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OBJECTIVES: Customized birthweight centiles have improved the detection of small for gestational age (SGA) and large for gestational age (LGA) babies compared to existing population standards. This study used perinatal registry data to derive coefficients for developing customized growth charts for Qatar. METHODS: The PEARL registry data on women delivering in Qatar (2017-2018) was used to develop a multivariable linear regression model predicting optimal birthweight. Physiological variables included gestational age, maternal height, weight, ethnicity, parity, and sex of the baby. Pathological variables such as hypertension, preexisting and gestational diabetes and smoking were calculated and excluded to derive the optimal weight at term. RESULTS: The regression model found a term optimal birthweight of 3,235 g for a Qatari nationality mother with median height (159 cm), booking weight (72 kg), parity (1) and gestation at birth (276 days) at the end of an uncomplicated pregnancy. Constitutional coefficients significantly affecting birthweight were gestational age, height, weight, and parity. The main pathological factors were preexisting diabetes (increase by +175.7 g) and smoking (decrease by -190.9 g). The SGA and LGA rates in the entire cohort after applying the population-specific customized centiles were 11.1 and 12.2 %, respectively (contrasting with the Hadlock standard: SGA-26.3 % and LGA-1.8 %, and Fenton standard: SGA-12.9 % and LGA-4.0 %). CONCLUSIONS: Constitutional and pathological variations in fetal growth and birthweight apply in the maternity population in Qatar and have been quantified to allow the generation of customised charts for better identification of pregnancies with abnormal growth. Currently in-use population standards may misdiagnose many SGA and LGA babies.

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BACKGROUND: Obesity and thinness are serious diseases, but cases with abnormal maternal weight have not been excluded from the calculations in the construction of customized fetal growth curves (CCs). METHOD: To determine if the new CCs, built excluding mothers with an abnormal weight, are better than standard CCs at identifying SGA. A total of 16,122 neonates were identified as SGA, LGA, or AGA, using the two models. Logistic regression and analysis of covariance were used to calculate the OR and CI for adverse outcomes by group. Gestational age was considered as a covariable. RESULTS: The SGA rates by the new CCs and by the standard CCs were 11.8% and 9.7%, respectively. The SGA rate only by the new CCs was 18% and the SGA rate only by the standard CCs was 0.01%. Compared to AGA by both models, SGA by the new CCs had increased rates of cesarean section, (OR 1.53 (95% CI 1.19, 1.96)), prematurity (OR 2.84 (95% CI 2.09, 3.85)), NICU admission (OR 5.41 (95% CI 3.47, 8.43), and adverse outcomes (OR 1.76 (95% CI 1.06, 2.60). The strength of these associations decreased with gestational age. CONCLUSION: The use of the new CCs allowed for a more accurate identification of SGA at risk of adverse perinatal outcomes as compared to the standard CCs.

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OBJECTIVE: The aim of the study was to determine if customized fetal growth charts developed excluding obese and underweight mothers (CC(18.5-25)) are better than customized curves (CC) at identifying pregnancies at risk of perinatal morbidity. MATERIAL AND METHODS: Data from 20,331 infants were used to construct CC and from 11,604 for CC(18.5-25), after excluding the cases with abnormal maternal BMI. The 2 models were applied to 27,507 newborns and the perinatal outcomes were compared between large for gestational age (LGA) or small for gestational age (SGA) according to each model. Logistic regression was used to calculate the OR of outcomes by the group, with gestational age (GA) as covariable. The confidence intervals of pH were calculated by analysis of covariance. RESULTS: The rate of cesarean and cephalopelvic disproportion (CPD) were higher in LGAonly by CC(18.5-25) than in LGAonly by CC. In SGAonly by CC(18.5-25), neonatal intensive care unit (NICU) and perinatal mortality rates were higher than in SGAonly by CC. Adverse outcomes rate was higher in LGAonly by CC(18.5-25) than in LGAonly by CC (21.6%; OR = 1.61, [1.34-193]) vs. (13.5%; OR = 0.84, [0.66-1.07]), and in SGA only by CC(18.5-25) than in SGAonly by CC (9.6%; OR = 1.62, [1.25-2.10] vs. 6.3%; OR = 1.18, [0.85-1.66]). CONCLUSION: The use of CC(18.5-25) allows a more accurate identification of LGA and SGA infants at risk of perinatal morbidity than conventional CC. This benefit increase and decrease, respectively, with GA.

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BACKGROUND: Fetal growth abnormalities are linked to stillbirth and other adverse pregnancy outcomes, and use of the correct birthweight standard is essential for accurate assessment of growth status and perinatal risk. OBJECTIVE: Two competing, conceptually opposite birthweight standards are currently being implemented internationally: customized gestation-related optimal weight (GROW) and INTERGROWTH-21st. We wanted to compare their performance when applied to a multiethnic international cohort, and evaluate their usefulness in the assessment of stillbirth risk at term. STUDY DESIGN: We analyzed routinely collected maternity data from 10 countries with a total of 1.25 million term pregnancies in their respective main ethnic groups. The 2 standards were applied to determine small for gestational age (SGA) and large for gestational age (LGA) rates, with associated relative risk and population-attributable risk of stillbirth. The customized standard (GROW) was based on the term optimal weight adjusted for maternal height, weight, parity, and ethnic origin, while INTERGROWTH-21st was a fixed standard derived from a multiethnic cohort of low-risk pregnancies. RESULTS: The customized standard showed an average SGA rate of 10.5% (range 10.1-12.7) and LGA rate of 9.5% (range 7.3-9.9) for the set of cohorts. In contrast, there was a wide variation in SGA and LGA rates with INTERGROWTH-21st, with an average SGA rate of 4.4% (range 3.1-16.8) and LGA rate of 20.6% (range 5.1-27.5). This variation in INTERGROWTH-21st SGA and LGA rates was correlated closely (R = ±0.98) to the birthweights predicted for the 10 country cohorts by the customized method to derive term optimal weight, suggesting that they were mostly due to physiological variation in birthweight. Of the 10.5% of cases defined as SGA according to the customized standard, 4.3% were also SGA by INTERGROWTH-21st and had a relative risk of 3.5 (95% confidence interval, 3.1-4.1) for stillbirth. A further 6.3% (60% of the whole customized SGA) were not SGA by INTERGROWTH-21st, and had a relative risk of 1.9 (95% confidence interval, 3.1-4.1) for stillbirth. An additional 0.2% of cases were SGA by INTERGROWTH-21st only, and had no increased risk of stillbirth. At the other end, customized assessment classified 9.5% of births as large for gestational age, most of which (9.0%) were also LGA by the INTERGROWTH-21st standard. INTERGROWTH-21st identified a further 11.6% as LGA, which, however, had a reduced risk of stillbirth (relative risk, 0.6; 95% confidence interval, 0.5-0.7). CONCLUSION: Customized assessment resulted in increased identification of small for gestational age and stillbirth risk, while the wide variation in SGA rates using the INTERGROWTH-21st standard appeared to mostly reflect differences in physiological pregnancy characteristics in the 10 maternity populations.

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OBJECTIVES: To assess the accuracy of customized growth charts for the ultrasound antenatal diagnostic of fetus small for gestational age in a high-risk population of preterm. METHODS: All premature infants born in a French university maternity center for a year and classified as small for gestational age at birth by using customized growth charts developed by Ego et al. were included in this retrospective study. At the ultrasound performed closest to the term, customized growth charts and population growth curves were compared for the antenatal diagnosis of a premature infants group classified small for gestational age in post-natal by customized growth charts and more at risk of perinatal complications. RESULTS: Sixty-seven newborns were included in the study. Fifty-one (76.1%) were secondarily classified as small for gestational age although they were eutrophic on the basis of population growth curves and 16 (23.9%) were small for gestational age on both curves. The average time between the last ultrasound and birth was 2.2 weeks. On the threshold of the tenth percentile, the sensitivities of customized growth charts and curves in population were not significantly different (29.85% versus 41.79% P=0.05) for antenatal detection of fetus small for gestational age. CONCLUSION: In our study, the use of customized growth charts does not improve the antenatal detection of most at-risk children.

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OBJECTIVE: To compare the effectiveness of customized vs population-based growth charts for the prediction of adverse pregnancy outcomes. METHODS: MEDLINE, ClinicalTrials.gov and The Cochrane Library were searched up to 31 May 2016 to identify interventional and observational studies comparing adverse outcomes among large- (LGA) and small- (SGA) for-gestational-age neonates, when classified according to customized vs population-based growth charts. Perinatal mortality and admission to the neonatal intensive care unit (NICU) of both SGA and LGA neonates, intrauterine fetal demise (IUFD) and neonatal mortality of SGA neonates, and neonatal shoulder dystocia and hypoglycemia as well as maternal third- and fourth-degree perineal lacerations in LGA pregnancies were evaluated. RESULTS: The electronic search identified 237 records that were examined based on title and abstract, of which 27 full-text articles were examined for eligibility. After excluding seven articles, 20 observational studies were included in a Bayesian meta-analysis. Neonates classified as SGA according to customized growth charts had higher risks of IUFD (odds ratio (OR), 7.8 (95% CI, 4.2-12.3)), neonatal death (OR, 3.5 (95% CI, 1.1-8.0)), perinatal death (OR, 5.8 (95% CI, 3.8-7.8)) and NICU admission (OR, 3.6 (95% CI, 2.0-5.5)) than did non-SGA cases. Neonates classified as SGA according to population-based growth charts also had increased risk for adverse outcomes, albeit the point estimates of the pooled ORs were smaller: IUFD (OR, 3.3 (95% CI, 1.9-5.0)), neonatal death (OR, 2.9 (95% CI, 1.2-4.5)), perinatal death (OR, 4.0 (95% CI, 2.8-5.1)) and NICU admission (OR, 2.4 (95% CI, 1.7-3.2)). For LGA vs non-LGA, there were no differences in pooled ORs for perinatal death, NICU admission, hypoglycemia and maternal third- and fourth-degree perineal lacerations when classified according to either the customized or the population-based approach. In contrast, both approaches indicated that LGA neonates are at increased risk for shoulder dystocia than are non-LGA ones (OR, 7.4 (95% CI, 4.9-9.8) using customized charts; OR, 8.0 (95% CI, 5.3-10.1) using population-based charts). CONCLUSIONS: Both customized and population-based growth charts can identify SGA neonates at risk for adverse outcomes. Although the point estimates of the pooled ORs may differ for some outcomes, the overlapping CIs and lack of direct comparisons prevent conclusions from being drawn on the superiority of one method. Future clinical trials should compare directly the two approaches in the management of fetuses of abnormal size. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

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OBJECTIVE: Our hypothesis was that newborns of obese mothers would be more likely to be classified as small for gestational age (SGA) by their customized growth curves than by the standard growth curves when compared to newborns of normal-weight mothers. METHODS: This is a retrospective cohort of primiparous patients delivering between 1 July 2008 and 30 June 2012. Normal-weight was defined as BMI ≤25 kg/m(2) and obese as BMI ≥ 30 kg/m(2). Infant birth-weight was characterized as SGA or non-SGA from the Lubchenco curve, the Fenton Preterm Growth Chart, and the customized growth curve. RESULTS: Infants were more likely to be classified as SGA on the customized curve compared with Lubchenco curve. Odds ratio was 2.8 (CI: 1.7-4.4; p = 0.001) for obese women and was 2.9 (CI: 1.7-5.1; p < 0.001) for normal-weight women. Infants were also more likely to be classified as SGA based on the customized curve compared with the Fenton Preterm Growth Curve. The odds ratio was 2.3 (CI: 1.4-3.8; p = 0.001) for obese women and was 1.5 (CI: 1.01-2.33; p = 0.04) for normal-weight women. CONCLUSIONS: Population-based curves may mask SGA in obese women. Our study demonstrates that customized growth curves identify more SGA than population-based growth curves in obese and normal-weight women.

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OBJECTIVES: The purpose of this study was to construct fetal biometric charts between 16 and 40 weeks' gestation that were customized for parental characteristics, race, and parity, using quantile regression analysis. METHODS: In a multicenter cross-sectional study, 8070 sonographic examinations from low-risk pregnancies between 16 and 40 weeks' gestation were analyzed. The fetal measurements obtained were biparietal diameter, head circumference, abdominal circumference, and femur diaphysis length. Quantile regression was used to examine the impact of parental height and weight, parity, and race across biometric percentiles for the fetal measurements considered. RESULTS: Paternal and maternal height were significant covariates for all of the measurements considered (P < .05). Maternal weight significantly influenced head circumference, abdominal circumference, and femur diaphysis length. Parity was significantly associated with biparietal diameter and head circumference. Central African race was associated with head circumference and femur diaphysis length, whereas North African race was only associated with femur diaphysis length. CONCLUSIONS: In this study we constructed customized biometric growth charts using quantile regression in a large cohort of low-risk pregnancies. These charts offer the advantage of defining individualized normal ranges of fetal biometric parameters at each specific percentile corrected for parental height and weight, parity, and race. This study supports the importance of including these variables in routine sonographic screening for fetal growth abnormalities.

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