The World Health Organization Fetal Growth Charts: A Multinational Longitudinal Study of Ultrasound Biometric Measurements and Estimated Fetal Weight

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Study Justification:
– Perinatal mortality and morbidity are major global health challenges associated with prematurity and reduced fetal growth.
– Fetal growth is linked to the risk of common noncommunicable diseases in adulthood.
– The World Health Organization (WHO) aims to provide fetal growth charts for estimated fetal weight (EFW) and common ultrasound biometric measurements for worldwide use.
Study Highlights:
– Multinational prospective observational longitudinal study of fetal growth in low-risk singleton pregnancies.
– Participants from ten countries were recruited, with reliable information on last menstrual period and gestational age confirmed by crown-rump length.
– Anthropometric and nutritional assessments, as well as seven scheduled ultrasound examinations, were conducted during pregnancy.
– Analysis of ultrasound measurements established longitudinal reference intervals for fetal biometric measurements and EFW.
– Variations in fetal growth were observed between countries and by maternal factors such as age, height, weight, and parity.
Study Recommendations:
– The WHO fetal growth charts for EFW and common ultrasound biometric measurements should be used for clinical practice and research.
– Further studies should explore the impact of fetal growth on long-term health outcomes.
– Efforts should be made to improve access to prenatal care and promote healthy maternal behaviors to optimize fetal growth.
Key Role Players:
– Researchers and scientists in the field of obstetrics and gynecology
– Healthcare providers and clinicians involved in prenatal care
– Policy makers and government officials responsible for maternal and child health programs
– Non-governmental organizations (NGOs) working on maternal and child health
Cost Items for Planning Recommendations:
– Research funding for further studies and implementation of the WHO fetal growth charts
– Training and education programs for healthcare providers on the use of the charts
– Development and dissemination of educational materials for pregnant women and their families
– Infrastructure and equipment for prenatal care facilities to support ultrasound measurements
– Monitoring and evaluation systems to assess the impact of implementing the charts on maternal and child health outcomes

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is based on a multinational prospective observational longitudinal study, which provides a strong foundation for the findings. The study included a large number of participants and utilized standardized measurement techniques. However, the study acknowledges that sample size is a limiting factor for generalization of the charts. To improve the strength of the evidence, future studies could aim to increase the sample size and include a more diverse population to enhance the generalizability of the findings.

Background: Perinatal mortality and morbidity continue to be major global health challenges strongly associated with prematurity and reduced fetal growth, an issue of further interest given the mounting evidence that fetal growth in general is linked to degrees of risk of common noncommunicable diseases in adulthood. Against this background, WHO made it a high priority to provide the present fetal growth charts for estimated fetal weight (EFW) and common ultrasound biometric measurements intended for worldwide use. Methods and Findings: We conducted a multinational prospective observational longitudinal study of fetal growth in low-risk singleton pregnancies of women of high or middle socioeconomic status and without known environmental constraints on fetal growth. Centers in ten countries (Argentina, Brazil, Democratic Republic of the Congo, Denmark, Egypt, France, Germany, India, Norway, and Thailand) recruited participants who had reliable information on last menstrual period and gestational age confirmed by crown–rump length measured at 8–13 wk of gestation. Participants had anthropometric and nutritional assessments and seven scheduled ultrasound examinations during pregnancy. Fifty-two participants withdrew consent, and 1,387 participated in the study. At study entry, median maternal age was 28 y (interquartile range [IQR] 25–31), median height was 162 cm (IQR 157–168), median weight was 61 kg (IQR 55–68), 58% of the women were nulliparous, and median daily caloric intake was 1,840 cal (IQR 1,487–2,222). The median pregnancy duration was 39 wk (IQR 38–40) although there were significant differences between countries, the largest difference being 12 d (95% CI 8–16). The median birthweight was 3,300 g (IQR 2,980–3,615). There were differences in birthweight between countries, e.g., India had significantly smaller neonates than the other countries, even after adjusting for gestational age. Thirty-one women had a miscarriage, and three fetuses had intrauterine death. The 8,203 sets of ultrasound measurements were scrutinized for outliers and leverage points, and those measurements taken at 14 to 40 wk were selected for analysis. A total of 7,924 sets of ultrasound measurements were analyzed by quantile regression to establish longitudinal reference intervals for fetal head circumference, biparietal diameter, humerus length, abdominal circumference, femur length and its ratio with head circumference and with biparietal diameter, and EFW. There was asymmetric distribution of growth of EFW: a slightly wider distribution among the lower percentiles during early weeks shifted to a notably expanded distribution of the higher percentiles in late pregnancy. Male fetuses were larger than female fetuses as measured by EFW, but the disparity was smaller in the lower quantiles of the distribution (3.5%) and larger in the upper quantiles (4.5%). Maternal age and maternal height were associated with a positive effect on EFW, particularly in the lower tail of the distribution, of the order of 2% to 3% for each additional 10 y of age of the mother and 1% to 2% for each additional 10 cm of height. Maternal weight was associated with a small positive effect on EFW, especially in the higher tail of the distribution, of the order of 1.0% to 1.5% for each additional 10 kg of bodyweight of the mother. Parous women had heavier fetuses than nulliparous women, with the disparity being greater in the lower quantiles of the distribution, of the order of 1% to 1.5%, and diminishing in the upper quantiles. There were also significant differences in growth of EFW between countries. In spite of the multinational nature of the study, sample size is a limiting factor for generalization of the charts. Conclusions: This study provides WHO fetal growth charts for EFW and common ultrasound biometric measurements, and shows variation between different parts of the world.

This was a multinational observational study approved by the WHO Research Project Review Panel (RP2) and the WHO Research Ethics Review Committee, secondarily approved by the national or local ethics review committee for each study center, and correspondingly carried out according to the Helsinki declaration on ethical principles for medical research in humans [20,21]. All women were recruited specifically for this study, gave written informed consent at inclusion, and otherwise followed their conventional antenatal care program separately from study sessions. Study measurements were revealed to the clinician when the information was thought to be of importance for the management of the pregnancy. The study protocol was published previously [20], so here we present a condensed account of the methods. The study selected participating centers from a range of ethnic and geographical settings, and intended to recruit 1,400 participants. The sample size calculation procedure was published previously [20]. The following centers participated in the study based on the proficient use of ultrasonography: Centro Rosarino de Estudios Perinatales, Rosario, Argentina; University of Campinas, Campinas, Brazil; University of Kinshasa, Kinshasa, Democratic Republic of the Congo (D. R. Congo); Rigshospitalet, Copenhagen University, Copenhagen, Denmark; Assiut University, Assiut, Egypt; Hôpital Antoine Béclère, Paris, France; University Medical Center, Hamburg-Eppendorf, Germany; All India Institute of Medical Sciences, New Delhi, India; Haukeland University Hospital, Bergen, Norway; and Khon Kaen University, Khon Kaen, Thailand. Participants without known health, environmental, and/or socioeconomic constraints were invited to participate in the study. Further inclusion criteria were used: living at an altitude lower than 1,500 m and near the study area (intended to promote compliance for the duration of the study and any possible follow-up studies); age ≥ 18 y and ≤ 40 y; body mass index (BMI) 18–30 kg/m2; singleton pregnancy; gestational age at entry between gestational week 8+0 d and 12+6 d according to reliable information on last menstrual period (LMP) and confirmed by ultrasound measurement of fetal crown–rump length; no history of chronic health problems; no long-term medication (including fertility treatment); no environmental or economic constraints likely to impede fetal growth; not smoking currently or in the previous 6 mo; no history of recurrent miscarriages; no previous preterm delivery (<37 wk) or birthweight < 2,500 g; and no evidence in the present pregnancy of congenital disease or fetal anomaly at study entry. Fetal anomalies detected during pregnancy or at birth were noted and verified postnatally. Pregnancies in which small-for-gestation-age fetuses were observed or intrauterine growth restriction was suspected were also noted. All mothers recruited were followed up until the end of the study, apart from those withdrawing consent. Women in the first trimester (before week 12+6 d of gestation) attending antenatal care clinics were approached by members of the study team and asked to participate. They were informed about the study objectives and procedures. Those who signed the consent form were enrolled in the study. After the ultrasound scan to assess agreement between gestational age based on LMP and that based on crown–rump length, they were scheduled for fetal biometry scans at monthly intervals. All infants had an anthropometric assessment after delivery, including measurement of birthweight. All pregnant women in the study were asked for a 24-h dietary recall at entry into the study (and at 28 and 36 wk of gestation) [22]. Clinically relevant conditions (e.g., hypertension, preeclampsia, and diabetes) occurring during pregnancy and childbirth were noted. Otherwise, no further procedures were added to the routine antenatal care provided at the study centers. Gestational age was confirmed by measuring the crown–rump length between gestational week 8 + 0 d and 12 + 6 d based on LMP and recorded as the average of three measurements. To acquire the crown–rump length, the midline sagittal section of the whole fetus was visualized with the fetus horizontal on the screen at 90 degrees to the angle of insonation. Gestational age was assessed by using the reference charts published by Robinson and Fleming [23]. The woman was eligible for the study provided that gestational age by crown–rump length confirmed LMP-based age within 7 d. The LMP-based age was used for the analyses. The first visit (dating scan) was between 8 + 0 and 12 + 6 wk, and subsequent visits for fetal biometry were scheduled at approximately 4-wk (±1 wk) intervals at 14, 18, 24, 28, 32, 36, and 40 wk. All scanning appointments were arranged at the time of the dating scan and study enrollment. All participants were scanned in the lateral recumbent position. The compulsory ultrasound measurements obtained at all visits included the following biometric parameters: biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), and humerus length (HL). At each examination, all measurements were obtained three times from three separately generated ultrasound images and uploaded electronically (with the associated images) to the data management system. The median of the three measurements of each parameter was used in the analyses. In addition, a full morphological evaluation (anomaly scan) was conducted at 18–24 wk following standard practice at each center. Fetuses diagnosed with any anomaly were managed according to local clinical guidelines. Their ultrasound measurements were included in the study, and the possible effect on the percentiles derived was evaluated. The following measurement techniques were used. BPD was measured as the outer–inner distance of the parietal bones in a cross-sectional view of the fetal head at the level of the thalami and cavum septi pellucidi or cerebral peduncles. The cerebellum was not included in the section. The measurement was obtained from an image with the midline echo as close as possible to the horizontal plane, 90 degrees to the ultrasound beam. HC was obtained from the same image as BPD as follows: calipers were placed on the outer borders of the occipital and frontal edges of the bone at the point of the midline of the skull, and the ellipse facility was used to follow the outer perimeter of the skull to calculate HC. AC was measured in the transverse section of the fetal abdomen that was as close as possible to circular and that included the stomach and the junction of the umbilical vein and portal sinus. The anteroposterior and transverse diameters were then measured with calipers placed on the outer borders of the body outline. The anteroposterior diameter was measured from the spine to the anterior abdominal wall, and the transverse diameter at a right angle to the anteroposterior diameter. The ellipse facility was used to calculate AC as outlined above. FL was measured from an image of the full femoral shaft in a plane close to 90 degrees to the ultrasound beam. The distal femoral epiphysis was excluded. Similarly, HL was measured from an image of the full humeral shaft in a plane close to 90 degrees to the ultrasound beam. The participating centers used identical ultrasound machines during the project (Voluson Expert E8, General Electric, Kretz Ultrasound, Zipf, Austria) equipped with two curvilinear transabdominal transducers (4–8 MHz and 1–5 MHz) and a transvaginal transducer (6–12 MHz), observing that the energy output was set so that thermal index (TI) was <1.0. The TI was automatically recorded and transmitted to the web-based data management system by the ultrasound machine. Measurement results were stored electronically, with the images together with all information collected from the mother and the perinatal outcomes. EFW was calculated by including HC, AC, and FL in Hadlock et al.’s third formula [24]. To facilitate assessment of relative fetal head size and growth, the ratios FL/HC and FL/BPD were established. The choice of participating centers was based on their proficient use of ultrasound by experienced sonographers. The sonographers participating in the study received specific training for the study and were certified as proficient under the supervision of a qualified instructor, according to a standard protocol. All the ultrasound operators had their scans assessed for quality during their early period in the project. Instruments and techniques used in all centers were standardized, i.e., equipment and training were provided to each of the measurement teams. Weight wearing light clothing was measured using a beam balance with nondetachable weights and recorded to the nearest 0.1 kg. Height of the mother was measured in the standing position using a stadiometer and recorded to the nearest millimeter. If the reading fell between two values, the lower was recorded. The 24-h diet recall assessment was carried out by a specifically trained nutritionist or nurse who asked the study participant about food and beverages consumed during the previous 24 h [22]. Further details are available elsewhere [20]. Birthweight was assessed at delivery, and neonatal morphometry carried out within 24 h according to the protocol [20]. Data were collected via a web-based data management system developed by Centro Rosarino de Estudios Perinatales, Rosario, Argentina. All data (clinical, anthropometric, nutritional, and fetal biometry measurements plus 2-D/3-D images) were stored in a central server compliant with good clinical practice. Data transmission was encrypted to assure data integrity and patient confidentiality. Access to the web system was password protected, and only authorized users had access. Data changes were documented by a complete audit trail record kept automatically by the web system (recording when, by whom, and why data were changed). Data entered into the web system were checked by the coordinating unit at Centro Rosarino de Estudios Perinatales for completeness, accuracy, reliability, and consistent intended performance. Different kinds of validation procedures were carried out (checking missing values and outliers, cross-checks, cross-time verifications among scanning appointments, and protocol compliance). Measurements and 2-D/3-D images corresponding to fetal biometry had special processing. In collaboration with General Electric Healthcare, Germany, ViewPoint software was installed at all participating centers, allowing a standard interface/procedure for scans and an automatic transfer of fetal biometry measurements/images to the web-based system. Thus, all fetal biometry measurements considered by the protocol were automatically transferred instead of being entered manually (except for D. R. Congo; there, a complete checking of values was done by the comparison of images and values entered into the web-based system). The above mentioned web-based system and procedures have been used in five previous HRP (UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction)/WHO multicenter studies and are proven to be efficient and compliant with HRP/WHO Standard Operating Procedures as well as with Title 21 CFR Part 11 of the Code of Federal Regulations, which deals with United States Food and Drug Administration guidelines on electronic records. Compared with the original protocol [20], the following aspects of the study were adjusted. Reliable information on LMP (confirmed by a measurement of crown–rump length), rather than ultrasound measured crown–rump length alone, was used as the basis for gestational age calculation for the following reasons: there is no evidence that ultrasound dating more accurately determines gestational age than a reliable LMP confirmed by crown–rump length; reliable LMP is the basis for establishing crown–rump length charts for dating; crown–rump length dating translates natural variation of size into variation of gestational age, which is not desirable for a study of growth; and LMP, not crown–rump length, is the accessible, low-cost method for gestational age assessment for all women in the world, and for the low-income areas usually the only one. The sample size calculation was based on the assumption of normality for the distribution of ultrasound measurements. However, we used quantile regression, which calculates quantiles (i.e., percentiles) directly from the observed measurements without making assumptions about the distribution. Maternal and fetal conditions occurring during pregnancy were not excluded from the analysis. The rationale for this was that the reference intervals of this study are intended primarily for clinical use and therefore should reflect the population for which they are intended as closely as possible. The pregnancy conditions (e.g., complications) that the study population experienced are those common to low-risk pregnancies around the world. Likewise, excluding all neonates below the 10th percentile of birthweight, as suggested in the protocol [20], would by definition remove the 10% of the participants at the bottom of the range (the vast majority being healthy in this low-risk cohort) and cause a corresponding distortion of the new growth charts, i.e., a substantial upward shift of all the lowest percentiles (10, 5, 2.5, and 1) in the direction of supernormal. Given the plethora of measurements, we prioritized clinical usefulness in the analyses and results presented here (e.g., EFW and common biometric measurements) and left the following for secondary studies and publications: transverse cerebellar diameter, fetal foot length, 3-D ultrasound acquisitions, maternal anthropometric measurements except height and weight, the second and third sets of dietary 24-h-recall data (at 28 and 36 wk of gestation), and newborn anthropometric measurements except birthweight. Descriptive statistics were calculated for the women’s characteristics at study entry, for mode of delivery, for birth events, and for fetal, neonatal, and maternal conditions, by country and overall. Protocol compliance was evaluated by comparing the dates of the windows of gestational age defined in the protocol with the dates of actual measurements. The ultrasound measurements were used to estimate reference curves for individual parameters (BPD, HC, AC, FL, HL, FL/HC, FL/BPD) and EFW based on Hadlock et al.’s formula 3 [24]. Reference curves were fitted using quantile regression for reference models, as described by Wei et al. [25] from the work of Koenker [26,27]. The development of reference curves has up to now in general used parametric models, based on assumptions about distribution and on transformation of the observations to normal distributions. Advances brought by computer power and by the work of Koenker and others have made it possible to estimate the distributions directly by estimating their quantiles. Quantile regression is now a well-established technique [26,27], and statistical software is available to fit quantile regression models. Quantile regression fits a function to each chosen quantile using linear programming and has the advantage of not imposing any distributional assumptions. The asymmetry and kurtosis of the fitted distributions may thus assume any form dictated by the data, even changing with gestational age. In addition, quantile regression is more robust against the influence of outliers in the data. The flexibility of the fitting and the fact that any inference drawn is entirely data-driven led us to choose quantile regression as the method for the construction of reference curves. The estimated quantiles were smoothed by polynomial functions of gestational age. Full models fitted a polynomial on gestational age for each country by including interaction terms between gestational age polynomial and country. Additive terms were included for other covariates. The models were checked by the residual analysis produced by the software. Hypotheses on the overall importance of covariates were formally tested using likelihood ratio or Wald chi-square tests. In addition, visual inspection of quantile profilers was used to assess the relevance of each covariate in explaining the variation. To compare the distributions of the different countries with the overall distribution, we used quantile–quantile plots. We calculated 95% confidence intervals for the difference between country and global EFW percentiles for particular gestational ages, using the result that the parameter estimates from quantile regression were asymptotically normally distributed [28]. Logarithms of ultrasound parameters and EFW were used for the fitting. This was done only to achieve better numerical accuracy and faster convergence of the fitting algorithm. After the fitting, the results were retransformed to the original scale. To describe growth asymmetry, we used the Bowley coefficient of asymmetry [29], based on differences of semi-quartile ranges relative to the quartile range, for the gestational ages 15 and 40 wk. Data were analyzed using SAS Software version 9.4 (SAS Institute, Cary, North Carolina, US) and JMP Pro 12 (SAS Institute, Cary, North Carolina, US).

The World Health Organization conducted a multinational longitudinal study to develop fetal growth charts for estimated fetal weight (EFW) and common ultrasound biometric measurements. The study included participants from ten countries and collected data on maternal characteristics, anthropometric measurements, nutritional assessments, and ultrasound measurements at various stages of pregnancy. The study found that there were differences in fetal growth and birthweight between countries, even after adjusting for gestational age. Factors such as maternal age, height, weight, and parity were also found to have an impact on fetal growth. The study used quantile regression to establish reference intervals for fetal biometric measurements and EFW. The resulting growth charts can be used to monitor fetal growth and improve access to maternal health by providing healthcare providers with standardized tools for assessing fetal growth and identifying potential risks.
AI Innovations Description
The recommendation from this study is to develop and implement the World Health Organization (WHO) fetal growth charts for estimated fetal weight (EFW) and common ultrasound biometric measurements. These charts can be used to monitor and track fetal growth during pregnancy, which is important for identifying potential risks and ensuring appropriate maternal care. By using these charts, healthcare providers can improve access to maternal health by accurately assessing fetal growth and making informed decisions regarding prenatal care and interventions. This innovation can help reduce perinatal mortality and morbidity, as well as contribute to the prevention of common noncommunicable diseases in adulthood.
AI Innovations Methodology
The study described in the provided text focuses on the development of fetal growth charts for estimated fetal weight (EFW) and common ultrasound biometric measurements. These charts are intended for worldwide use and aim to improve access to maternal health by providing accurate reference values for fetal growth.

To simulate the impact of recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Identify the recommendations: Based on the findings and conclusions of the study, identify specific recommendations that could improve access to maternal health. For example, recommendations could include improving prenatal care services, increasing awareness about the importance of regular ultrasound examinations, or implementing interventions to address factors affecting fetal growth.

2. Define the indicators: Determine the indicators that will be used to measure the impact of the recommendations on improving access to maternal health. These indicators could include the number of pregnant women receiving prenatal care, the frequency of ultrasound examinations, or the percentage of women with healthy fetal growth.

3. Collect baseline data: Gather baseline data on the selected indicators before implementing the recommendations. This will provide a benchmark against which the impact of the recommendations can be measured.

4. Implement the recommendations: Put the identified recommendations into action. This may involve collaborating with healthcare providers, policymakers, and other stakeholders to implement changes in healthcare systems, policies, and practices.

5. Monitor and evaluate: Continuously monitor and evaluate the implementation of the recommendations. Collect data on the selected indicators to assess the impact of the recommendations on improving access to maternal health.

6. Analyze the data: Analyze the collected data to determine the extent to which the recommendations have improved access to maternal health. This could involve comparing the baseline data with the data collected after the implementation of the recommendations.

7. Interpret the results: Interpret the results of the data analysis to understand the impact of the recommendations. Identify any trends, patterns, or significant changes in the selected indicators.

8. Adjust and refine: Based on the findings, make adjustments and refinements to the recommendations if necessary. This could involve modifying strategies, interventions, or policies to further improve access to maternal health.

9. Communicate the results: Share the results of the impact assessment with relevant stakeholders, including healthcare providers, policymakers, and the community. This will help raise awareness, inform decision-making, and facilitate further improvements in access to maternal health.

By following this methodology, it would be possible to simulate the impact of recommendations on improving access to maternal health based on the findings of the study.

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