20.5 kg/m2 or >40.5 kg/m2, respectively), smoking status, gestational diabetes mellitus, hypertension, preeclampsia, and fetal anomalies. A P value <.05 was considered significant.

      Development of a customized (PRB/NICHD) fetal growth standard for African American women

      We used penalized fixed-effects quantile regression models
      • Koenker R.
      Quantile regression for longitudinal data.
      • Koenker R.W.
      Quantile regression.
      to fit individual centiles (5th, 10th, 50th, 90th, and 95th) of the distributions of fetal biometric parameters, indices of proportionality, and EFW as a function of gestational age. We relied on Bayesian information criteria recommended by Lee et al
      • Lee E.R.
      • Noh H.
      • Park B.U.
      Model selection via Bayesian information criterion for quantile regression models.
      to determine the “shrinkage parameter” of the fetus-specific fixed effects. The resulting population-level centiles (ie, noncustomized, and representing the entire study population) were superimposed on the raw data for visualization purposes and compared to other noncustomized standards, such as the NICHD African American standard
      • Buck Louis G.M.
      • Grewal J.
      • Albert P.S.
      • et al.
      Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
      and the WHO standard noncustomized by fetal sex.
      To determine the effect of covariates on fetal weight centiles, additional covariates were considered for inclusion in the quantile regression models and retained if significant: maternal height, weight, and parity; fetal sex; extremely low or high BMI; smoking status; diabetes; hypertension; preeclampsia; preterm delivery; fetal anomalies; and, importantly, interaction terms between these covariates and gestational age. The 5th, 10th, 50th, 90th, and 95th centiles of EFW were derived from a model that had the same terms but eventually different coefficients for each centile curve. The EFW data were first log transformed; therefore, each covariate without a significant interaction with gestational age had a constant proportional effect on a given EFW centile throughout gestation. The effects of covariates were reported as a percentage of change in estimated weight.
      Although fitting of the quantile regression models involved EFW data from all pregnancies regardless of outcome, the prediction of customized normal centiles from the quantile regression models was based only on the contribution of nonpathologic factors that affect growth. This is in keeping with the concept proposed by Gardosi et al.
      • Gardosi J.
      • Mongelli M.
      • Wilcox M.
      • Chang A.
      An adjustable fetal weight standard.
      All statistical analyses were conducted using the R statistical language and environment (www.r-project.org), including the rqpd package for longitudinal quantile regression, available from R-Forge (https://r-forge.r-project.org). Centiles for the customized GROW standard were obtained using the bulk centile calculator version 6.7.8_US from the authors’ website (https://www.gestation.net/).

      Results

      Maternal characteristics

      For the group of 4001 African American women, the median maternal age, height, and weight were 23 (IQR 20-27) years, 163 (IQR 157-168) cm, and 73 (IQR 61-91) kg, respectively. There were 632 women (15.8%) who delivered preterm (<37 weeks of gestation), and 1457 (36%) were nulliparous.
      For the group of 182 Caucasian women, the median maternal age, height, and weight were 26 (IQR 22-30) years, 163 (IQR 157-168) cm, and 68 (IQR 59-84) kg, respectively. There were 29 women (15.9%) who delivered preterm, and 67 (37%) were nulliparous.

      Factors affecting birthweight of neonates delivered at term

      Neonatal data were analyzed from 3368 African American and 152 Caucasian women who delivered at term and had available birthweight data. Table 1 shows the results of multilinear regression of birthweight on gestational age at delivery, maternal weight, height, and parity and fetal sex, as well as pathologic risk factors: extremely low or high BMI, smoking, and diabetes. All of these variables explained 28% of the variance in birthweight at term (R2 = 0.28).
      Table 1Effect of covariates on birthweight in women with term delivery
      VariableBirthweight, gP value
      CoefficientSE
      Intercept322316.3<.001
      GA from 40 wk
       Linear14410.3<.001
       Quadratic–156.0.02
       Cubic32.7.36
      Sex
       Male15013.3<.001
      Race
       Caucasian13332.9<.001
      Maternal height (from 163 cm)
      Effect is estimated for 10-cm increments in maternal height
      7810.6<.001
      Maternal weight (from 64 kg)
      Effect is estimated for 10-kg increments in maternal weight.
      255.1<.001
      Parity
       Para 15816.6<.001
       Para 29619.6<.001
       Para 38520.5<.001
      Low BMI (<20.5 kg/m2)–8125.4.001
      High BMI (>40.4 kg/m2)–4032.1.21
      Smoking–9217.2<.001
      Diabetes24735.1<.001
      Analysis involved data from 3368 African American and 152 Caucasian women who delivered at term and had available birthweight data. In the regression model (R2 = 0.28), intercept (3223 g) represents mean birthweight at 40 wk (280 d) of GA for a nulliparous African American mother, having a height of 163 cm, weighing 64 kg at first visit, nonsmoking, and without diabetes; 10th/90th centiles of BMI in African American women in the study population were used to define abnormally low and high BMI, respectively.
      BMI, body mass index; GA, gestational age.
      Tarca et al. Fetal growth chart for African American women. Am J Obstet Gynecol 2018.
      a Effect is estimated for 10-cm increments in maternal height
      b Effect is estimated for 10-kg increments in maternal weight.
      The mean birthweight at 40 weeks (280 days) was 3223 g for a female fetus born to a nulliparous African American mother having a height of 163 cm, weighing 64 kg at the first visit, nonsmoking, and without diabetes (Table 1). Such a combination of maternal weight and height for the reference pregnancy was used to enable direct comparisons to previously reported effects on birthweight in a different US population.
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      Independent of all other factors listed in Table 1, mean birthweight was higher for male fetuses (by 150 g), Caucasian mothers (by 133 g), and parous women (58 g, 96 g, and 85 g for parity 1, 2, and ≥3, respectively). An additional 10 cm in maternal height increased birthweight by 78 g, and an additional 10 kg of maternal weight was associated with a 25-g increase in birthweight. Such increments in maternal height and weight were chosen to enable comparison to a previous study.
      • Kiserud T.
      • Piaggio G.
      • Carroli G.
      • et al.
      The World Health Organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight.
      A low BMI (<10th percentile, 20.5 kg/m2) was associated with an 81-g decrease in mean birthweight, whereas a high BMI (>90th percentile, 40.4 kg/m2) had a negative effect on the mean birthweight that did not reach statistical significance (40 g, P = .21). Smoking was associated with a 92-g decrease in mean birthweight, while diabetes was associated with a 247-g increase in mean birthweight. Preeclampsia, gestational hypertension, and fetal anomalies were considered as pathologic covariates, but they did not have a significant effect on term birthweight (all P > .05) and were not included in the regression model (Table 1). Neonates with congenital anomalies had a lower mean birthweight (71-g difference); however, this was not significant, probably due to the low prevalence of congenital anomalies in our cohort (1.8%). Although more prevalent, preeclampsia (4.8%) and gestational hypertension (13.1%) had a smaller magnitude of effect; hence, they were also nonsignificant in this analysis.

      Customized fetal growth standard for the African American population in Detroit, MI

      Given the ethnic differences in EFW reported in the NICHD study
      • Buck Louis G.M.
      • Grewal J.
      • Albert P.S.
      • et al.
      Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
      and in birthweight data reported herein, combined with the limited number of Caucasian women in our study population, we decided to focus on developing a customized fetal growth standard for African American women. Noncustomized centiles (5th, 10th, 50th, 90th, and 95th) of fetal biometric parameters, EFW, and indices of proportionality for all 4001 African American women (regardless of clinical outcome) are shown in Supplementary Figure 2. The centile curves in Supplementary Figure 2 can be considered a local reference since about 10% of data points are </>10th/90th centiles and no pathologic factors were excluded. The local reference for EFW was superimposed onto the noncustomized NICHD African American and WHO standards (Supplementary Figure 3). While the 10th, 50th, and 90th EFW centile curves for our local reference were systematically lower than those of the WHO standard, the variability in estimated weight at 40 weeks (distance between the 10th and 90th centiles) were similar. By contrast, the 10th centile of the NICHD standard was lower (especially close to term), the 50th centile was about the same, and the 90th centile was higher than that of our local reference (Supplementary Figure 3).
      To define a customized EFW chart that corresponds to normal growth, we fitted quantile regression models that included maternal height, weight, and parity and fetal sex, while accounting for and excluding the contribution of pathologic factors with significant effect on at least 1 of the weight centiles: extremely low or high BMI, smoking, diabetes, preterm delivery, and fetal anomalies (Supplementary Table). Figure 1 shows the effects of nonpathologic covariates on the predicted normal fetal weight centiles (10th, 50th, and 90th). In Figure 1, the EFW standard used as the baseline (continuous lines) corresponds to a female fetus of a nulliparous African American mother, who is 163 cm in height and 64 kg in weight. Since the effects (derived from quantile regression models described in the Supplementary Table) may vary with gestational age for some covariates, these effects are presented at 2 gestational ages (30 and 40 weeks) in Table 2 and can be summarized as follows:
      Figure thumbnail gr1
      Figure 1Effect of covariates on fetal growth in African American women
      Unless otherwise stated, continuous lines represent the estimated fetal weight median and 10th/90th centiles for a female (F) fetus born at term to a nulliparous African American mother with a height (Ht) of 163 cm and a weight (Wt) of 64 kg at the first visit. Interrupted lines show how the chart would change for: A, male (M) fetus; B, additional 10 kg of maternal Wt; C, mother in her third pregnancy (parity = 2); and D, combination of factors (additional 10 kg in maternal Wt, additional 10 cm Ht, parity of 2, and M fetus).
      Tarca et al. Fetal growth chart for African American women. Am J Obstet Gynecol 2018.
      Table 2Effect of covariates on estimated fetal weight at 30 and 40 weeks of gestation
      EFW (g) at 30 wkEFW (g) at 40 wk
      5th10th50th90th95th5th10th50th90th95th
      1206126014401643171627772914336937733902
      Effect (% change)
      Fetal sex (male)2.3
      Significant effects (P < .05).
      2
      Significant effects (P < .05).
      1.9
      Significant effects (P < .05).
      1.9
      Significant effects (P < .05).
      2.4
      Significant effects (P < .05).
      2.3
      Significant effects (P < .05).
      2
      Significant effects (P < .05).
      1.9
      Significant effects (P < .05).
      1.9
      Significant effects (P < .05).
      2.4
      Significant effects (P < .05).
      Maternal height (10 cm)0.9
      Significant effects (P < .05).
      1.0
      Significant effects (P < .05).
      1.2
      Significant effects (P < .05).
      1.8
      Significant effects (P < .05).
      1.9
      Significant effects (P < .05).
      0.9
      Significant effects (P < .05).
      1.0
      Significant effects (P < .05).
      1.2
      Significant effects (P < .05).
      1.8
      Significant effects (P < .05).
      1.9
      Significant effects (P < .05).
      Maternal weight (10 kg)0.6
      Significant effects (P < .05).
      0.7
      Significant effects (P < .05).
      0.6
      Significant effects (P < .05).
      0.6
      Significant effects (P < .05).
      0.6
      Significant effects (P < .05).
      1.4
      Significant effects (P < .05).
      1.4
      Significant effects (P < .05).
      1.1
      Significant effects (P < .05).
      1.2
      Significant effects (P < .05).
      1.1
      Significant effects (P < .05).
      Para 10.10.8
      Significant effects (P < .05).
      0.5
      Significant effects (P < .05).
      1.1
      Significant effects (P < .05).
      01.12.1
      Significant effects (P < .05).
      0.72.4
      Significant effects (P < .05).
      0.7
      Para 21.4
      Significant effects (P < .05).
      1.9
      Significant effects (P < .05).
      1.2
      Significant effects (P < .05).
      2.4
      Significant effects (P < .05).
      1.4
      Significant effects (P < .05).
      3.4
      Significant effects (P < .05).
      3.4
      Significant effects (P < .05).
      2.2
      Significant effects (P < .05).
      4.1
      Significant effects (P < .05).
      3.0
      Significant effects (P < .05).
      Para 31.00.91.5
      Significant effects (P < .05).
      2.3
      Significant effects (P < .05).
      1.8
      Significant effects (P < .05).
      1.10.22.4
      Significant effects (P < .05).
      4.0
      Significant effects (P < .05).
      3.7
      Significant effects (P < .05).
      BMI <20.51.60.8–1.4
      Significant effects (P < .05).
      –2.0
      Significant effects (P < .05).
      –2.2
      Significant effects (P < .05).
      1.6
      Significant effects (P < .05).
      0.8–1.4
      Significant effects (P < .05).
      –2.0
      Significant effects (P < .05).
      –2.2
      Significant effects (P < .05).
      BMI >40.4–1.4–1.3–0.9
      Significant effects (P < .05).
      –0.21.0–5.3
      Significant effects (P < .05).
      –4.6
      Significant effects (P < .05).
      –3.3
      Significant effects (P < .05).
      –1.70.2
      Smoking (yes)–3.6
      Significant effects (P < .05).
      –2.8
      Significant effects (P < .05).
      –2.1
      Significant effects (P < .05).
      –2.5
      Significant effects (P < .05).
      –2.5
      Significant effects (P < .05).
      –7.8
      Significant effects (P < .05).
      –5.5
      Significant effects (P < .05).
      –3.2
      Significant effects (P < .05).
      –3.5
      Significant effects (P < .05).
      –2.4
      Significant effects (P < .05).
      Diabetes5.4
      Significant effects (P < .05).
      4.6
      Significant effects (P < .05).
      3.4
      Significant effects (P < .05).
      3.7
      Significant effects (P < .05).
      3.1
      Significant effects (P < .05).
      6.5
      Significant effects (P < .05).
      5.6
      Significant effects (P < .05).
      4.2
      Significant effects (P < .05).
      4.6
      Significant effects (P < .05).
      4.5
      Significant effects (P < .05).
      Preterm delivery–12.0
      Significant effects (P < .05).
      –9.8
      Significant effects (P < .05).
      –2.8
      Significant effects (P < .05).
      0.31.3–14.5
      Significant effects (P < .05).
      –11.9
      Significant effects (P < .05).
      –3.5
      Significant effects (P < .05).
      –0.71.3
      Fetal anomalies–5.1
      Significant effects (P < .05).
      –3.7
      Significant effects (P < .05).
      –2
      Significant effects (P < .05).
      –0.80.8–5.1
      Significant effects (P < .05).
      –3.7
      Significant effects (P < .05).
      –2
      Significant effects (P < .05).
      –0.80.8
      Top panel shows EFW centiles at 30 wk (left) and 40 wk (right) of gestation for a female fetus of a nulliparous African American mother, having a height of 163 cm, weighing 64 kg at the first visit, nonsmoking, and without diabetes. Middle and bottom panels display the effects of nonpathologic and pathologic covariates, respectively. Effects are expressed as percentage change in weight. Positive values correspond to an increase while negative values correspond to a decrease in EFW centiles. For example, the effect of fetal sex (male vs female) is associated with about a 2% increase in EFW, and this effect is the same at 30 and 40 wk gestation and affects all centiles similarly. By contrast, maternal height has a stronger positive effect at higher EFW centiles, and this effect does not depend on gestation. Positive effect of additional 10 kg in maternal weight (with normal BMI range) is about twice as large at 40 wk as at 30 wk for all centiles.
      BMI, body mass index; EFW, estimated fetal weight.
      Tarca et al. Fetal growth chart for African American women. Am J Obstet Gynecol 2018.
      a Significant effects (P < .05).

      Fetal sex

      The EFW of male fetuses was about 2% higher than that of female fetuses, independent of all other factors listed in Table 2. This effect was similar among all centiles of the distribution that were evaluated (5th, 10th, 50th, 90th, and 95th). Since no interaction was found between fetal sex and gestational age, customization by fetal sex involves a proportional increase of the entire chart (all centile curves) by about 2% for male fetuses (Figure 1, A, and Table 2).

      Maternal height

      This covariate had a significant effect on all centiles of EFW, yet the effect was higher for the most extreme centiles. The 95th EFW centile increased by about 2% for each additional 10 cm of maternal height while the 5th centile increased by about 1%. The interaction between maternal height and gestational age was not significant; therefore, customization by maternal height involved a proportional shift of the EFW chart for taller mothers, with higher centile curves being shifted more than the lower centiles (Table 2).

      Maternal weight

      For women with a BMI between the 10th and 90th percentiles of the population, the effect of maternal weight on all centiles of EFW at 40 weeks was up to a 1.4% increase for each additional 10 kg in maternal weight. However, since the interaction between maternal weight and gestational age was significant for all centiles, the effect of maternal weight increased with gestational age, being about twice as high at 40 weeks as it was at 30 weeks of gestation (Figure 1, B, and Table 2).

      Parity

      Fetuses of parous women had a higher EFW than those of nulliparous women, although the magnitude of such an effect varied among centiles and changed with gestational age. For example, compared to nulliparous women, the 90th centile of EFW for women in their third pregnancy (parity = 2) was 4.1% higher at 40 weeks but only 2.4% higher at 30 weeks of gestation (Figure 1, C, and Table 2).
      Figure 1, D, illustrates the combined effect of change in multiple covariates on the normal growth chart of African American women. For example, at 40 weeks of gestation, the 90th centile of EFW for a male fetus of a mother in her third pregnancy (para = 2), who is 173 cm tall and weighs 74 kg, is 9% higher (4122 g) than for a female fetus of a nulliparous mother who is 10 cm shorter and weighs 10 kg less (3773 g).
      The effects of pathologic factors on EFW were higher than those of nonpathologic variables, and such effects also varied across gestation and among the centiles (Table 2). The effect of maternal complications that led to a preterm delivery was associated with a 12% reduction in the 5th centile of EFW at 30 weeks, and with a 5.3% and a 7.8% reduction at 40 weeks for women with a high BMI and those who smoked, respectively.
      The equations describing the PRB/NICHD customized chart are provided in Supplementary Table along with an example of the calculation of centiles. In addition, we provide a user-friendly spreadsheet calculator, available from the authors’ website (http://bioinformaticsprb.med.wayne.edu/). This tool allows: (1) interactive exploration of the effect of covariates on the growth chart; (2) obtaining the customized centile corresponding to an observed EFW value (determined from AC, HC, and FL measurements) for a given gestational age; and (3) printing of the entire customized chart for a given pregnancy.

      Comparison of fetal growth standards for classifying fetuses as SGA or LGA

      Our next objective was to determine how different fetal growth standards affect the classification of pregnancies as being at risk for either an SGA or LGA fetus. Therefore, we applied 4 different growth standards, including the PRB/NICHD standard developed herein, to classify fetuses of 4001 African American women based on the observed EFW at the last available ultrasound examination. The median gestational age at the last examination was 36.0 (IQR 33-38) weeks. We determined the overall proportions of fetuses that screened positive for SGA or LGA, but also separately for women with a term or a preterm delivery (Table 3).
      Table 3Percentage of unselected pregnancies predicted at risk of a small- (<10th) or large- (>90th) for-gestational-age neonate by different standards
      Fetuses classified as SGA <10th, %Fetuses classified as LGA >90th, %
      PretermTermAllPretermTermAll
      NICHD AA17.6 (14.7–20.8)5.3 (4.6–6.1)7.2 (6.4–8.1)13 (10.5–15.9)12.2 (11.2–13.4)12.3 (11.4–13.4)
      WHO24.2 (21–27.8)10 (9–11)12.2 (11.2–13.3)10.6 (8.4–13.3)10 (9–11.1)10.1 (9.2–11.1)
      WHO by sex24.5 (21.3–28.1)10.8 (9.8–11.9)13 (12–14.1)11.9 (9.5–14.7)10.6 (9.6–11.7)10.8 (9.8–11.8)
      GROW26.3 (22.9–29.9)9.7 (8.7–10.7)12.3 (11.3–13.4)7.8 (5.8–10.2)8.9 (7.9–9.9)8.7 (7.9–9.6)
      PRB/NICHD AA29 (25.5–32.7)11.6 (10.6–12.8)14.4 (13.3–15.5)8.9 (6.8–11.4)9.2 (8.3–10.3)9.2 (8.3–10.1)
      Standards compared are the NICHD AA, WHO with or without customization by fetal sex, customized GROW, and PRB/NICHD customized for AA women. Analysis of SGA and LGA is based on the last available scan of each pregnancy; median gestational age at the last scan is 36.0 (interquartile range 33-38) wk. Proportion (95% confidence intervals) of fetuses classified as SGA or LGA was determined for pregnancies delivered preterm (<37 wk), at term, and overall.
      AA, African American; GROW, gestation-related optimal weight; LGA, large for gestational age; NICHD, Eunice Kennedy Shriver National Institute of Child Health and Human Development/National Institutes of Health; PRB, Perinatology Research Branch; SGA, small for gestational age; WHO, World Health Organization.
      Tarca et al. Fetal growth chart for African American women. Am J Obstet Gynecol 2018.
      The percentage of fetuses classified as SGA (<10th centile) was as follows: (1) NICHD African American standard, 7.2%; (2) GROW standard, 12.3%; (3) WHO standard, 12.2% (13% if customized by fetal sex); and (4) PRB/NICHD standard, 14.4%. All fetal growth standards except the NICHD African American standard classified more SGA fetuses than the expected 10% cut-off.
      The proportion of fetuses classified as SGA was 2- to 3-fold higher among women who delivered preterm compared to those who delivered at term, depending upon the standard used. The rate of SGA among fetuses delivered preterm was as follows: NICHD African American standard, 17.6%; WHO standard, 24.2% (24.5% if customized by fetal sex); GROW standard, 26.3%; and PRB/NICHD standard, 29%.
      To illustrate the similarity among the 4 different standards, we constructed a Venn diagram to represent the number of fetuses classified as SGA by each combination of standards (Figure 2, A ). All fetuses identified as SGA by the NICHD African American standard were also identified by at least 2 other standards. Of note, the WHO standard classified 71 fetuses as SGA that were not identified as such by any other standard. The highest agreement among standards, as assessed by Cohen's kappa coefficient, occurred between the PRB/NICHD and GROW standards (kappa = 0.84), followed by the PRB/NICHD standard and the WHO standard customized by fetal sex (kappa = 0.79). On the other hand, the lowest agreement, although still substantial,
      • Landis J.R.
      • Koch G.G.
      The measurement of observer agreement for categorical data.
      was between the NICHD African American and PRB/NICHD standards (kappa = 0.63).
      Figure thumbnail gr2
      Figure 2Agreement among standards for small (SGA)- and large (LGA)-for-gestational-age screening
      Fetuses of African American (AA) women were classified as A, SGA (<10th) or B, LGA (>90th) based on the last available scan before delivery using 4 standards: the NICHD AA, the WHO customized by fetal sex, the customized GROW, and the customized PRB/NICHD AA. For SGA classification, the highest agreement among standards, as assessed by Cohen's kappa coefficient, occurred between PRB/NICHD AA and GROW (kappa = 0.84), followed by PRB/NICHD AA and WHO customized by fetal sex (kappa = 0.79), while the least agreement was indicated between NICHD AA and PRB/NICHD AA (kappa = 0.63). For LGA classification, the highest agreement among standards occurred between PRB/NICHD AA and GROW and also between WHO customized by fetal sex and NICHD AA (both pairs, kappa = 0.85).
      Tarca et al. Fetal growth chart for African American women. Am J Obstet Gynecol 2018.
      The percentage of fetuses classified as LGA was: (1) GROW, 8.7%; (2) PRB/NICHD customized, 9.2%; (3) WHO, 10.1% (10.8% if customized by fetal sex); and (4) NICHD African American standards, 12.3%. Of note, the LGA rates for the GROW and NICHD African American standards were significantly lower or higher than the expected 10% cut-off, respectively (Table 3).
      Unlike the rate of SGA, the rate of LGA was similar between fetuses delivered preterm or at term, for all fetal growth standards (Table 3).
      The agreement among the different standards for LGA classification can be visualized in the Venn diagram in Figure 2, B. The PRB/NICHD and GROW standards were in high agreement (kappa = 0.85), and the same was true for the WHO standard customized by fetal sex and NICHD African American standards (kappa = 0.85). Even the least similar pair of standards (NICHD African American and GROW) was still in substantial agreement for the LGA classification (kappa = 0.61).

      Comment

      The principal findings of the study are as follows. First, the birthweight of a term neonate is affected by maternal ethnicity, weight, height, and parity and fetal sex. Second, longitudinal fetal weight analysis revealed the following features of fetal growth: (1) all weight centiles were about 2% higher for male than for female fetuses; (2) maternal height had a positive effect on fetal weight, with larger fetuses being affected more (2% increase in the 95th centile of weight for each 10-cm increase in height); and (3) maternal weight and parity had positive effects on fetal weight that increased with gestation and varied among the weight centiles. Third, the rate of SGA was 7.2% for the NICHD African American standard, 12.3% for the GROW standard, 13% for the WHO standard customized by fetal sex, and 14.4% for the PRB/NICHD customized standard herein. For all standards, the proportion of SGA was at least 2-fold higher among fetuses delivered preterm than at term. Fourth, the rate of LGA was 8.7% for the GROW standard, 9.2% for the PRB/NICHD customized standard, 10.8% for the WHO standard customized by fetal sex, and 12.3% for the NICHD African American standard. Finally, the highest agreement among any 2 standards was between the GROW and PRB/NICHD standards for both SGA and LGA classifications (Cohen's interrater agreement kappa = 0.85).

      Factors affecting birthweight in term neonates

      We found that the mean birthweight of a female neonate born at 40 weeks to a reference African American mother (nulliparous, 163 cm tall, and weighing 64 kg) was 3223 g, which is similar to the 3226 g reported by Gardosi and Francis
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      in a US population. The effects of several nonpathologic and pathologic factors on birthweight were also similar between these 2 studies, such as 150 vs 132 g for fetal sex, 133 vs 161 g difference between Caucasian and African American women, and 247 vs 241 g for diabetes. Although consistent in terms of significance and direction of effect, the magnitude of effect of other covariates was somewhat lower in this study compared to those reported by Gardosi and Francis.
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      The negative effect of a high BMI (>90th centile) on birthweight in the current study was similar to the one reported by Gardosi and Francis
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      (40 vs 63.4 g), but it did not reach statistical significance. One reason for differences in the magnitude of effect for some covariates is that the US population in the study by Gardosi and Francis
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      was composed mostly of women of European origin, while this study was composed of mostly African American women.
      Ethnic differences in fetal biometric parameters were also recently assessed by other investigators for women with a low-risk pregnancy.
      • Buck Louis G.M.
      • Grewal J.
      • Albert P.S.
      • et al.
      Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
      The difference in mean birthweight between African American and Caucasian women at term in the study herein was about one-half (133 g) compared to that reported in the NICHD study (246 g).
      • Buck Louis G.M.
      • Grewal J.
      • Albert P.S.
      • et al.
      Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
      Possible explanations for this discrepancy are differences in population characteristics and the covariates accounted for in each analysis.

      One-size-fits-all vs customized fetal growth standards

      There is controversy as to whether a population-based or a customized chart should be used to screen fetuses as being at risk for SGA or LGA. SGA fetuses are at increased risk for fetal death and adverse neonatal outcomes (eg, cesarean delivery for nonreassuring fetal heart rate status, neonatal death, and admission to a neonatal intensive care unit).
      • Kase B.A.
      • Carreno C.A.
      • Blackwell S.C.
      Customized estimated fetal weight: a novel antenatal tool to diagnose abnormal fetal growth.
      • Serena C.
      • Marchetti G.
      • Rambaldi M.P.
      • et al.
      Stillbirth and fetal growth restriction.
      • Smith N.A.
      • Bukowski R.
      • Thomas A.M.
      • Cantonwine D.
      • Zera C.
      • Robinson J.N.
      Identification of pathologically small fetuses using customized, ultrasound and population-based growth norms.
      • Agarwal P.
      • Rajadurai V.S.
      • Yap F.
      • et al.
      Comparison of customized and cohort-based birthweight standards in identification of growth-restricted infants in GUSTO cohort study.
      • Moon M.
      • Baek M.J.
      • Ahn E.
      • Odibo A.O.
      Association between small for gestational age and intrauterine fetal death: comparing a customized South Korean growth standard versus a population-based fetal growth chart.
      Although other customization methods exist, such as the individualized growth assessment,
      • Deter R.L.
      Individualized growth assessment: evaluation of growth using each fetus as its own control.
      • Deter R.L.
      • Lee W.
      • Sangi-Haghpeykar H.
      • Tarca A.L.
      • Yeo L.
      • Romero R.
      Individualized fetal growth assessment: critical evaluation of key concepts in the specification of third trimester size trajectories.
      • Barker E.D.
      • McAuliffe F.M.
      • Alderdice F.
      • et al.
      The role of growth trajectories in classifying fetal growth restriction.
      the GROW approach of Gardosi et al
      • Gardosi J.
      • Mongelli M.
      • Wilcox M.
      • Chang A.
      An adjustable fetal weight standard.
      is the most widely adopted customized standard and has been applied to several populations, including a mostly Caucasian population in the United States.
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      • Figueras F.
      • Meler E.
      • Iraola A.
      • et al.
      Customized birthweight standards for a Spanish population.
      • Unterscheider J.
      • Geary M.P.
      • Daly S.
      • et al.
      The customized fetal growth potential: a standard for Ireland.
      The same authors reported that customization of fetal growth improved the detection of small fetuses at risk for fetal death and adverse neonatal outcomes, such as neonatal death and a low 5-minute Apgar score.
      • Clausson B.
      • Gardosi J.
      • Francis A.
      • Cnattingius S.
      Perinatal outcome in SGA births defined by customized versus population-based birthweight standards.
      However, previous comparisons between customized and population-based growth charts for the detection of fetuses at risk for adverse outcome yielded conflicting results.
      • Costantine M.M.
      • Mele L.
      • Landon M.B.
      • et al.
      Customized versus population approach for evaluation of fetal overgrowth.
      • Smith N.A.
      • Bukowski R.
      • Thomas A.M.
      • Cantonwine D.
      • Zera C.
      • Robinson J.N.
      Identification of pathologically small fetuses using customized, ultrasound and population-based growth norms.
      • Agarwal P.
      • Rajadurai V.S.
      • Yap F.
      • et al.
      Comparison of customized and cohort-based birthweight standards in identification of growth-restricted infants in GUSTO cohort study.
      • De Jong C.L.
      • Francis A.
      • Van Geijn H.P.
      • Gardosi J.
      Customized fetal weight limits for antenatal detection of fetal growth restriction.
      • Iraola A.
      • Gonzalez I.
      • Eixarch E.
      • et al.
      Prediction of adverse perinatal outcome at term in small-for-gestational age fetuses: comparison of growth velocity vs customized assessment.
      • Larkin J.C.
      • Hill L.M.
      • Speer P.D.
      • Simhan H.N.
      Risk of morbid perinatal outcomes in small-for-gestational-age pregnancies: customized compared with conventional standards of fetal growth.
      • Landres I.V.
      • Clark A.
      • Chasen S.T.
      Improving antenatal prediction of small-for-gestational-age neonates by using customized versus population-based reference standards.
      • Costantine M.M.
      • Lai Y.
      • Bloom S.L.
      • et al.
      Population versus customized fetal growth norms and adverse outcomes in an intrapartum cohort.
      • Gaillard R.
      • Jaddoe V.W.
      Assessment of fetal growth by customized growth charts.
      • Sjaarda L.A.
      • Albert P.S.
      • Mumford S.L.
      • Hinkle S.N.
      • Mendola P.
      • Laughon S.K.
      Customized large-for-gestational-age birthweight at term and the association with adverse perinatal outcomes.
      • Melamed N.
      • Ray J.G.
      • Shah P.S.
      • Berger H.
      • Kingdom J.C.
      Should we use customized fetal growth percentiles in urban Canada?.
      • Carberry A.E.
      • Gordon A.
      • Bond D.M.
      • Hyett J.
      • Raynes-Greenow C.H.
      • Jeffery H.E.
      Customized versus population-based growth charts as a screening tool for detecting small for gestational age infants in low-risk pregnant women.
      • Moussa H.N.
      • Wu Z.H.
      • Han Y.
      • et al.
      Customized versus population fetal growth norms and adverse outcomes associated with small for gestational age infants in a high-risk cohort.
      • White S.W.
      • Marsh J.A.
      • Lye S.J.
      • Briollais L.
      • Newnham J.P.
      • Pennell C.E.
      Improving customized fetal biometry by longitudinal modeling.
      • Ghi T.
      • Cariello L.
      • Rizzo L.
      • et al.
      Customized fetal growth charts for parents' characteristics, race, and parity by quantile regression analysis: a cross-sectional multicenter Italian study.
      • Iliodromiti S.
      • Mackay D.F.
      • Smith G.C.
      • et al.
      Customized and noncustomized birth weight centiles and prediction of stillbirth and infant mortality and morbidity: a cohort study of 979,912 term singleton pregnancies in Scotland.
      • Stock S.J.
      • Myers J.
      Defining abnormal fetal growth and perinatal risk: population or customized standards?.
      A recent meta-analysis
      • Chiossi G.
      • Pedroza C.
      • Costantine M.M.
      • Truong V.T.T.
      • Gargano G.
      • Saade G.R.
      Customized vs population-based growth charts to identify neonates at risk of adverse outcome: systematic review and Bayesian meta-analysis of observational studies.
      reported that the odds ratios of the association between adverse pregnancy outcomes (eg, perinatal mortality and neonatal intensive care unit admission) and abnormal birthweight were higher for the customized GROW standard compared to the noncustomized standards, although the difference was not statistically significant. Reaching a consensus regarding which type of fetal growth standards should be implemented in clinical care remains an important question, as it has a direct effect on patient management and care.

      Development of a customized fetal growth standard for the African American population

      Previous fetal growth standards were derived from fetal biometric data by excluding patients who developed complications during the current pregnancy
      • Buck Louis G.M.
      • Grewal J.
      • Albert P.S.
      • et al.
      Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
      and/or those with certain risk factors, such as an abnormal BMI, smoking, and adverse perinatal outcomes in previous pregnancies.
      • Kiserud T.
      • Piaggio G.
      • Carroli G.
      • et al.
      The World Health Organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight.
      • Buck Louis G.M.
      • Grewal J.
      • Albert P.S.
      • et al.
      Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
      Our approach was to adjust for the presence of pathology in the current pregnancy while assessing the effects of nonpathologic factors on fetal growth. The effects of pathologic variables included in the quantile regression models do not contribute to defining the normal fetal weight chart (eg, the chart will not be lowered because of a risk factor, eg, smoking), but the additional data from patients with pathologic factors increased the power to dissect the effect of nonpathologic covariates on fetal growth and helped to better calibrate the model so as to distinguish normal from abnormal growth.
      Of interest, all variables that had a significant effect on birthweight of neonates delivered at term (Table 1) also had a significant effect on EFW in the longitudinal analysis (Table 2 and Supplementary Table). This is important because it increases confidence that these variables are indeed needed to define the fetal growth potential, since birthweight data are more reliable than EFW data. In addition, although a high BMI (>90th centile) was not associated with a significant decrease in term birthweight (Table 2), it had a negative effect on the lower centiles of EFW. The 5th and 10th centiles of weight at 40 weeks were about 4.6% lower for women with a BMI >40.4; hence, this group of women are at higher risk of delivering an SGA neonate contrary to other observations.
      • Cnattingius S.
      • Bergstrom R.
      • Lipworth L.
      • Kramer M.S.
      Prepregnancy weight and the risk of adverse pregnancy outcomes.
      Similarly, although the negative effect of fetal anomalies on birthweight of neonates delivered at term was not significant, fetal anomalies were associated with up to a 5% reduction in the median, 10th centile, and 5th centile of EFW (Table 2).
      While our approach is conceptually similar to Gardosi et al,
      • Gardosi J.
      • Mongelli M.
      • Wilcox M.
      • Chang A.
      An adjustable fetal weight standard.
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      the customization parameters in our study were based directly on EFW data rather than on birthweight. Moreover, instead of assuming that each covariate has a proportionally constant effect on EFW at each gestational age, we tested for the first time and found significant interactions between parity as well as maternal weight and gestational age (Figure 1 and Table 2). Testing for these interactions would not have been feasible using cross-sectional birthweight data. Additionally, similar to the study by WHO,
      • Kiserud T.
      • Piaggio G.
      • Carroli G.
      • et al.
      The World Health Organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight.
      we used quantile regression to determine the effect of covariates on each centile of the distribution, rather than assessing the effect on mean fetal weight and assuming a normal distribution of weight around the mean value at each gestational age. Growth chart customization by differentially adjusting the centile curves according to the specific contribution and timing of each factor is novel. Such differences in both study design and analytical approach are reflected in our new customized fetal growth standard and impact the number of fetuses that will screen positive for SGA or LGA as well as who those fetuses are.

      SGA and LGA screening rates using different fetal growth standards

      The newly developed PRB/NICHD customized growth standard was compared to 3 existing standards: GROW,
      • Gardosi J.
      • Francis A.
      A customized standard to assess fetal growth in a US population.
      WHO with and without adjustment by fetal sex,
      • Kiserud T.
      • Piaggio G.
      • Carroli G.
      • et al.
      The World Health Organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight.
      and NICHD African American.
      • Buck Louis G.M.
      • Grewal J.
      • Albert P.S.
      • et al.
      Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
      A comparison to the INTERGROWTH-21st standard
      • Papageorghiou A.T.
      • Ohuma E.O.
      • Altman D.G.
      • et al.
      International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st project.
      was not performed due to differences in the ultrasound protocols that were previously noted
      • Tarca A.L.
      • Hernandez-Andrade E.
      • Ahn H.
      • et al.
      Single and serial fetal biometry to detect preterm and term small- and large-for-gestational-age neonates: a longitudinal cohort study.
      (eg, the BPD was measured from the outer to the outer, while we measured from the outer to the inner, borders of the parietal bones) and also due to the different EFW formula used in the INTERGROWTH-21st standard. Among the 4 standards compared in this study, there were significant differences in the fraction of fetuses classified as SGA (<10th centile) based on the last available ultrasound examination for each pregnancy. The proportion of fetuses that screened positive for SGA in a certain population is determined, in part, by the burden of pregnancy complications present in the population that are related to growth restriction. Indeed, since 15.8% of the African American women in the current study population delivered preterm, it is not surprising that most standards classified significantly more fetuses as SGA (<10th centile) than the expected 10%. For fetuses delivered preterm, the SGA screen-positive rate was significantly >10% for all standards, with the GROW and PRB/NICHD customized standards classifying 26.3% and 29% of the preterm population as being at risk, respectively. This finding is consistent with previous reports showing that SGA fetuses are at an increased risk of a preterm delivery.
      • Bukowski R.
      • Gahn D.
      • Denning J.
      • Saade G.
      Impairment of growth in fetuses destined to deliver preterm.
      • Hawkins L.K.
      • Schnettler W.T.
      • Modest A.M.
      • Hacker M.R.
      • Rodriguez D.
      Association of third-trimester abdominal circumference with provider-initiated preterm delivery.
      The rate of fetuses classified as SGA (<10th centile) in women who delivered at term was close to 10% for the WHO and GROW standards, and significantly >10% for the PRB/NICHD (11.6%) and lower for the NICHD African American (5.3%) standards. The fact that the 10th centile of the NICHD African American standard is low for our population, and hence it screens positive for fewer SGA fetuses than expected (7.2%), can also be understood from Supplementary Figure 3, where the 10th centile curve of the NICHD standard is lower than the corresponding centile of the local reference, especially after 37 weeks of gestation. However, the customized PRB/NICHD standard is built excluding the contribution of fetuses with risk factors associated with lower weight (eg, preterm delivery), thus the 10th centile of the PRB/NICHD standard is higher than the 10th centile of the local reference shown in Supplementary Figure 3, therefore classifying 14.4% of fetuses as SGA.
      Only the NICHD African American standard classified significantly more fetuses as LGA (12.3%) than the reference cut-off of 10%, which, combined with a lower than expected rate of SGA, suggests that this standard is low for our patient population. The GROW standard identified significantly less than expected (8.7%), while the PRB/NICHD standard identified 9.2% of fetuses as LGA. Since this was an unselected population, it is reasonable to assume that not all fetuses reached their growth potential; hence, standards classifying slightly less fetuses as LGA are actually tracking the growth potential of fetuses rather than being miscalibrated.
      In addition to comparing the SGA and LGA screening rates among the 4 growth standards, we provided complementary information regarding the agreement among the standards in terms of which fetuses are at risk. Using Venn diagrams (Figure 2) and interrater agreement statistics for all pairs of standards, we found that the 2 fully customized standards (GROW and PRB/NICHD) were the most similar, reaching an interrater agreement kappa of about 0.85 for both SGA and LGA classifications. Considering the multiple differences in the design of the 4 standards compared herein, such as the population on which they were based (homogenous vs multiethnic), the type of data they were derived from (birthweight vs fetal weight), the analytical assumptions they relied on, and the factors these standards were customized for (ethnicity or fetal sex only vs fully customized), this study suggests that customization by the same set of covariates is key for the reproducibility of growth assessment.

      Research and clinical implications

      This study confirms previous observations that maternal ethnicity, height, weight, and parity and fetal sex are factors affecting birthweight and/or fetal growth;
      • Abrams B.F.
      • Laros Jr., R.K.
      Prepregnancy weight, weight gain, and birth weight.
      • Zhang J.
      • Bowes Jr., W.A.
      Birth-weight-for-gestational-age patterns by race, sex, and parity in the United States population.
      • Ay L.
      • Kruithof C.J.
      • Bakker R.
      • et al.
      Maternal anthropometrics are associated with fetal size in different periods of pregnancy and at birth. The Generation R study.
      hence, they should be considered when defining fetal growth potential.
      • Cnattingius S.
      • Bergstrom R.
      • Lipworth L.
      • Kramer M.S.
      Prepregnancy weight and the risk of adverse pregnancy outcomes.
      • Gardosi J.
      Customized fetal growth standards: rationale and clinical application.
      Customization of growth charts is commonly performed by assuming a proportionally constant effect of covariates during gestation, and we found that, indeed, this assumption holds for genetically determined (fetal sex) or transmissible (height) traits. However, the effects of maternal weight and parity are proportionally graded with gestational age. Additionally, the effects of maternal height and parity in African American women were graded among the different centiles of EFW. The customization approach proposed herein can be applied to other populations as well, provided that ultrasound data and relevant covariate information are available. An easy-to-use implementation of the PRB/NICHD customized growth chart for African American women is freely available from the authors’ website (http://bioinformaticsprb.med.wayne.edu/).
      The higher similarity of the two fully customized standards compared to the similarity between the two partially customized standards suggests that the use of customized charts is more likely to lead to reproducible growth assessment across studies.
      Depending on the standard used, the rate of fetuses that screened positive for SGA can vary by a factor of 2 in a given population. The use of fully customized standards in high-risk populations may identify more fetuses as being at risk for growth restriction. However, comparing how the in utero SGA and LGA screening based on different standards relates to an SGA or LGA diagnosis at birth and to adverse pregnancy outcomes was outside the scope of the current study. Of note, the ability of ultrasound-based EFW to predict actual birthweight was described previously.
      • Hadlock F.P.
      • Harrist R.B.
      • Sharman R.S.
      • Deter R.L.
      • Park S.K.
      Estimation of fetal weight with the use of head, body, and femur measurements–a prospective study.
      • Sovio U.
      • White I.R.
      • Dacey A.
      • Pasupathy D.
      • Smith G.C.
      Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the pregnancy outcome prediction (POP) study: a prospective cohort study.
      • Tarca A.L.
      • Hernandez-Andrade E.
      • Ahn H.
      • et al.
      Single and serial fetal biometry to detect preterm and term small- and large-for-gestational-age neonates: a longitudinal cohort study.
      For example, in a blinded study conducted in a low-risk population, Sovio et al
      • Sovio U.
      • White I.R.
      • Dacey A.
      • Pasupathy D.
      • Smith G.C.
      Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the pregnancy outcome prediction (POP) study: a prospective cohort study.
      reported that an EFW <10th centile at 36 weeks of gestation correctly identified 57% of fetuses (sensitivity) that were destined to have a birthweight <10th centile, with a specificity of 95%. In their study, a noncustomized EFW standard was used for screening while the gold standard for SGA was based on a fetal sex-customized birthweight reference.
      • Sovio U.
      • White I.R.
      • Dacey A.
      • Pasupathy D.
      • Smith G.C.
      Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the pregnancy outcome prediction (POP) study: a prospective cohort study.

      Strengths and limitations

      We conducted the largest longitudinal fetal growth study in an African American population to date. Additional strengths of our study are that all patients were enrolled at a single ultrasound unit and that a consistent protocol was implemented to acquire ultrasound data. Moreover, the large sample size combined with advanced analytical approaches allowed the development of customized fetal growth centiles for an African American population under less-restrictive analytical assumptions than before. Although a possible limitation is that the ultrasound examinations studied herein were not scheduled at fixed gestational-age time points (as was the case for other fetal growth studies), the average number of scans (5) still compares favorably to previous reports.

      Conclusion

      We report herein the largest longitudinal fetal growth study of pregnant women self-reported as African American. We found that the effects of maternal weight and parity on EFW increase with gestational age and that maternal height and parity affect small or large fetuses differently. The PRB/NICHD customized growth chart was designed to account for these features of fetal growth. This standard classified more fetuses as being SGA (14.4%) than other standards, especially among fetuses delivered preterm. Moreover, this standard classified as LGA about the same fraction of fetuses as expected (10%). The comparison among the 4 growth standards considered herein revealed that the most important factor determining the agreement among standards is whether they account for the same factors known to affect fetal growth.

      Appendix

      Figure thumbnail fx1
      Supplementary Figure 1Patient selection flowchart
      Of the 4681 women enrolled in the study, only the most prevalent ethnic groups, African American (AA) (4239, 90.6%) and Caucasian (197, 4.2%), were considered for inclusion in the analysis. Patients were excluded if maternal age was unknown or if maternal age was <18 or >40 years. Patients were also excluded if they did not undergo an ultrasound scan between 14-40 weeks of gestation. Scans were discarded if they had outlier biometric values, resulting in a dataset of 4139 AA and 188 Caucasian women with at least 1 valid scan. An additional 144 women were excluded because of missing maternal weight, height, and parity, and fetal sex information, leading to a dataset of 4001 AA and 182 Caucasian women. Data from the 4001 AA women (20,663 ultrasound scans) were used to build the customized estimated fetal weight chart. The effect of covariates, including maternal race, on birthweight was assessed using data from the subset of 3368 AA and 152 Caucasian women with an available birthweight and who delivered at term.
      Tarca et al. Fetal growth charts for African American women. Am J Obstet Gynecol 2018.
      Figure thumbnail fx2
      Supplementary Figure 2Noncustomized fetal biometry and estimated fetal weight centiles in an unselected population of African American (AA) women
      Centiles (5th, 10th, 50th, 90th, and 95th) of fetal biometry, proportionality ratios, and estimated fetal weight determined using penalized quantile regression in an unselected population of AA women.
      Tarca et al. Fetal growth charts for African American women. Am J Obstet Gynecol 2018.
      Figure thumbnail fx3
      Supplementary Figure 3Comparison of the estimated fetal weight local reference to the NICHD African American (AA) and WHO standards
      Centiles (10th, 50th, and 90th) of estimated fetal weight derived from all 4001 women in this study (also shown in ) superimposed onto the same centiles of NICHD AA and WHO noncustomized standards.
      Tarca et al. Fetal growth charts for African American women. Am J Obstet Gynecol 2018.
      Supplementary TableLongitudinal analysis of estimated fetal weight
      VariableCoefficientP value
      5th10th50th90th95th5th10th50th90th95th
      Intercept7.9297.9778.1228.2368.269<.001<.001<.001<.001<.001
      t0.55490.54860.56520.52530.4912<.001<.001<.001<.001<.001
      t2–0.2506–0.2668–0.2643–0.2944–0.3280<.001<.001<.001<.001<.001
      t30.02840.02270.02050.01150.0021<.001<.001<.001.023.705
      Ht0.00890.00960.01190.01780.0184.006<.001<.001<.001<.001
      Wt0.01360.01370.01140.01230.0110<.001<.001<.001<.001<.001
      t × Wt0.00730.00720.00500.00590.0049<.001<.001<.001<.001.002
      Para 10.01070.02120.00710.02350.0073.367.002.154<.001.368
      Para 20.03350.03300.02180.04000.0295.002<.001<.001<.001.003
      Para ≥30.01090.00200.02380.03950.0368.402.849<.001<.001<.001
      t × Para 10.01010.01290.00180.01310.0075.212.012.636.008.237
      t × Para 20.01930.01390.01000.01610.0155.021.038.014.003.029
      t × Para ≥30.0008–0.00660.00870.01710.0194.937.38.05.008.01
      Sex (male)0.02300.01990.01840.01850.0239<.001<.001<.001<.001<.001
      BMI <20.50.01600.0081–0.0136–0.0207–0.0226.032.083<.001<.001<.001
      BMI >40.4–0.0545–0.0471–0.0332–0.01680.0018<.001<.001<.001.223.925
      Smoking (yes)–0.0807–0.0571–0.0321–0.0356–0.0242<.001<.001<.001<.001.007
      Diabetes0.06340.05430.04080.04470.0436<.001<.001<.001<.001.002
      Preterm delivery–0.1562–0.1262–0.0361–0.00650.0126<.001<.001<.001.448.158
      Fetal anomalies–0.0520–0.0376–0.0207–0.00760.0084.002.002.015.369.605
      t × BMI >40.4–0.0403–0.0343–0.0238–0.0143–0.0079<.001.001<.001.111.496
      t × Smoking–0.0441–0.0286–0.0111–0.01060.0007<.001<.001.002.055.919
      t2 × Diabetes–0.0104–0.0094–0.0072–0.0085–0.0132.024.02.013.006.003
      t2 × Preterm delivery0.02860.02300.00810.0038–0.0001<.001<.001<.001.208.979
      Quantile regression coefficients (left) and P values (right) for different centiles of log-estimated fetal weight. Customized estimated fetal weight centiles that exclude the effect of pathologies (PRB/NICHD standard) can be obtained by multiplying the coefficients of nonpathologic covariates (top panel) with corresponding predictors, summing terms, and exponentiating results. Coefficients for pathologic variables (extremely low or high BMI, smoking, diabetes, preterm delivery, and fetal anomalies) are also shown, but they are not to be used in predicting centiles. Analysis is centered at 40 wk of gestation for a female fetus of a nulliparous African American mother, having a height of 163 cm and weighing 64 kg at the first visit. t is the gestational age in wk from 40 wk scaled by 10. Ht is maternal height in cm from 163 cm scaled by 10. Wt is maternal weight in kg from 64 kg scaled by 10. For example, the 10th centile at 30 wk of gestation for a male fetus of an African American mother weighing 74 kg, 173 cm tall, and in her third pregnancy (parity = 2) can be calculated as: exp (7.977 + 0.5486 × t − 0.2668 × t2 + 0.0227 × t3 + 0.0096 × Ht + 0.0137 × Wt + 0.0072 × t × Wt + 0.033 × para2 + 0.0139 × t × para2 + 0.0199 × sex) = 1331 g, where: t = (30 − 40)/10 = −1; Ht = (173 − 163)/10 = 1; Wt = (74 − 64)/10 = 1; para2 = 1 and sex = 1.
      BMI, body mass index.
      Tarca et al. Fetal growth chart for African American women. Am J Obstet Gynecol 2018.

      References

        • Jeanty P.
        • Cantraine F.
        • Romero R.
        • Cousaert E.
        • Hobbins J.C.
        A longitudinal study of fetal weight growth.
        J Ultrasound Medicine. 1984; 3: 321-328
        • Lampl M.
        • Jeanty P.
        Timing is everything: a reconsideration of fetal growth velocity patterns identifies the importance of individual and sex differences.
        Am J Hum Biol. 2003; 15: 667-680
        • Tanner J.M.
        Fetus into man.
        1st ed. Harvard University Press, Cambridge (MA)1978
        • Bornstein M.H.
        • Arterberry M.E.
        • Lamb M.E.
        Development in infancy: a contemporary introduction.
        5th ed. Psychology Press, New York (NY)2014
        • Battaglia F.C.
        • Lubchenco L.O.
        A practical classification of newborn infants by weight and gestational age.
        J Pediatr. 1967; 71: 159-163
        • Gaccioli F.
        • Aye I.
        • Sovio U.
        • Charnock-Jones D.S.
        • Smith G.C.S.
        Screening for fetal growth restriction using fetal biometry combined with maternal biomarkers.
        Am J Obstet Gynecol. 2017;
        • Kalafat E.
        • Morales-Rosello J.
        • Thilaganathan B.
        • Tahera F.
        • Khalil A.
        Risk of operative delivery for intrapartum fetal compromise in small-for-gestational-age fetuses at term: an internally validated prediction model.
        Am J Obstet Gynecol. 2018; 218: 134.e1-134.e8
        • McEwen E.C.
        • Guthridge S.L.
        • He V.Y.
        • McKenzie J.W.
        • Boulton T.J.
        • Smith R.
        What birthweight percentile is associated with optimal perinatal mortality and childhood education outcomes?.
        Am J Obstet Gynecol. 2017;
        • Mendez-Figueroa H.
        • Truong V.T.
        • Pedroza C.
        • Khan A.M.
        • Chauhan S.P.
        Small-for-gestational-age infants among uncomplicated pregnancies at term: a secondary analysis of 9 Maternal-Fetal Medicine Units Network studies.
        Am J Obstet Gynecol. 2016; 215: 628.e1-628.e7
        • Costantine M.M.
        • Mele L.
        • Landon M.B.
        • et al.
        Customized versus population approach for evaluation of fetal overgrowth.
        Am J Perinatol. 2013; 30: 565-572
        • Anderson N.H.
        • Sadler L.C.
        • McKinlay C.J.
        • McCowan L.M.
        INTERGROWTH-21st vs customized birthweight standards for identification of perinatal mortality and morbidity.
        Am J Obstet Gynecol. 2016; 214: 509.e1-509.e7
        • Chauhan S.P.
        • Beydoun H.
        • Chang E.
        • et al.
        Prenatal detection of fetal growth restriction in newborns classified as small for gestational age: correlates and risk of neonatal morbidity.
        Am J Perinatol. 2014; 31: 187-194
        • Hadlock F.P.
        • Deter R.L.
        • Harrist R.B.
        • Park S.K.
        Estimating fetal age: computer-assisted analysis of multiple fetal growth parameters.
        Radiology. 1984; 152: 497-501
        • Hadlock F.P.
        • Harrist R.B.
        • Sharman R.S.
        • Deter R.L.
        • Park S.K.
        Estimation of fetal weight with the use of head, body, and femur measurements–a prospective study.
        Am J Obstet Gynecol. 1985; 151: 333-337
        • Villar J.
        • Knight H.E.
        • de Onis M.
        • et al.
        Conceptual issues related to the construction of prescriptive standards for the evaluation of postnatal growth of preterm infants.
        Arch Dis Childhood. 2010; 95: 1034-1038
        • Papageorghiou A.T.
        • Ohuma E.O.
        • Altman D.G.
        • et al.
        International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st project.
        Lancet. 2014; 384: 869-879
        • Gardosi J.
        • Chang A.
        • Kalyan B.
        • Sahota D.
        • Symonds E.M.
        Customized antenatal growth charts.
        Lancet. 1992; 339: 283-287
        • Gardosi J.
        • Mongelli M.
        • Wilcox M.
        • Chang A.
        An adjustable fetal weight standard.
        Ultrasound Obstet Gynecol. 1995; 6: 168-174
        • Gardosi J.
        • Francis A.
        A customized standard to assess fetal growth in a US population.
        Am J Obstet Gynecol. 2009; 201: 25.e1-25.e7
        • Kiserud T.
        • Piaggio G.
        • Carroli G.
        • et al.
        The World Health Organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight.
        PLoS Med. 2017; 14: e1002220
        • Buck Louis G.M.
        • Grewal J.
        • Albert P.S.
        • et al.
        Racial/ethnic standards for fetal growth: the NICHD fetal growth studies.
        Am J Obstet Gynecol. 2015; 213: 449.e1-449.e41
        • Hadlock F.P.
        • Harrist R.B.
        • Martinez-Poyer J.
        In utero analysis of fetal growth: a sonographic weight standard.
        Radiology. 1991; 181: 129-133
        • Vrachnis N.
        • Botsis D.
        • Iliodromiti Z.
        The fetus that is small for gestational age.
        Ann N Y Acad Sci. 2006; 1092: 304-309
        • Figueras F.
        • Meler E.
        • Iraola A.
        • et al.
        Customized birthweight standards for a Spanish population.
        Eur J Obstet Gynecol Reprod Biol. 2008; 136: 20-24
        • Unterscheider J.
        • Geary M.P.
        • Daly S.
        • et al.
        The customized fetal growth potential: a standard for Ireland.
        Eur J Obstet Gynecol Reprod Biol. 2013; 166: 14-17
        • Deter R.L.
        Individualized growth assessment: evaluation of growth using each fetus as its own control.
        Semin Perinatol. 2004; 28: 23-32
        • Deter R.L.
        • Lee W.
        • Sangi-Haghpeykar H.
        • Tarca A.L.
        • Yeo L.
        • Romero R.
        Individualized fetal growth assessment: critical evaluation of key concepts in the specification of third trimester size trajectories.
        J Matern Fetal Neonatal Med. 2014; 27: 543-551
        • Barker E.D.
        • McAuliffe F.M.
        • Alderdice F.
        • et al.
        The role of growth trajectories in classifying fetal growth restriction.
        Obstet Gynecol. 2013; 122: 248-254
        • Sovio U.
        • White I.R.
        • Dacey A.
        • Pasupathy D.
        • Smith G.C.
        Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the pregnancy outcome prediction (POP) study: a prospective cohort study.
        Lancet. 2015; 386: 2089-2097
        • Romero R.
        • Deter R.
        Should serial fetal biometry be used in all pregnancies?.
        Lancet. 2015; 386: 2038-2040
        • McCowan L.M.
        • Harding J.E.
        • Stewart A.W.
        Customized birthweight centiles predict SGA pregnancies with perinatal morbidity.
        BJOG. 2005; 112: 1026-1033
        • Johnsen S.L.
        • Rasmussen S.
        • Wilsgaard T.
        • Sollien R.
        • Kiserud T.
        Longitudinal reference ranges for estimated fetal weight.
        Acta Obstet Gynecol Scand. 2006; 85: 286-297
        • Odibo A.O.
        • Francis A.
        • Cahill A.G.
        • Macones G.A.
        • Crane J.P.
        • Gardosi J.
        Association between pregnancy complications and small-for-gestational-age birth weight defined by customized fetal growth standard versus a population-based standard.
        J Matern Fetal Neonatal Med. 2011; 24: 411-417
        • Cha H.H.
        • Kim J.Y.
        • Choi S.J.
        • Oh S.Y.
        • Roh C.R.
        • Kim J.H.
        Can a customized standard for large for gestational age identify women at risk of operative delivery and shoulder dystocia?.
        J Perinat Med. 2012; 40: 483-488
        • Kase B.A.
        • Carreno C.A.
        • Blackwell S.C.
        Customized estimated fetal weight: a novel antenatal tool to diagnose abnormal fetal growth.
        Am J Obstet Gynecol. 2012; 207: 218.e1-218.e5
        • Sovio U.
        • Smith G.C.S.
        The effect of customization and use of a fetal growth standard on the association between birthweight percentile and adverse perinatal outcome.
        Am J Obstet Gynecol. 2017;
        • Simcox L.E.
        • Myers J.E.
        • Cole T.J.
        • Johnstone E.D.
        Fractional fetal thigh volume in the prediction of normal and abnormal fetal growth during the third trimester of pregnancy.
        Am J Obstet Gynecol. 2017; 217: 453.e1-453.e12
        • Altman D.G.
        • Ohuma E.O.
        • International Fetal and Newborn Growth Consortium for the 21st Century
        Statistical considerations for the development of prescriptive fetal and newborn growth standards in the INTERGROWTH-21st project.
        BJOG. 2013; 120 (v): 71-76
        • Cheikh Ismail L.
        • Knight H.E.
        • Ohuma E.O.
        • Hoch L.
        • Chumlea W.C.
        Anthropometric standardization and quality control protocols for the construction of new, international, fetal and newborn growth standards: the INTERGROWTH-21st project.
        BJOG. 2013; 120 (v): 48-55
        • Villar J.
        • Altman D.G.
        • Purwar M.
        • et al.
        The objectives, design and implementation of the INTERGROWTH-21st project.
        BJOG. 2013; 120 (v): 9-26
        • Merialdi M.
        • Widmer M.
        • Gulmezoglu A.M.
        • et al.
        WHO multicenter study for the development of growth standards from fetal life to childhood: the fetal component.
        BMC Pregnancy Childbirth. 2014; 14: 157
        • Chitty L.S.
        • Altman D.G.
        • Henderson A.
        • Campbell S.
        Charts of fetal size: 4. Femur length.
        Br J Obstet Gynaecol. 1994; 101: 132-135
        • Chitty L.S.
        • Altman D.G.
        • Henderson A.
        • Campbell S.
        Charts of fetal size: 3. Abdominal measurements.
        Br J Obstet Gynaecol. 1994; 101: 125-131
        • Chitty L.S.
        • Altman D.G.
        • Henderson A.
        • Campbell S.
        Charts of fetal size: 2. Head measurements.
        Br J Obstet Gynaecol. 1994; 101: 35-43
        • Altman D.G.
        • Chitty L.S.
        Design and analysis of studies to derive charts of fetal size.
        Ultrasound Obstet Gynecol. 1993; 3: 378-384
        • Salomon L.J.
        • Alfirevic Z.
        • Berghella V.
        • et al.
        Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan.
        Ultrasound Obstet Gynecol. 2011; 37: 116-126
        • American Institute of Ultrasound in Medicine
        AIUM practice guideline for the performance of obstetric ultrasound examinations.
        J Ultrasound Med. 2013; 32: 1083-1101
        • Koenker R.
        Quantile regression for longitudinal data.
        J Multivar Anal. 2004; 91: 74-89
        • Koenker R.W.
        Quantile regression.
        Cambridge University Press, New York (NY)2005
        • Lee E.R.
        • Noh H.
        • Park B.U.
        Model selection via Bayesian information criterion for quantile regression models.
        J Am Stat Assoc. 2014; 109: 216-229
        • Landis J.R.
        • Koch G.G.
        The measurement of observer agreement for categorical data.
        Biometrics. 1977; 33: 159-174
        • Serena C.
        • Marchetti G.
        • Rambaldi M.P.
        • et al.
        Stillbirth and fetal growth restriction.
        J Matern Fetal Neonatal Med. 2013; 26: 16-20
        • Smith N.A.
        • Bukowski R.
        • Thomas A.M.
        • Cantonwine D.
        • Zera C.
        • Robinson J.N.
        Identification of pathologically small fetuses using customized, ultrasound and population-based growth norms.
        Ultrasound Obstet Gynecol. 2014; 44: 595-599
        • Agarwal P.
        • Rajadurai V.S.
        • Yap F.
        • et al.
        Comparison of customized and cohort-based birthweight standards in identification of growth-restricted infants in GUSTO cohort study.
        J Matern Fetal Neonatal Med. 2016; 29: 2519-2522
        • Moon M.
        • Baek M.J.
        • Ahn E.
        • Odibo A.O.
        Association between small for gestational age and intrauterine fetal death: comparing a customized South Korean growth standard versus a population-based fetal growth chart.
        J Matern Fetal Neonatal Med. 2016; 29: 872-874
        • Clausson B.
        • Gardosi J.
        • Francis A.
        • Cnattingius S.
        Perinatal outcome in SGA births defined by customized versus population-based birthweight standards.
        BJOG. 2001; 108: 830-834
        • De Jong C.L.
        • Francis A.
        • Van Geijn H.P.
        • Gardosi J.
        Customized fetal weight limits for antenatal detection of fetal growth restriction.
        Ultrasound Obstet Gynecol. 2000; 15: 36-40
        • Iraola A.
        • Gonzalez I.
        • Eixarch E.
        • et al.
        Prediction of adverse perinatal outcome at term in small-for-gestational age fetuses: comparison of growth velocity vs customized assessment.
        J Perinat Med. 2008; 36: 531-535
        • Larkin J.C.
        • Hill L.M.
        • Speer P.D.
        • Simhan H.N.
        Risk of morbid perinatal outcomes in small-for-gestational-age pregnancies: customized compared with conventional standards of fetal growth.
        Obstet Gynecol. 2012; 119: 21-27
        • Landres I.V.
        • Clark A.
        • Chasen S.T.
        Improving antenatal prediction of small-for-gestational-age neonates by using customized versus population-based reference standards.
        J Ultrasound Med. 2013; 32: 1581-1586
        • Costantine M.M.
        • Lai Y.
        • Bloom S.L.
        • et al.
        Population versus customized fetal growth norms and adverse outcomes in an intrapartum cohort.
        Am J Perinatol. 2013; 30: 335-341
        • Gaillard R.
        • Jaddoe V.W.
        Assessment of fetal growth by customized growth charts.
        Ann Nutr Metab. 2014; 65: 149-155
        • Sjaarda L.A.
        • Albert P.S.
        • Mumford S.L.
        • Hinkle S.N.
        • Mendola P.
        • Laughon S.K.
        Customized large-for-gestational-age birthweight at term and the association with adverse perinatal outcomes.
        Am J Obstet Gynecol. 2014; 210: 63.e1-63.e11
        • Melamed N.
        • Ray J.G.
        • Shah P.S.
        • Berger H.
        • Kingdom J.C.
        Should we use customized fetal growth percentiles in urban Canada?.
        J Obstet Gynaecol Can. 2014; 36: 164-170
        • Carberry A.E.
        • Gordon A.
        • Bond D.M.
        • Hyett J.
        • Raynes-Greenow C.H.
        • Jeffery H.E.
        Customized versus population-based growth charts as a screening tool for detecting small for gestational age infants in low-risk pregnant women.
        Cochrane Database Syst Rev. 2014; 5: CD008549
        • Moussa H.N.
        • Wu Z.H.
        • Han Y.
        • et al.
        Customized versus population fetal growth norms and adverse outcomes associated with small for gestational age infants in a high-risk cohort.
        Am J Perinatol. 2015; 32: 621-626
        • White S.W.
        • Marsh J.A.
        • Lye S.J.
        • Briollais L.
        • Newnham J.P.
        • Pennell C.E.
        Improving customized fetal biometry by longitudinal modeling.
        J Matern Fetal Neonatal Med. 2016; 29: 1888-1894
        • Ghi T.
        • Cariello L.
        • Rizzo L.
        • et al.
        Customized fetal growth charts for parents' characteristics, race, and parity by quantile regression analysis: a cross-sectional multicenter Italian study.
        J Ultrasound Med. 2016; 35: 83-92
        • Iliodromiti S.
        • Mackay D.F.
        • Smith G.C.
        • et al.
        Customized and noncustomized birth weight centiles and prediction of stillbirth and infant mortality and morbidity: a cohort study of 979,912 term singleton pregnancies in Scotland.
        PLoS Med. 2017; 14: e1002228
        • Stock S.J.
        • Myers J.
        Defining abnormal fetal growth and perinatal risk: population or customized standards?.
        PLoS Med. 2017; 14: e1002229
        • Chiossi G.
        • Pedroza C.
        • Costantine M.M.
        • Truong V.T.T.
        • Gargano G.
        • Saade G.R.
        Customized vs population-based growth charts to identify neonates at risk of adverse outcome: systematic review and Bayesian meta-analysis of observational studies.
        Ultrasound Obstet Gynecol. 2017; 50: 156-166
        • Cnattingius S.
        • Bergstrom R.
        • Lipworth L.
        • Kramer M.S.
        Prepregnancy weight and the risk of adverse pregnancy outcomes.
        N Engl J Med. 1998; 338: 147-152
        • Tarca A.L.
        • Hernandez-Andrade E.
        • Ahn H.
        • et al.
        Single and serial fetal biometry to detect preterm and term small- and large-for-gestational-age neonates: a longitudinal cohort study.
        PLoS One. 2016; 11: e0164161
        • Bukowski R.
        • Gahn D.
        • Denning J.
        • Saade G.
        Impairment of growth in fetuses destined to deliver preterm.
        Am J Obstet Gynecol. 2001; 185: 463-467
        • Hawkins L.K.
        • Schnettler W.T.
        • Modest A.M.
        • Hacker M.R.
        • Rodriguez D.
        Association of third-trimester abdominal circumference with provider-initiated preterm delivery.
        J Matern Fetal Neonatal Med. 2014; 27: 1228-1231
        • Abrams B.F.
        • Laros Jr., R.K.
        Prepregnancy weight, weight gain, and birth weight.
        Am J Obstet Gynecol. 1986; 154: 503-509
        • Zhang J.
        • Bowes Jr., W.A.
        Birth-weight-for-gestational-age patterns by race, sex, and parity in the United States population.
        Obstet Gynecol. 1995; 86: 200-208
        • Ay L.
        • Kruithof C.J.
        • Bakker R.
        • et al.
        Maternal anthropometrics are associated with fetal size in different periods of pregnancy and at birth. The Generation R study.
        BJOG. 2009; 116: 953-963
        • Gardosi J.
        Customized fetal growth standards: rationale and clinical application.
        Semin Perinatol. 2004; 28: 33-40