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Prediction of small for gestational age neonates: screening by maternal factors, fetal biometry, and biomarkers at 35–37 weeks’ gestation

Published:January 29, 2019DOI:https://doi.org/10.1016/j.ajog.2019.01.227

      Background

      Small for gestational age (SGA) neonates are at increased risk for perinatal mortality and morbidity; however, the risks can be substantially reduced if the condition is identified prenatally, because in such cases close monitoring and appropriate timing of delivery and prompt neonatal care can be undertaken. The traditional approach of identifying pregnancies with SGA fetuses is maternal abdominal palpation and serial measurements of symphysial–fundal height, but the detection rate of this approach is less than 30%. A higher performance of screening for SGA is achieved by sonographic fetal biometry during the third trimester; screening at 30–34 weeks’ gestation identifies about 80% of SGA neonates delivering preterm but only 50% of those delivering at term, at a screen-positive rate of 10%. There is some evidence that routine ultrasound examination at 36 weeks’ gestation is more effective than that at 32 weeks in predicting birth of SGA neonates.

      Objective

      To investigate the potential value of maternal characteristics and medical history, sonographically estimated fetal weight (EFW) and biomarkers of impaired placentation at 35+0– 36+6 weeks’ gestation in the prediction of delivery of SGA neonates.

      Materials and Methods

      A dataset of 19,209 singleton pregnancies undergoing screening at 35+0–36+6 weeks’ gestation was divided into a training set and a validation set. The training dataset was used to develop models from multivariable logistic regression analysis to determine whether the addition of uterine artery pulsatility index (UtA-PI), umbilical artery PI (UA-PI), fetal middle cerebral artery PI (MCA-PI), maternal serum placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFLT) would improve the performance of maternal factors and EFW in the prediction of delivery of SGA neonates. The models were then tested in the validation dataset to assess performance of screening.

      Results

      First, in the training dataset, in the SGA group, compared to those with birthweight in ≥10th percentile, the median multiple of the median (MoM) values of PlGF and MCA-PI were reduced, whereas UtA-PI, UA-PI, and sFLT were increased. Second, multivariable regression analysis demonstrated that in the prediction of SGA in <10th percentile there were significant contributions from maternal factors, EFW Z-score, UtA-PI MoM, MCA-PI MoM, and PlGF MoM. Third, in the validation dataset, prediction of 90% of SGA neonates delivering within 2 weeks of assessment was achieved by a screen-positive rate of 67% (95% confidence interval [CI], 64–70%) in screening by maternal factors, 23% (95% CI, 20–26%) by maternal factors, and EFW and 21% (95% CI, 19–24%) by the addition of biomarkers. Fourth, prediction of 90% of SGA neonates delivering at any stage after assessment was achieved by a screen-positive rate of 66% (95% CI, 65–67%) in screening by maternal factors, 32% (95% CI, 31–33%) by maternal factors and EFW and 30% (95% CI, 29–31%) by the addition of biomarkers.

      Conclusion

      The addition of biomarkers of impaired placentation only marginally improves the predictive performance for delivery of SGA neonates achieved by maternal factors and fetal biometry at 35+0–36+6 weeks’ gestation.

      Key words

      Small for gestational age (SGA) neonates are at increased risk for perinatal mortality and morbidity, but the risks can be substantially reduced if the condition is identified prenatally, because in such cases close monitoring and appropriate timing of delivery and prompt neonatal care can be undertaken.
      • Lindqvist P.G.
      • Molin J.
      Does antenatal identification of small-for-gestational age fetuses significantly improve their outcome?.
      • Gaccioli F.
      • Aye I.L.M.H.
      • Sovio U.
      • Charnock-Jones D.S.
      • Smith G.C.S.
      Screening for fetal growth restriction using fetal biometry combined with maternal biomarkers.
      The traditional approach of identifying pregnancies with SGA fetuses is maternal abdominal palpation and serial measurements of symphysial–fundal height, but the detection rate (DR) of this approach is less than 30%.
      • Bais J.M.J.
      • Eskes M.
      • Pel M.
      • Bonsel G.J.
      • Bleker O.P.
      Effectiveness of detection of intrauterine retardation by abdominal palpation as screening test in a low-risk population: an observational study.
      • Lindhard A.
      • Nielsen P.V.
      • Mouritsen L.A.
      • Zachariassen A.
      • Sørensen H.U.
      • Rosenø H.
      The implications of introducing the symphyseal-fundal height-measurement. A prospective randomized controlled trial.
      A few studies involving a small number of cases (725–3690) reported that a higher performance of screening for SGA is achieved by sonographic fetal biometry during the third trimester; in these studies, the DR varied from 54% to 75%, at a screen-positive rate of 10–25%.
      • Skovron M.L.
      • Berkowitz G.S.
      • Lapinski R.H.
      • Kim J.M.
      • Chitkara U.
      Evaluation of early third-trimester ultrasound screening for intrauterine growth retardation.
      • David C.
      • Tagliavini G.
      • Pilu G.
      • Rudenholz A.
      • Bovicelli L.
      Receiver-operator characteristic curves for the ultrasonographic prediction of small-for-gestational-age fetuses in low-risk pregnancies.
      • De Reu P.A.
      • Smits L.J.
      • Oosterbaan H.P.
      • Nijhuis J.G.
      Value of a single early third trimester fetal biometry for the prediction of birthweight deviations in a low risk population.
      • Di Lorenzo G.
      • Monasta L.
      • Ceccarello M.
      • Cecotti V.
      • D'Ottavio G.
      Third trimester abdominal circumference, estimated fetal weight and uterine artery Doppler for the identification of newborns small and large for gestational age.
      • Souka A.P.
      • Papastefanou I.
      • Pilalis A.
      • Michalitsi V.
      • Kassanos D.
      Performance of third-trimester ultrasound for prediction of small-for-gestational-age neonates and evaluation of contingency screening policies.
      • Souka A.P.
      • Papastefanou I.
      • Pilalis A.
      • Michalitsi V.
      • Panagopoulos P.
      • Kassanos D.
      Performance of the ultrasound examination in the early and late third trimester for the prediction of birthweight deviations.
      • Fadigas C.
      • Saiid Y.
      • Gonzalez R.
      • Poon L.C.
      • Nicolaides K.H.
      Prediction of small for gestational age neonates: screening by fetal biometry at 35-37 weeks.
      A prospective study at 30–34 weeks’ gestation in 30,849 singleton pregnancies found that screening by a combination of maternal characteristics and history with sonographic estimated fetal weight (EFW) predicted 80% of SGA neonates with birthweight <10th percentile delivering at <5 weeks of assessment, at a 10% screen-positive rate; the respective DR for prediction of SGA neonates delivering at ≥5 weeks of assessment was 52%.
      • Bakalis S.
      • Silva M.
      • Akolekar R.
      • Poon L.C.
      • Nicolaides K.H.
      Prediction of small for gestational age neonates: screening by fetal biometry at 30–34 weeks.
      A subsequent study of 9472 singleton pregnancies at 30−34 weeks reported that the performance of screening by maternal factors and EFW was improved by the addition of uterine artery pulsatility index (UtA-PI), mean arterial pressure (MAP), and serum placental growth factor (PlGF); the DR of SGA <10th percentile, at a 10% screen-positive rate, was 89% for those delivering at <37 weeks' gestation but only 57% for those delivering at ≥37 weeks.
      • Bakalis S.
      • Peeva G.
      • Gonzalez R.
      • Poon L.C.
      • Nicolaides K.H.
      Prediction of small-for-gestational-age neonates: screening by biophysical and biochemical markers at 30-34 weeks.
      Consequently, the performance of screening for SGA at 30 (725−3690)34 weeks is acceptably high for those delivering preterm, but disappointingly low for those delivering at term.

      Why was this study conducted?

      • To investigate the potential value of maternal characteristics and medical history, sonographically estimated fetal weight (EFW), and biomarkers of impaired placentation at 35+0− 36+6 weeks’ gestation in the prediction of delivery of small for gestational age (SGA) neonates.

      Key findings

      • Prediction of 90% of SGA neonates delivering within 2 weeks of assessment was achieved by a screen-positive rate of 67% in screening by maternal factors, 23% by maternal factors and EFW, and 21% by the addition of biomarkers; the respective values for prediction of SGA neonates delivering at any stage after assessment were 66%, 32%, and 30%.

      What does this add to what is known?

      • Addition of biomarkers of impaired placentation only marginally improves the predictive performance of small for gestational age neonates achieved by maternal factors and fetal biometry at 35+0–36+6 weeks’ gestation.
      A randomized trial in 2586 low-risk pregnancies reported that routine ultrasound examination at 36 weeks’ gestation is more effective than that at 32 weeks in detecting SGA neonates and related adverse perinatal and neonatal outcomes.
      • Roma E.
      • Arnau A.
      • Berdala R.
      • Bergos C.
      • Montesinos J.
      • Figueras F.
      Ultrasound screening for fetal growth restriction at 36 vs 32 weeks' gestation: a randomized trial (ROUTE).
      A few studies examined the performance of screening for SGA at 35−37 weeks’ gestation by a combination of EFW and biomarkers. A study of 5121 pregnancies reported that in screening by maternal factors and EFW the DR of SGA <10th percentile delivering at ≥37 weeks was 66%, at a 10% screen-positive rate, and this performance was not improved by the addition of UtA-PI and MAP.
      • Fadigas C.
      • Guerra L.
      • Garcia-Tizon Larroca S.
      • Poon L.C.
      • Nicolaides K.H.
      Prediction of small-for-gestational-age neonates: screening by uterine artery Doppler and mean arterial pressure at 35-37 weeks.
      Similarly, a study of 946 pregnancies reported that screening by EFW predicted 59% of SGA <10th percentile, at a 10% screen-positive rate, and the performance was not improved by the addition of UtA-PI and the cerebroplacental ratio (CPR).
      • Triunfo S.
      • Crispi F.
      • Gratacos E.
      • Figueras F.
      Prediction of delivery of small-for-gestational-age neonates and adverse perinatal outcome by fetoplacental Doppler at 37 weeks' gestation.
      A study of 3859 pregnancies reported that in screening by maternal factors and EFW the DR of SGA <10th percentile delivering at ≥37 weeks was not improved by the addition of PlGF and soluble fms-like tyrosine kinase-1 (sFLT).
      • Fadigas C.
      • Peeva G.
      • Mendez O.
      • Poon L.C.
      • Nicolaides K.H.
      Prediction of small-for-gestational-age neonates: screening by placental growth factor and soluble fms-like tyrosine kinase-1 at 35-37 weeks.
      The objective of this study in 19,208 singleton pregnancies undergoing routine antenatal assessment at 35+0−36+6 weeks’ gestation is to investigate further the potential value of maternal factors, EFW, and biomarkers of impaired placentation in the prediction of delivery of SGA neonates.

      Materials and Methods

      Two datasets were used for this study. The first dataset comprised 124,443 singleton pregnancies undergoing routine ultrasound examination at 11+0−13+6 weeks’ gestation at King’s College Hospital, London or Medway Maritime Hospital, Gillingham, UK, between March 2006 and December 2016. This dataset was used to derive the patient-specific prior risk for delivery of SGA neonates from maternal characteristics and medical history. The second dataset was derived from a prospective observational study in 19,209 women with singleton pregnancies attending for a routine hospital visit at 35+0−36+6 weeks’ gestation at King’s College Hospital, London, or Medway Maritime Hospital, Gillingham, UK, between March 2014 and September 2018. This visit included recording of maternal demographic characteristics and medical history, ultrasound examination for fetal anatomy and measurement of fetal head circumference, abdominal circumference and femur length for calculation of EFW (using the formula by Hadlock et al,
      • Hadlock F.P.
      • Harrist R.B.
      • Martinez-Poyer J.
      In utero analysis of fetal growth: a sonographic weight standard.
      because a systematic review identified this as being the most accurate model
      • Hammami A.
      • Mazer Zumaeta A.
      • Syngelaki A.
      • Akolekar R.
      • Nicolaides K.H.
      Ultrasonographic estimation of fetal weight: development of new model and assessment of performance of previous models.
      ), transabdominal color Doppler ultrasound for measurement of the mean UtA-PI, UA-PI, and MCA-PI,
      • Albaiges G.
      • Missfelder-Lobos H.
      • Lees C.
      • Parra M.
      • Nicolaides K.H.
      One-stage screening for pregnancy complications by color Doppler assessment of the uterine arteries at 23 weeks’ gestation.
      • Ciobanu A.
      • Wright A.
      • Syngelaki A.
      • Wright D.
      • Akolekar R.
      • Nicolaides K.H.
      Fetal Medicine Foundation reference ranges for umbilical artery and middle cerebral artery pulsatility index and cerebroplacental ratio.
      measurement of MAP by validated automated devices and a standardized protocol,
      • Poon L.C.
      • Zymeri N.A.
      • Zamprakou A.
      • Syngelaki A.
      • Nicolaides K.H.
      Protocol for measurement of mean arterial pressure at 11-13 weeks' gestation.
      and measurement of serum concentration of PlGF and sFLT by an automated biochemical analyzer (Cobas e411 system, Roche Diagnostics, Penzberg, Germany, or BRAHMS KRYPTOR compact PLUS, Thermo Fisher Scientific, Hennigsdorf, Germany). Gestational age was determined by the measurement of fetal crown−rump length at 11−13 weeks or the fetal head circumference at 19−24 weeks.
      • Robinson H.P.
      • Fleming J.E.
      A critical evaluation of sonar crown-rump length measurements.
      • Snijders R.J.
      • Nicolaides K.H.
      Fetal biometry at 14-40 weeks’ gestation.
      The women gave written informed consent to participate in the study, which was approved by the NHS Research Ethics Committee. The inclusion criteria for this study were singleton pregnancies examined at 35+0−36+6 weeks’ gestation and delivering a non-malformed live birth or stillbirth. We excluded pregnancies with aneuploidies and major fetal abnormalities.

      Patient Characteristics

      Patient characteristics recorded included maternal age, racial origin (white, black, South Asian, East Asian, and mixed), method of conception (natural, in vitro fertilization or use of ovulation induction drugs), cigarette smoking during pregnancy, medical history of chronic hypertension and diabetes mellitus, obstetric history including parity (parous or nulliparous if no previous pregnancies at ≥24 weeks’ gestation), and previous pregnancy with SGA. Maternal weight and height were measured.

      Sample Analyses

      In the Cobas e411 of Roche Diagnostics, the interassay coefficients of variation for the low and high concentrations were 5.4% and 3.0% for PlGF, and 3.0% and 3.2% for sFlt-1, respectively; assays cover a measurement range from 3 to 10,000 pg/mL for PlGF and from 10 to 85,000 pg/mL for sFLT. In the BRAHMS KRYPTOR compact PLUS of Thermo Fisher Scientific, the interassay coefficients of variation for the low and high concentrations were 22% and 5% for PlGF, and 5% and 5% for sFLT, respectively; assays cover a measurement range from 3.6 to 7000 pg/mL for PLGF and from 22 to 90,000 pg/mL for sFLT.

      Outcome Measures

      Data on pregnancy outcome were collected from the hospital maternity records or the general medical practitioners of the women. The outcome measures of the study were birth of a neonate with birthweight <10th or <3rd percentile for gestational age at delivery, based on the Fetal Medicine Foundation fetal and neonatal population weight charts.
      • Nicolaides K.H.
      • Wright D.
      • Syngelaki A.
      • Wright A.
      • Akolekar R.
      Fetal Medicine Foundation fetal and neonatal population weight charts.

      Statistical Analysis

      Data were expressed as median (interquartile range [IQR]) for continuous variables and n (%) for categorical variables. The Mann−Whitney U test and χ2 test or Fisher exact test were used for comparing outcome groups for continuous and categorical data, respectively. Significance was assumed at 5%.
      The a priori risk for SGA based on maternal factors was derived in the dataset of 124,443 singleton pregnancies at 11+0−13+6 weeks’ gestation using multivariable logistic regression analysis with backward stepwise elimination to determine which of the factors among maternal characteristics and medical and obstetric history had a significant contribution in predicting SGA <10th percentile. Prior to the regression analysis, the continuous variables, such as age, weight, and height were centered by subtracting the arithmetic mean from each value. Multiple categorical variables were dummy coded as binary variables to estimate the independent effect of each category.
      In the dataset of 19,209 singleton pregnancies examined at 35+0−36+6 weeks’ gestation, the observed measurements of EFW were expressed as Z scores for gestational age.
      • Nicolaides K.H.
      • Wright D.
      • Syngelaki A.
      • Wright A.
      • Akolekar R.
      Fetal Medicine Foundation fetal and neonatal population weight charts.
      The measurements of UA-PI, MCA-PI, UtA-PI, MAP, PlGF, and sFLT were converted to multiple of the normal median (MoM).
      • Poon L.C.
      • Zymeri N.A.
      • Zamprakou A.
      • Syngelaki A.
      • Nicolaides K.H.
      Protocol for measurement of mean arterial pressure at 11-13 weeks' gestation.
      • Panaitescu A.
      • Ciobanu A.
      • Syngelaki A.
      • Wright A.
      • Wright D.
      • Nicolaides K.H.
      Screening for pre-eclampsia at 35-37 weeks’ gestation.
      The dataset of 19,209 pregnancies was randomly divided into 2 separate datasets for development and validation of prediction models. Multivariable logistic regression analysis was then used in the training dataset to determine whether the maternal factor−derived logit (prior risk), EFW, UA-PI and MCA-PI, UtA-PI, MAP, PlGF, and sFLT had a significant contribution in predicting SGA <10th and SGA <3rd percentiles delivering within 2 weeks and at any stage after assessment. The performance of screening was determined by receiver operating characteristic (ROC) curves. The models developed from the multivariate analysis in the training dataset were then tested on the validation dataset to determine the performance of screening by analysis of ROC curves for various combinations of biomarkers in addition to maternal factors and EFW.
      The statistical software package SPSS 24.0 (IBM SPSS Statistics for Windows, Version 24.0; IBM Corp., Armonk, NY) and Medcalc (Medcalc Software, Mariakerke, Belgium) were used for data analyses.

      Results

      Patient Characteristics

      The characteristics of the study population of 124,443 pregnancies examined at 11−13 weeks’ gestation for establishment of the prior risk and the 19,209 examined at 35+0−36+6 weeks, divided into training and validation datasets, are shown in Tables 1 and 2, respectively. In the validation dataset of 9605 pregnancies 1097 (11.4%) delivered within 2 weeks of assessment.
      Table 1Characteristics of the study population at 11+0 –13+6 weeks’ gestation for estimation of prior risk
      CharacteristicBW ≥10th percentile (n = 108,802)SGA <10th percentile (n = 15,641)P value
      Maternal age, y, median (IQR)31.2 (26.7–35.1)30.3 (25.3–34.7)<.001
      Maternal weight, kg, median (IQR)67.0 (60.0–78.0)63.0 (56.0–73.0)<.001
      Maternal height, cm, median (IQR)165 (160–169)162 (157–167)<.001
      Gestation at screening, days, median (IQR)89 (86–92)89 (86–91)<.001
      Racial origin
       White, n (%)83926 (77.1)10028 (64.1)<.001
       Black, n (%)16177 (14.9)3522 (22.5)<.001
       South Asian, n (%)4060 (3.7)1237 (7.9)<.001
       East Asian, n (%)2074 (1.9)380 (2.4)<.001
       Mixed, n (%)2565 (2.4)474 (3.0)<.001
      Cigarette smoker, n (%)9820 (9.0)2752 (17.6)<.001
      Conception
       Natural, n (%)105245 (96.7)15057 (96.3)
       Ovulation drugs, n (%)1285 (1.2)207 (1.3).126
       In vitro fertilization, n (%)2272 (2.1)377 (2.4).009
      Medical conditions
       Chronic hypertension, n (%)1205 (1.1)374 (2.4)<.001
       Diabetes mellitus type 1, n (%)479 (0.4)41 (0.3).001
       Diabetes mellitus type 2, n (%)467 (0.4)88 (0.6).011
      Past obstetric history
       Nulliparous, n (%)49537 (45.5)8955 (57.3)
       Parous with prior SGA, n (%)10973 (10.1)3039 (19.4)<.001
       Parous without prior SGA, n (%)48292 (44.4)3647 (23.3)<.001
      Gestational age at delivery, wk, median (IQR)40.1 (39.0–40.9)39.4 (38.1–40.5)<.001
      BW, birthweight; IQR, interquartile range; SGA, small for gestational age.
      Ciobanu et al. Third-trimester screening for SGA. Am J Obstet Gynecol 2019.
      Table 2Characteristics of the study population at 35+0–36+6 weeks’ gestation
      CharacteristicTraining datasetValidation dataset
      BW ≥10th percentile (n = 8592)SGA <10th percentile (n = 1012)P valueBW ≥10th percentile (n = 8593)SGA <10th percentile (n = 1012)P value
      Maternal age, y, median (IQR)32.2 (28.1–35.7)31.7 (27.2–35.4)<.00132.2 (28.1–35.7)31.3 (26.6–35.2)<.001
      Maternal weight, kg, median (IQR)79.8 (71.4–90.4)74.0 (66.0–84.0)<.00179.5 (71.6–90.0)73.0 (65.7–82.4)<.001
      Maternal height, cm, median (IQR)165 (161–170)163 (159–167)<.001165 (161–170)163 (158–167)<.001
      Gestational age at screening, wk, median (IQR)36.1 (35.9–36.4)36.1 (35.9–36.4).65436.1 (35.9–36.4)36.1 (35.9–36.4).096
      Racial origin
       White, n (%)6838 (79.6)690 (68.2)6846 (79.7)671 (66.3)
       Black, n (%)976 (11.4)180 (17.8)<.0011023 (11.9)187 (18.5)<.001
       South Asian, n (%)338 (3.9)87 (8.6)<.001310 (3.6)92 (9.1)<.001
       East Asian, n (%)177 (2.1)25 (2.5).390173 (2.0)26 (2.6).240
       Mixed, n (%)263 (3.1)30 (3.0).866241 (2.8)36 (3.6).176
      Cigarette smoker, n (%)527 (6.1)125 (12.4)<.001535 (6.2)135 (13.3)<.001
      Conception
       Natural, n (%)8290 (96.5)971 (95.9)8303 (96.6)970 (95.8)
       Ovulation drugs, n (%)49 (0.6)6 (0.6).92848 (0.6)7 (0.7).359
       In vitro fertilization, n (%)302 (3.5)41 (4.1).384290 (3.4)42 (4.2).202
      Medical conditions
       Chronic hypertension, n (%)85 (1.0)16 (1.6).08194 (1.1)14 (1.4).409
       Diabetes mellitus type 1, n (%)34 (0.4)0.02334 (0.4)2 (0.2).253
       Diabetes mellitus type 2, n (%)63 (0.7)4 (0.4).15257 (0.7)3 (0.3).110
      Past obstetric history
       Nulliparous, n (%)3915 (45.6)589 (58.2)3916 (45.6)590 (58.3)
       Parous without prior SGA, n (%)4223 (49.2)271 (26.8)<.0014221 (49.1)270 (26.7)<.001
       Parous with prior SGA, n (%)454 (5.3)152 (15.0)<.001456 (5.3)152 (15.0)<.001
      Estimated fetal weight, percentile, median (IQR)59.2 (35.9–79.4)12.2 (3.9–27.6)<.00158.8 (35.4–79.2)13.2 (3.9–27.5)<.001
      Uterine artery PI MoM, median (IQR)0.98 (0.84–1.16)1.04 (0.86–1.28)<.0010.98 (0.84–1.15)1.07 (0.89–1.29)<.001
      Umbilical artery PI MoM, median (IQR)1.01 (0.90–1.13)1.08 (0.96–1.20)<.0011.01 (0.91–1.13)1.08 (0.96–1.21)<.001
      Middle cerebral artery PI MoM, median (IQR)0.99 (0.89–1.09)0.96 (0.86–1.08)<.0010.99 (0.89–1.11)0.95 (0.86–1.08)<.001
      Placental growth factor MoM, median (IQR)1.03 (0.58–1.84)0.63 (0.35–1.24)<.0011.04 (0.58–1.85)0.65 (0.36–1.24)<.001
      sFLT MoM, median (IQR)0.96 (0.70–1.37)1.03 (0.71–1.66)<.0010.96 (0.69–1.37)1.05 (0.72–1.68)<.001
      Gestational age at delivery in weeks, median (IQR)40.0 (39.1–40.9)39.4 (38.4–40.4)<.00140.0 (39.1–40.9)39.4 (38.4–40.4)<.001
      Birthweight in percentile, median (IQR)55.7 (33.1–77.5)4.5 (1.9–7.0)<.00155.5 (33.2–77.6)4.6 (1.9–7.0)<.001
      BW, birthweight; IQR, interquartile range; SGA, small for gestational age; MoM, multiple of the median; PI, pulsatility index; sFLT, soluble fms-like tyrosine kinase-1.
      Comparisons between normals and SGA: χ2 test or Fisher exact test for categorical variables, and Mann–Whitney U test or Student t test: P < .05.
      Ciobanu et al. Third-trimester screening for SGA. Am J Obstet Gynecol 2019.
      In the 124,443 pregnancies examined at 11−13 weeks’ gestation, the birthweight was <10th percentile in 15,641 (12.6%). The distribution of SGA <10th percentile that delivered at <32, 32−36 and at ≥37 weeks’ gestation was 3.6% (n = 559), 11.5% (n = 1803), and 84.9% (n = 13,279), respectively.

      Prior Risk for SGA

      The prior risk for SGA <10th percentile is calculated from the following formula: odds/(1+odds), where odds = eY and Y is derived from multivariable logistic regression analysis. Regression coefficients and adjusted odds ratios of each of the maternal factors in the prediction algorithms are presented in Table 3. The likelihood of SGA increased with maternal age and decreased with maternal weight and height. The risk was higher in women of black, South Asian, East Asian, and mixed racial origins than in white women, cigarette smokers, those with chronic hypertension, those with diabetes mellitus type 2, and parous women with a prior history of SGA. The risk was lower in parous women without a prior history of SGA and in those with diabetes mellitus type 1.
      Table 3Fitted regression model with maternal characteristics and history for the prediction of small for gestational age neonates with birthweight below the 10th percentile
      CharacteristicUnivariableMultivariable
      OR (95% CI)P valueOR (95% CI)P value
      Maternal age–30 y0.98 (0.97–0.98)<.0011.01 (1.00–1.01)<.001
      Maternal weight–70 kg0.98 (0.97–0.98)<.0010.98 (0.98–0.99)<.001
      Maternal height–164 (cm)0.94 (0.94–0.95)<.0010.96 (0.96–0.97)<.001
      Racial origin
       White (reference)1.00
       Black1.82 (1.75–1.90)<.0012.16 (2.07–2.26)<.001
       South Asian2.55 (2.39–2.72)<.0012.00 (1.87–2.15)<.001
       East Asian1.53 (1.37–1.71)<.0011.15 (1.02–1.29).021
       Mixed1.55 (1.40–1.71)<.0011.45 (1.31–1.61)<.001
      Conception
       Natural (Reference)1.001.00
       Ovulation induction drugs1.13 (0.97–1.31).1161.22 (1.05–1.43).010
       In vitro fertilization1.16 (1.04–1.30).0081.17 (1.05–1.32).007
      Cigarette smoker2.15 (2.06–2.25)<.0012.59 (2.47–2.72)<.001
      Medical disorders
       Chronic hypertension2.19 (1.95–2.46)<.0012.39 (2.11–2.72)<.001
       Diabetes mellitus type 10.60 (0.43–0.82).0010.62 (0.45–0.86).004
       Diabetes mellitus type 21.31 (1.04–1.65).0201.35 (1.06–1.71).017
      Past obstetric history
       Nulliparous (Reference)1.001.00
       Parous with no prior SGA, n (%)0.42 (0.40–0.44)<.0010.40 (0.39–0.42)<.001
       Parous with prior SGA, n (%)1.53 (1.46–1.60)<.0011.23 (1.17–1.29)<.001
      OR, odds ratio; CI, confidence interval; SGA, small for gestational age.
      Y = –2.05847 + (0.00664*Age) + (–0.01585*Weight) + (–0.04113*Height) + (0.77099*Black) + (0.69489*South Asian) + (0.13596*East Asian) + (0.36953*Mixed race) + (0.20161*Ovulation drugs) + (0.15918*IVF conception) + (0.95299*Smoking) + (0.87258*Chronic hypertension) + (–0.47573*Diabetes type 1) + (0.29632*Diabetes type 2) + (–0.90660*Parous no previous SGA) + (0.20848*Parous previous SGA).
      Ciobanu et al. Third-trimester screening for SGA. Am J Obstet Gynecol 2019.

      Biomarkers

      In the SGA <10th percentile group, compared to those with birthweight ≥10th percentile, the median MoM values of PlGF (0.65 vs 1.04; P < .001) and MCA-PI (0.96 vs. 0.99; P < .001) were lower, whereas UtA-PI (1.06 vs 0.98; P < .001), UA-PI (1.08 vs 1.01; P < .001) and sFLT (1.04 vs 0.96; P < .001) were higher. The deviations of biomarkers from normal were more pronounced in those with birthweight in the 3rd percentile than in the 10th percentile (P < .001). In the SGA <10th percentile group, the deviation in biomarker levels from normal decreased with increasing interval between assessment and delivery (EFW Z score r = 0.087, P < .001; UtA-PI: r = −0.110, P < .001; MAP: r = −0.111, P < .001; PlGF: r = 0.203, P < .001; sFlt-1: r = −0.216, P < .001; UA-PI: r = −0.044, P < .001; MCA-PI: r = 0.082, P < .001). There was no significant difference in the median biomarker MoM values between the training and validation datasets in either the SGA group or in those with birthweight ≥10th percentile (Table 2).

      Prediction of SGA

      In the training dataset, multivariable logistic regression analysis demonstrated that in the prediction of SGA <10th percentile there were significant contributions from maternal factors, EFW Z score, UtA-PI MoM, MCA-PI MoM, and PlGF MoM (Table 4).
      Table 4Fitted regression models with maternal characteristics and history (maternal factors), estimated fetal weight Z score, and biomarkers at 35+0–36+6 weeks’ gestation for the prediction of small for gestational age neonates with birthweight below the 10th percentile
      Independent variableCoefficientSEOR95% CIP value
      Intercept0.858040.08038
      Maternal factors + EFW3.110530.0937422.43(18.67–26.96)<.001
      Uterine artery PI MoM0.724950.347412.07(1.05–4.08)<.001
      Middle cerebral artery PI MoM–2.173590.617310.11(0.03–0.38)<.001
      Placental growth factor MoM–1.390960.115570.25(0.20–0.31)<.001
      CI, confidence interval; EFW, estimated fetal weight; MoM, multiple of the median; OR, odds ratio; PI, pulsatility index; SE, standard error.
      Ciobanu et al. Third-trimester screening for SGA. Am J Obstet Gynecol 2019.
      The performance of predicting birth of SGA neonates at any stage after assessment at 35−37 weeks by maternal factors, EFW, and biomarkers is reported in Table 5. The area under the ROC curve (AUC) and DR at a 10% screen-positive rate in the validation dataset were consistent with those in the training dataset. The DRs at different screen-positive rates for SGA <10th percentile delivering within 2 weeks and at any time from assessment in screening by maternal factors, maternal factors, and EFW Z score and combined screening by maternal factors, EFW Z score, and biomarkers in the validation dataset are shown in Figure 1.
      Table 5Performance of prediction of small for gestational age neonates with birthweight <10th and <3rd percentiles delivering at any stage after screening at 35+0–36+6 weeks’ gestation
      Screening testTraining datasetValidation dataset
      AUC (95% CI)DR at 10% SPR % (95% CI)AUC (95% CI)DR at 10% SPR % (95% CI)
      SGA <10th percentile
      Maternal factors0.709 (0.693–0.725)30 (27–33)0.719 (0.710–0.728)32 (30–36)
      Maternal factors plus EFW Z score0.891 (0.885–0.897)67 (64–70)0.890 (0.883–0.896)66 (63–69)
      + Mean arterial pressure0.892 (0.886–0.898)67 (64–70)0.891 (0.884–0.897)66 (63–69)
      + UtA-PI0.892 (0.887–0.898)67 (64–70)0.892 (0.886–0.899)67 (64–70)
      + UA-PI0.893 (0.886–0.899)68 (65–71)0.892 (0.885–0.898)68 (65–71)
      + MCA-PI0.894 (0.887–0.898)68 (65–71)0.891 (0.885–0.897)66 (63–69)
      + Placental growth factor0.902 (0.896–0.908)70 (67–72)0.897 (0.891–0.903)69 (66–72)
      + Soluble fms-like tyrosine kinase-10.895 (0.888–0.899)68 (65–71)0.891 (0.884–0.897)67 (64–70)
      + UtA-PI + UA-PI + MCA-PI0.895 (0.888–0.900)68 (65–71)0.893 (0.887–0.899)67 (64–70)
      + UtA-PI + MCA-PI + PlGF0.903 (0.897–0.909)70 (67–72)0.898 (0.892–0.904)69 (66–72)
      SGA <3rd percentile
      Maternal factors0.743 (0.719–0.768)40 (34–45)0.738 (0.729–0.747)37 (32–42)
      Maternal factors plus EFW Z score0.931 (0.926–0.936)77 (72–81)0.920 (0.915–0.926)76 (71–80)
      + Mean arterial pressure0.931 (0.926–0.936)79 (74–83)0.921 (0.916–0.927)76 (71–81)
      + UtA-PI0.933 (0.927–0.937)78 (74–83)0.922 (0.916–0.927)76 (71–80)
      + UA-PI0.931 (0.926–0.936)78 (74–83)0.923 (0.917–0.928)76 (71–80)
      + MCA-PI0.932 (0.927–0.937)78 (74–82)0.922 (0.916–0.927)76 (71–80)
      + Placental growth factor0.939 (0.934–0.943)82 (77–86)0.925 (0.920–0.931)77 (73–82)
      + Soluble fms-like tyrosine kinase-10.936 (0.931–0.941)80 (75–84)0.921 (0.916–0.927)76 (72–81)
      + UtA-PI + UA-PI + MCA-PI0.932 (0.927–0.937)80 (75–84)0.924 (0.918–0.929)77 (72–81)
      + UtA-PI + MCA-PI + PlGF0.940 (0.735–0.745)82 (78–86)0.929 (0.923–0.934)79 (74–83)
      AUC, area under the receiver operating characteristic curve; CI, confidence interval; DR, detection rate; EFW, estimated fetal weight; MCA-PI, middle cerebral artery pulsatility index; SGA, small for gestational age; SPR, screen-positive rate; UA-PI, umbilical artery pulsatility index; UtA-PI, uterine artery pulsatility index.
      Ciobanu et al. Third-trimester screening for SGA. Am J Obstet Gynecol 2019.
      Figure thumbnail gr1
      Figure 1Receiver operating characteristic (ROC) curves of maternal factors (black line), maternal factors with estimated fetal weight (blue), maternal factors with estimated fetal weight and biomarkers (red) at 35+0–36+6 weeks’ gestation, in the prediction of small for gestational age neonates with birthweight below the 10th percentile, delivering within 2 weeks (left) and at any time (right) from assessment
      Ciobanu et al. Third-trimester screening for SGA. Am J Obstet Gynecol 2019.
      In the validation dataset, the DR of SGA <10th percentile delivering at any stage after assessment, at a 10% screen-positive rate, was 32% in screening by maternal factors, 66% by maternal factors and EFW Z score, and 69% by maternal factors, EFW Z score, and MoM values of UtA-PI, MCA-PI, and PlGF; the respective values for SGA <3rd percentile were 37%, 76%, and 79% (Table 5). The DR of SGA <10th percentile delivering within 2 weeks of assessment, at a 10% screen-positive rate, was 31% (95% confidence interval [CI], 25−37; AUC 0.718, 95% CI, 0.69−0.744) in screening by maternal factors, 75% (95% CI, 69−81; AUC 0.931, 95% CI, 0.914−0.945) by maternal factors and EFW Z score, and 80% (95% CI, 74−86; AUC 0.933, 95% CI, 0.917−0.949) by maternal factors, EFW Z score, and MoM values of UtA-PI, MCA-PI, and PlGF; the respective values for SGA <3rd percentile were 33% (95% CI, 25−42; AUC 0.726, 95% CI, 0.699−0.652), 85% (95% CI, 77−91; AUC 0.945, 95% CI, 0.930−0.958), and 83% (95% CI, 77−90; AUC 0.945, 95% CI, 0.930−0.958).
      The screen-positive rates necessary to achieve prediction of 85%, 90%, and 95% of SGA neonates delivering within 2 weeks and at any stage from assessment are shown in Table 6. If the desired DR of SGA <10th percentile delivering within 2 weeks of assessment was 90%, the necessary screen-positive rate would be 67% in screening by maternal factors, 23% by maternal factors and EFW Z score, and 21% by the combined test; the respective values for SGA <3rd percentile were 63%, 18%, and 15%.
      Table 6Screen-positive rate necessary to achieve prediction of 85%, 90%, and 95% of small for gestational age neonates delivering within 2 weeks and at any stage after assessment at 35+0–36+6 weeks’ gestation
      Screening testSPR for 85% DR % (95% CI)SPR for 90% DR% (95% CI)SPR for 95% DR% (95% CI)
      SGA within 2 wk
      SGA <10th percentile
       Maternal factors60 (57–63)67 (64–70)83 (80–85)
       Maternal factors + EFW Z score16 (13–18)23 (20–26)31 (28–34)
       + UtA-PI + MCA-PI + PlGF13 (11–16)21 (19–24)29 (26–33)
      SGA <3rd percentile
       Maternal factors57 (53–60)63 (60–66)70 (67–73)
       Maternal factors + EFW Z score12 (10–14)18 (16–21)27 (24–30)
       + UtA-PI + MCA-PI + PlGF11 (9–13)15 (13–18)21 (19–24)
      SGA at any stage
      SGA <10th percentile
       Maternal factors59 (58–60)66 (65–67)84 (83–85)
       Maternal factors + EFW Z score24 (23–25)32 (31–33)43 (42–44)
       + UtA-PI + MCA-PI + PlGF23 (22–24)30 (29–31)40 (39–41)
      SGA <3rd percentile
       Maternal factors60 (59–61)68 (67–69)75 (74–76)
       Maternal factors + EFW Z score17 (16–18)23 (22–24)31 (30–32)
       + UtA-PI + MCA-PI + PlGF15 (14–16)20 (19–21)28 (27–29)
      CI, confidence interval; DR, detection rate; EFW, estimated fetal weight; MCA-PI, middle cerebral artery pulsatility index; PlGF, placental growth factor; SGA, small for gestational age; SPR, screen-positive rate; UtA-PI, uterine artery pulsatility index.
      Ciobanu et al. Third-trimester screening for SGA. Am J Obstet Gynecol 2019.

      Comment

      Main Study Findings

      The findings from this study demonstrate that the risk of delivering SGA neonates increases with maternal age; decreases with maternal weight and height; is higher in women of black, South Asian, East Asian, and mixed racial origins than in white women; and increases in cigarette smokers, those with chronic hypertension, those with diabetes mellitus type 2, and parous women with prior history of SGA. The risk is lower in parous women without a prior history of SGA and in those with diabetes mellitus type 1. The distribution of SGA <10th percentile that delivered at <32, 32−36, and at ≥37 weeks’ gestation was 3.6%, 11.5%, and 84.9%, respectively; therefore, the vast majority of SGA neonates are born at term.
      In pregnancies that deliver SGA neonates, the EFW, PlGF, and MCA-PI at 35+0−36+6 weeks’ gestation are reduced, whereas the UtA-PI, UA-PI, and sFLT are increased. The deviations of biomarkers from normal are more pronounced in those with severe disease reflected at lower birthweight (3rd vs 10th percentile) and delivery within 2 weeks rather than at any stage from assessment. Multivariable regression analysis demonstrated that a significant independent conribution in the prediction of SGA was provided by maternal factors, EFW Z score, and MoM values of UtA-PI, MCA-PI, and PlGF. Screening by maternal factors and EFW predicted 75% and 85% of SGA neonates with birthweight <10th and <3rd percentiles delivering within 2 weeks of assessment, at a screen-positive rate of 10%; the respective values for SGA delivering at any stage after assessment were 66% and 76%. The addition of other biomarkers had a marginal improvement in predictive performance of SGA neonates. If the desired detection rate of SGA <10th percentile delivering within 10 weeks of assessment was 90%, the necessary screen-positive rate would be 67% in screening by maternal factors, 23% by maternal factors and EFW, and 21% by a combination of maternal factors, EFW, and biomarkers of impaired placentation; the respective values for prediction of SGA neonates delivering at any stage after assessment were 66%, 32%, and 30%.
      The objective of our study was to define the performance of maternal factors, fetal biometry, and biomarkers of impaired placentation in the prediction of SGA neonates during routine assessment at 35+0−36+6 weeks’ gestation. The rationale for such prediction is that SGA neonates, especially those with birthweight <3rd percentile, are at substantially increased risk for neonatal death and adverse neonatal outcome.
      • McIntire D.D.
      • Bloom S.L.
      • Casey B.M.
      • Leveno K.J.
      Birthweight in relation to morbidity and mortality among newborn infants.
      However, a high proportion of SGA fetuses are constitutionally small at no increased risk for adverse outcome,
      • Deter R.L.
      • Lee W.
      • Sangi-Haghpeykar H.
      • Kingdom J.
      • Romero R.
      Third trimester growth restriction patterns: individualized assessment using a fetal growth pathology score.
      and 80−85% of perinatal deaths and cases of hypoxic ischemic encephalopathy at term, cesarean delivery for presumed fetal distress in labor, and presence of surrogate markers of perinatal hypoxia, including low 5-minute Apgar score, low cord blood pH, and admission to the neonatal intensive care unit for more than 24 hours, occur in infants with birthweight ≥10th percentile.
      • Akolekar R.
      • Syngelaki A.
      • Gallo D.M.
      • Poon L.C.
      • Nicolaides K.H.
      Umbilical and fetal middle cerebral artery Doppler at 35-37 weeks’ gestation in the prediction of adverse perinatal outcome.
      • Akolekar R.
      • Ciobanu A.
      • Zingler E.
      • Syngelaki A.
      • Nicolaides K.H.
      Cerebroplacental ratio at 35-37 weeks’ gestation in the prediction of adverse perinatal outcome.
      It could therefore be argued that prenatal care should be directed at identifying hypoxemic rather than small fetuses. One such potential marker of fetal hypoxia is low CPR.
      • Nicolaides K.H.
      • Bilardo K.M.
      • Soothill P.W.
      • Campbell S.
      Absence of end diastolic frequencies in the umbilical artery a sign of fetal hypoxia and acidosis.
      • Vyas S.
      • Nicolaides K.H.
      • Bower S.
      • Campbell S.
      Middle cerebral artery flow velocity waveforms in fetal hypoxaemia.
      • Bahado-Singh R.O.
      • Kovanci E.
      • Jeffres A.
      • et al.
      The Doppler cerebroplacental ratio and perinatal outcome in intrauterine growth restriction.
      • DeVore G.R.
      The importance of the cerebroplacental ratio in the evaluation of fetal well-being in SGA and AGA fetuses.
      • Prior T.
      • Mullins E.
      • Bennett P.
      • Kumar S.
      Prediction of intrapartum fetal compromise using the cerebroumbilical ratio: a prospective observational study.
      • Khalil A.A.
      • Morales-Rosello J.
      • Morlando M.
      • et al.
      Is fetal cerebroplacental ratio an independent predictor of intrapartum fetal compromise and neonatal unit admission?.
      • Khalil A.A.
      • Morales-Rosello J.
      • Elsadigg M.
      • et al.
      The association between fetal Doppler and admission to neonatal unit at term.
      • Khalil A.
      • Morales-Rosello J.
      • Khan N.
      • et al.
      Is cerebroplacental ratio a marker of impaired fetal growth velocity and adverse pregnancy outcome?.
      However, major studies in women undergoing routine ultrasound examination at 35+6−36+6 weeks’ gestation found that low CPR provided poor prediction of adverse perinatal outcome in both small and appropriate for gestational age fetuses.
      • Akolekar R.
      • Syngelaki A.
      • Gallo D.M.
      • Poon L.C.
      • Nicolaides K.H.
      Umbilical and fetal middle cerebral artery Doppler at 35-37 weeks’ gestation in the prediction of adverse perinatal outcome.
      • Akolekar R.
      • Ciobanu A.
      • Zingler E.
      • Syngelaki A.
      • Nicolaides K.H.
      Cerebroplacental ratio at 35-37 weeks’ gestation in the prediction of adverse perinatal outcome.
      Consequently, there is no justification for a shift of the focus of prenatal care from identification of pregnancies with low EFW to that of pregnancies with low CPR. We are currently investigating the potential value of biochemical markers in the prediction of adverse outcome in small and appropriate for gestational age fetuses.
      An alternative strategy for identifying malnourished SGA fetuses is to perform serial ultrasound scans to estimate fetal growth potential and to generate individualized third-trimester size trajectories.
      • Deter R.L.
      • Lee W.
      • Sangi-Haghpeykar H.
      • Kingdom J.
      • Romero R.
      Third trimester growth restriction patterns: individualized assessment using a fetal growth pathology score.
      • Deter R.L.
      • Lee W.
      • Yeo L.
      • Erez O.
      • Ramamurthy U.
      • Naik M.
      • Romero R.
      Individualized growth assessment: conceptual framework and practical implementation for the evaluation of fetal growth and neonatal growth outcome.
      In our study, we undertook assessment at a single point, rather than using serial scans, to evaluate growth.

      Comparison With Findings From Previous Studies

      Our findings that prediction of SGA at term by a combination of maternal factors and EFW at 35−37 weeks’ gestation is superior to that of screening at 30−34 weeks
      • Bakalis S.
      • Silva M.
      • Akolekar R.
      • Poon L.C.
      • Nicolaides K.H.
      Prediction of small for gestational age neonates: screening by fetal biometry at 30–34 weeks.
      is consistent with the results of a previous study in 2288 pregnancies undergoing ultrasound examination in both of these gestational windows,
      • Souka A.P.
      • Papastefanou I.
      • Pilalis A.
      • Michalitsi V.
      • Panagopoulos P.
      • Kassanos D.
      Performance of the ultrasound examination in the early and late third trimester for the prediction of birthweight deviations.
      and those of a randomized trial comparing the performance of ultrasound examination at 36 vs 32 weeks’ gestation.
      • Roma E.
      • Arnau A.
      • Berdala R.
      • Bergos C.
      • Montesinos J.
      • Figueras F.
      Ultrasound screening for fetal growth restriction at 36 vs 32 weeks' gestation: a randomized trial (ROUTE).
      Similarly, the finding that the performance of screening for SGA at 35−37 weeks by maternal factors and biometry is not significantly improved by additional biomarkers is consistent with findings of previous smaller studies that examined the additional value of some of the biomarkers examined in this study.
      • Fadigas C.
      • Guerra L.
      • Garcia-Tizon Larroca S.
      • Poon L.C.
      • Nicolaides K.H.
      Prediction of small-for-gestational-age neonates: screening by uterine artery Doppler and mean arterial pressure at 35-37 weeks.
      • Triunfo S.
      • Crispi F.
      • Gratacos E.
      • Figueras F.
      Prediction of delivery of small-for-gestational-age neonates and adverse perinatal outcome by fetoplacental Doppler at 37 weeks' gestation.
      • Fadigas C.
      • Peeva G.
      • Mendez O.
      • Poon L.C.
      • Nicolaides K.H.
      Prediction of small-for-gestational-age neonates: screening by placental growth factor and soluble fms-like tyrosine kinase-1 at 35-37 weeks.
      In our much-larger study, we used training and validation datasets to examine the predictive performance of screening for 2 degrees of severity of SGA (<10th and <3rd percentiles) and at 2 intervals from assessment (within 2 weeks and at any stage) and to report the potential contribution of 5 biomarkers of impaired placentation (UtA-PI, UA-PI, MCA-PI, PlGF, and sFLT).

      Implications for Clinical Practice

      In the proposed new pyramid of pregnancy care,
      • Nicolaides K.H.
      Turning the pyramid of prenatal care.
      an integrated clinic at 11−13 weeks’ gestation, in which biophysical and biochemical markers are combined with maternal characteristics and medical history, aims to identify pregnancies at high risk for preterm PE and/or SGA and, through pharmacological intervention, to reduce the prevalence of these complications.
      • Wright D.
      • Syngelaki A.
      • Akolekar R.
      • Poon L.C.
      • Nicolaides K.H.
      Competing risks model in screening for preeclampsia by maternal characteristics and medical history.
      • O’Gorman N.
      • Wright D.
      • Syngelaki A.
      • et al.
      Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11-13 weeks’ gestation.
      • Rolnik D.L.
      • Wright D.
      • Poon L.C.
      • et al.
      Aspirin versus placebo in pregnancies at high risk for preterm preeclampsia.
      • Wright D.
      • Rolnik D.L.
      • Syngelaki A.
      • et al.
      Aspirin for Evidence-Based Preeclampsia Prevention trial: effect of aspirin on length of stay in the neonatal intensive care unit.
      • Wright D.
      • Poon L.C.
      • Rolnik D.L.
      • et al.
      Aspirin for Evidence-Based Preeclampsia Prevention trial: influence of compliance on beneficial effect of aspirin in prevention of preterm preeclampsia.
      • Poon L.C.
      • Wright D.
      • Rolnik D.L.
      • et al.
      Aspirin for Evidence-Based Preeclampsia Prevention trial: effect of aspirin in prevention of preterm preeclampsia in subgroups of women according to their characteristics and medical and obstetrical history.
      • Roberge S.
      • Bujold E.
      • Nicolaides K.H.
      Aspirin for the prevention of preterm and term preeclampsia: systematic review and metaanalysis.
      • Tan M.Y.
      • Poon L.C.
      • Rolnik D.L.
      • et al.
      Prediction and prevention of small-for-gestational-age neonates: evidence from SPREE and ASPRE.
      The objective of subsequent visits, at around 20 and 32 or 36 weeks’ gestation, are to identify the high-risk group and, through close monitoring of such pregnancies, to minimize adverse perinatal events by determining the appropriate time and place for iatrogenic delivery. We have previously proposed that all women should be offered a third-trimester scan for assessment of fetal growth and well-being, and that the timing of such a scan, at 32 and/or 36 weeks, should be contingent on the results of assessment at around 20 weeks.
      • Poon L.C.
      • Lesmes C.
      • Gallo D.M.
      • Akolekar R.
      • Nicolaides K.H.
      Prediction of small-for-gestational-age neonates: screening by biophysical and biochemical markers at 19-24 weeks.
      Assessment at 20 weeks’ gestation would stratify the population into a high-risk group comprising <0.5% of all pregnancies and containing all cases of SGA that deliver at <32 weeks; a moderate-risk group comprising about 16% of pregnancies and containing about 90% of cases of SGA that deliver at 32−36 weeks; and a low-risk group comprising about 60% of pregnancies and containing about 90% of cases of SGA that deliver at ≥37 weeks. It was proposed that the high-risk group would require reassessment at 26−28 weeks and then again at 32 and 36 weeks if not delivered; the moderate-risk group would be reassessed at 32 and 36 weeks; and the low-risk group would be reassessed at 36 weeks.
      • Poon L.C.
      • Lesmes C.
      • Gallo D.M.
      • Akolekar R.
      • Nicolaides K.H.
      Prediction of small-for-gestational-age neonates: screening by biophysical and biochemical markers at 19-24 weeks.
      Each assessment would then identify a very-high-risk group in need of intensive monitoring, including fetal growth, biophysical profile, fetal heart rate patterns, and fetal Doppler profile, to define the best plan for delivery.
      This study provides the necessary data for development of policies to achieve prenatal prediction of a desired percentage of SGA neonates. If the assessment at 36 weeks’ gestation includes a combination of maternal factors, EFW, and biophysical and biochemical markers of impaired placentation, it could potentially predict about 90% of SGA neonates delivering within 2 weeks of assessment at a screen-positive rate of about 20%, and 90% of SGA neonates delivering at any stage after assessment at a screen-positive rate of 30%. The additional value of biomarkers in the prediction of SGA neonates is marginal, and their contribution in reducing the screen-positive rate by 2% would be achieved at a greatly increased cost of screening. However, in an integrated clinic at 35+0–36+6 weeks’ gestation, measurement of sFLT and PlGF is useful in the prediction of PE,
      • Ciobanu A.
      • Wright A.
      • Panaitescu A.
      • Syngelaki A.
      • Wright D.
      • Nicolaides K.H.
      Prediction of imminent preeclampsia at 35-37 weeks’ gestation.
      and measurement of UtA-PI, UA-PI, and MCA-PI is important in the assessment of oxygenation of SGA fetuses.
      • Bahado-Singh R.O.
      • Kovanci E.
      • Jeffres A.
      • et al.
      The Doppler cerebroplacental ratio and perinatal outcome in intrauterine growth restriction.
      • DeVore G.R.
      The importance of the cerebroplacental ratio in the evaluation of fetal well-being in SGA and AGA fetuses.
      • McCowan L.M.
      • Figueras F.
      • Anderson N.H.
      Evidence-based national guidelines for the management of suspected fetal growth restriction: comparison, consensus, and controversy.
      • Figueras F.
      • Caradeux J.
      • Crispi F.
      • Eixarch E.
      • Peguero A.
      • Gratacos E.
      Diagnosis and surveillance of late-onset fetal growth restriction.
      The best management of the screen-positive group with the objective of reducing perinatal death and handicap remains to be determined.

      Strengths and Limitations of the Study

      The strengths of this third-trimester screening study for SGA are, first, examination of a large population of pregnant women attending for routine assessment of fetal growth and well-being at 35–37 weeks’ gestation; second, the use of the Bayes theorem to combine the prior risk from maternal characteristics and medical history with fetal biometry and biomarkers of impaired placentation to estimate patient-specific risks and the performance of screening for SGA of different severities delivering at selected intervals from the time of assessment; and third, use of different datasets for training and validation of the prediction models.
      A limitation of the study is that the results of fetal biometry at the 35+0−36+6 weeks’ scan were made available to the patients’ obstetricians, who would have taken specific actions of further monitoring for the cases of suspected SGA, and consequently the performance of screening, particularly in those delivering within 2 weeks of assessment, would be positively biased.

      Conclusion

      About 85% of SGA neonates are born at term. Effective screening for late SGA is provided by a combination of maternal factors and fetal biometry at 35+0–36+6 weeks’ gestation, and the addition of biomarkers of impaired placentation only marginally improves the predictive performance of such screening.

      References

        • Lindqvist P.G.
        • Molin J.
        Does antenatal identification of small-for-gestational age fetuses significantly improve their outcome?.
        Ultrasound Obstet Gynecol. 2005; 25: 258-264
        • Gaccioli F.
        • Aye I.L.M.H.
        • 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. 2018; 218: S725-S737
        • Bais J.M.J.
        • Eskes M.
        • Pel M.
        • Bonsel G.J.
        • Bleker O.P.
        Effectiveness of detection of intrauterine retardation by abdominal palpation as screening test in a low-risk population: an observational study.
        Eur J Obstet Gynecol Reprod Biol. 2004; 116: 164-169
        • Lindhard A.
        • Nielsen P.V.
        • Mouritsen L.A.
        • Zachariassen A.
        • Sørensen H.U.
        • Rosenø H.
        The implications of introducing the symphyseal-fundal height-measurement. A prospective randomized controlled trial.
        Br J Obstet Gynaecol. 1990; 97: 675-680
        • Skovron M.L.
        • Berkowitz G.S.
        • Lapinski R.H.
        • Kim J.M.
        • Chitkara U.
        Evaluation of early third-trimester ultrasound screening for intrauterine growth retardation.
        J Ultrasound Med. 1991; 10: 153-159
        • David C.
        • Tagliavini G.
        • Pilu G.
        • Rudenholz A.
        • Bovicelli L.
        Receiver-operator characteristic curves for the ultrasonographic prediction of small-for-gestational-age fetuses in low-risk pregnancies.
        Am J Obstet Gynecol. 1996; 174: 1037-1042
        • De Reu P.A.
        • Smits L.J.
        • Oosterbaan H.P.
        • Nijhuis J.G.
        Value of a single early third trimester fetal biometry for the prediction of birthweight deviations in a low risk population.
        J Perinat Med. 2008; 36: 324-329
        • Di Lorenzo G.
        • Monasta L.
        • Ceccarello M.
        • Cecotti V.
        • D'Ottavio G.
        Third trimester abdominal circumference, estimated fetal weight and uterine artery Doppler for the identification of newborns small and large for gestational age.
        Eur J Obstet Gynecol Reprod Biol. 2013; 166: 133-138
        • Souka A.P.
        • Papastefanou I.
        • Pilalis A.
        • Michalitsi V.
        • Kassanos D.
        Performance of third-trimester ultrasound for prediction of small-for-gestational-age neonates and evaluation of contingency screening policies.
        Ultrasound Obstet Gynecol. 2012; 39: 535-542
        • Souka A.P.
        • Papastefanou I.
        • Pilalis A.
        • Michalitsi V.
        • Panagopoulos P.
        • Kassanos D.
        Performance of the ultrasound examination in the early and late third trimester for the prediction of birthweight deviations.
        Prenat Diagn. 2013; 33: 915-920
        • Fadigas C.
        • Saiid Y.
        • Gonzalez R.
        • Poon L.C.
        • Nicolaides K.H.
        Prediction of small for gestational age neonates: screening by fetal biometry at 35-37 weeks.
        Ultrasound Obstet Gynecol. 2015; 45: 559-565
        • Bakalis S.
        • Silva M.
        • Akolekar R.
        • Poon L.C.
        • Nicolaides K.H.
        Prediction of small for gestational age neonates: screening by fetal biometry at 30–34 weeks.
        Ultrasound Obstet Gynecol. 2015; 45: 551-558
        • Bakalis S.
        • Peeva G.
        • Gonzalez R.
        • Poon L.C.
        • Nicolaides K.H.
        Prediction of small-for-gestational-age neonates: screening by biophysical and biochemical markers at 30-34 weeks.
        Ultrasound Obstet Gynecol. 2015; 46: 446-451
        • Roma E.
        • Arnau A.
        • Berdala R.
        • Bergos C.
        • Montesinos J.
        • Figueras F.
        Ultrasound screening for fetal growth restriction at 36 vs 32 weeks' gestation: a randomized trial (ROUTE).
        Ultrasound Obstet Gynecol. 2015; 46: 391-397
        • Fadigas C.
        • Guerra L.
        • Garcia-Tizon Larroca S.
        • Poon L.C.
        • Nicolaides K.H.
        Prediction of small-for-gestational-age neonates: screening by uterine artery Doppler and mean arterial pressure at 35-37 weeks.
        Ultrasound Obstet Gynecol. 2015; 45: 715-721
        • Triunfo S.
        • Crispi F.
        • Gratacos E.
        • Figueras F.
        Prediction of delivery of small-for-gestational-age neonates and adverse perinatal outcome by fetoplacental Doppler at 37 weeks' gestation.
        Ultrasound Obstet Gynecol. 2017; 49: 364-371
        • Fadigas C.
        • Peeva G.
        • Mendez O.
        • Poon L.C.
        • Nicolaides K.H.
        Prediction of small-for-gestational-age neonates: screening by placental growth factor and soluble fms-like tyrosine kinase-1 at 35-37 weeks.
        Ultrasound Obstet Gynecol. 2015; 46: 191-197
        • Hadlock F.P.
        • Harrist R.B.
        • Martinez-Poyer J.
        In utero analysis of fetal growth: a sonographic weight standard.
        Radiology. 1991; 181: 129-133
        • Hammami A.
        • Mazer Zumaeta A.
        • Syngelaki A.
        • Akolekar R.
        • Nicolaides K.H.
        Ultrasonographic estimation of fetal weight: development of new model and assessment of performance of previous models.
        Ultrasound Obstet Gynecol. 2018; 52: 35-43
        • Albaiges G.
        • Missfelder-Lobos H.
        • Lees C.
        • Parra M.
        • Nicolaides K.H.
        One-stage screening for pregnancy complications by color Doppler assessment of the uterine arteries at 23 weeks’ gestation.
        Obstet Gynecol. 2000; 96: 559-564
        • Ciobanu A.
        • Wright A.
        • Syngelaki A.
        • Wright D.
        • Akolekar R.
        • Nicolaides K.H.
        Fetal Medicine Foundation reference ranges for umbilical artery and middle cerebral artery pulsatility index and cerebroplacental ratio.
        Ultrasound Obstet Gynecol. 2018 Oct 24; https://doi.org/10.1002/uog.20157
        • Poon L.C.
        • Zymeri N.A.
        • Zamprakou A.
        • Syngelaki A.
        • Nicolaides K.H.
        Protocol for measurement of mean arterial pressure at 11-13 weeks' gestation.
        Fetal Diagn Ther. 2012; 31: 42-48
        • Robinson H.P.
        • Fleming J.E.
        A critical evaluation of sonar crown-rump length measurements.
        Br J Obstet Gynaecol. 1975; 82: 702-710
        • Snijders R.J.
        • Nicolaides K.H.
        Fetal biometry at 14-40 weeks’ gestation.
        Ultrasound Obstet Gynecol. 1994; 4: 34-48
        • Nicolaides K.H.
        • Wright D.
        • Syngelaki A.
        • Wright A.
        • Akolekar R.
        Fetal Medicine Foundation fetal and neonatal population weight charts.
        Ultrasound Obstet Gynecol. 2018; 52: 44-51
        • Panaitescu A.
        • Ciobanu A.
        • Syngelaki A.
        • Wright A.
        • Wright D.
        • Nicolaides K.H.
        Screening for pre-eclampsia at 35-37 weeks’ gestation.
        Ultrasound Obstet Gynecol. 2018; 52: 501-506
        • McIntire D.D.
        • Bloom S.L.
        • Casey B.M.
        • Leveno K.J.
        Birthweight in relation to morbidity and mortality among newborn infants.
        N Engl J Med. 1999; 340: 1234-1238
        • Deter R.L.
        • Lee W.
        • Sangi-Haghpeykar H.
        • Kingdom J.
        • Romero R.
        Third trimester growth restriction patterns: individualized assessment using a fetal growth pathology score.
        J Matern Fetal Neonatal Med. 2018; 31: 2155-2163
        • Akolekar R.
        • Syngelaki A.
        • Gallo D.M.
        • Poon L.C.
        • Nicolaides K.H.
        Umbilical and fetal middle cerebral artery Doppler at 35-37 weeks’ gestation in the prediction of adverse perinatal outcome.
        Ultrasound Obstet Gynecol. 2015; 46: 82-92
        • Akolekar R.
        • Ciobanu A.
        • Zingler E.
        • Syngelaki A.
        • Nicolaides K.H.
        Cerebroplacental ratio at 35-37 weeks’ gestation in the prediction of adverse perinatal outcome.
        Am J Obstet Gynecol. 2019; (submitted)
        • Nicolaides K.H.
        • Bilardo K.M.
        • Soothill P.W.
        • Campbell S.
        Absence of end diastolic frequencies in the umbilical artery a sign of fetal hypoxia and acidosis.
        BMJ. 1988; 297: 1026-1027
        • Vyas S.
        • Nicolaides K.H.
        • Bower S.
        • Campbell S.
        Middle cerebral artery flow velocity waveforms in fetal hypoxaemia.
        Br J Obstet Gynaecol. 1990; 97: 797-803
        • Bahado-Singh R.O.
        • Kovanci E.
        • Jeffres A.
        • et al.
        The Doppler cerebroplacental ratio and perinatal outcome in intrauterine growth restriction.
        Am J Obstet Gynecol. 1999; 180: 750-756
        • DeVore G.R.
        The importance of the cerebroplacental ratio in the evaluation of fetal well-being in SGA and AGA fetuses.
        Am J Obstet Gynecol. 2015; 213: 5-15
        • Prior T.
        • Mullins E.
        • Bennett P.
        • Kumar S.
        Prediction of intrapartum fetal compromise using the cerebroumbilical ratio: a prospective observational study.
        Am J Obstet Gynecol. 2013; 208: 124.e1-124.e6
        • Khalil A.A.
        • Morales-Rosello J.
        • Morlando M.
        • et al.
        Is fetal cerebroplacental ratio an independent predictor of intrapartum fetal compromise and neonatal unit admission?.
        Am J Obstet Gynecol. 2015; 213: 54.e1-54.e10
        • Khalil A.A.
        • Morales-Rosello J.
        • Elsadigg M.
        • et al.
        The association between fetal Doppler and admission to neonatal unit at term.
        Am J Obstet Gynecol. 2015; 213: 57.e1-57.e7
        • Khalil A.
        • Morales-Rosello J.
        • Khan N.
        • et al.
        Is cerebroplacental ratio a marker of impaired fetal growth velocity and adverse pregnancy outcome?.
        Am J Obstet Gynecol. 2017; 216: 606.e1-606.e10
        • Deter R.L.
        • Lee W.
        • Yeo L.
        • Erez O.
        • Ramamurthy U.
        • Naik M.
        • Romero R.
        Individualized growth assessment: conceptual framework and practical implementation for the evaluation of fetal growth and neonatal growth outcome.
        Am J Obstet Gynecol. 2018; 218: S656-S678
        • Nicolaides K.H.
        Turning the pyramid of prenatal care.
        Fetal Diagn Ther. 2011; 29: 183-196
        • Wright D.
        • Syngelaki A.
        • Akolekar R.
        • Poon L.C.
        • Nicolaides K.H.
        Competing risks model in screening for preeclampsia by maternal characteristics and medical history.
        Am J Obstet Gynecol. 2015; 213: 62.e1-62.e10
        • O’Gorman N.
        • Wright D.
        • Syngelaki A.
        • et al.
        Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11-13 weeks’ gestation.
        Am J Obstet Gynecol. 2016; 214: 103.e1-103.e12
        • Rolnik D.L.
        • Wright D.
        • Poon L.C.
        • et al.
        Aspirin versus placebo in pregnancies at high risk for preterm preeclampsia.
        N Engl J Med. 2017; 377: 613-622
        • Wright D.
        • Rolnik D.L.
        • Syngelaki A.
        • et al.
        Aspirin for Evidence-Based Preeclampsia Prevention trial: effect of aspirin on length of stay in the neonatal intensive care unit.
        Am J Obstet Gynecol. 2018; 218: 612.e1-612.e6
        • Wright D.
        • Poon L.C.
        • Rolnik D.L.
        • et al.
        Aspirin for Evidence-Based Preeclampsia Prevention trial: influence of compliance on beneficial effect of aspirin in prevention of preterm preeclampsia.
        Am J Obstet Gynecol. 2017; 217: 685.e1-685.e5
        • Poon L.C.
        • Wright D.
        • Rolnik D.L.
        • et al.
        Aspirin for Evidence-Based Preeclampsia Prevention trial: effect of aspirin in prevention of preterm preeclampsia in subgroups of women according to their characteristics and medical and obstetrical history.
        Am J Obstet Gynecol. 2017; 217: 585.e1-585.e5
        • Roberge S.
        • Bujold E.
        • Nicolaides K.H.
        Aspirin for the prevention of preterm and term preeclampsia: systematic review and metaanalysis.
        Am J Obstet Gynecol. 2018; 218: 287-293
        • Tan M.Y.
        • Poon L.C.
        • Rolnik D.L.
        • et al.
        Prediction and prevention of small-for-gestational-age neonates: evidence from SPREE and ASPRE.
        Ultrasound Obstet Gynecol. 2018; 52: 52-59
        • Poon L.C.
        • Lesmes C.
        • Gallo D.M.
        • Akolekar R.
        • Nicolaides K.H.
        Prediction of small-for-gestational-age neonates: screening by biophysical and biochemical markers at 19-24 weeks.
        Ultrasound Obstet Gynecol. 2015; 46: 437-445
        • Ciobanu A.
        • Wright A.
        • Panaitescu A.
        • Syngelaki A.
        • Wright D.
        • Nicolaides K.H.
        Prediction of imminent preeclampsia at 35-37 weeks’ gestation.
        Am J Obstet Gynecol. 2019; (submitted)
        • McCowan L.M.
        • Figueras F.
        • Anderson N.H.
        Evidence-based national guidelines for the management of suspected fetal growth restriction: comparison, consensus, and controversy.
        Am J Obstet Gynecol. 2018; 218: S855-S868
        • Figueras F.
        • Caradeux J.
        • Crispi F.
        • Eixarch E.
        • Peguero A.
        • Gratacos E.
        Diagnosis and surveillance of late-onset fetal growth restriction.
        Am J Obstet Gynecol. 2018; 218: S790-S802