Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: A population-based machine learning study

Background: Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population\u27s general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18-36 days, even when the best ultrasound estimates are used. The aims of this study are to improve estimates of fetal gestational age and provide personalised predictions of future growth.Methods: Using ultrasound-derived, fetal biometric data, we developed a machine learning approach to accurately estimate gestational age. The accuracy of the method is determined by reference to exactly known facts pertaining to each fetus-specifically, intervals between ultrasound visits-rather than the date of the mother\u27s last menstrual period. The data stem from a sample of healthy, well-nourished participants in a large, multicentre, population-based study, the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st). The generalisability of the algorithm is shown with data from a different and more heterogeneous population (INTERBIO-21st Fetal Study).Findings: In the context of two large datasets, we estimated gestational age between 20 and 30 weeks of gestation with 95% confidence to within 3 days, using measurements made in a 10-week window spanning the second and third trimesters. Fetal gestational age can thus be estimated in the 20-30 weeks gestational age window with a prediction interval 3-5 times better than with any previous algorithm. This will enable improved management of individual pregnancies. 6-week forecasts of the growth trajectory for a given fetus are accurate to within 7 days. This will help identify at-risk fetuses more accurately than currently possible. At population level, the higher accuracy is expected to improve fetal growth charts and population health assessments.Interpretation: Machine learning can circumvent long-standing limitations in determining fetal gestational age and future growth trajectory, without recourse to often inaccurately known information, such as the date of the mother\u27s last menstrual period. Using this algorithm in clinical practice could facilitate the management of individual pregnancies and improve population-level health. Upon publication of this study, the algorithm for gestational age estimates will be provided for research purposes free of charge via a web portal.Funding: Bill & Melinda Gates Foundation, Office of Science (US Department of Energy), US National Science Foundation, and National Institute for Health Research Oxford Biomedical Research Centre

Statistical methodology for constructing gestational age‐related charts using cross‐sectional and longitudinal data: The INTERGROWTH‐21st project as a case study

Most studies aiming to construct reference or standard charts use a cross‐sectional design, collecting one measurement per participant. Reference or standard charts can also be constructed using a longitudinal design, collecting multiple measurements per participant. The choice of appropriate statistical methodology is important as inaccurate centiles resulting from inferior methods can lead to incorrect judgements about fetal or newborn size, resulting in suboptimal clinical care. Reference or standard centiles should ideally provide the best fit to the data, change smoothly with age (eg, gestational age), use as simple a statistical model as possible without compromising model fit, and allow the computation of Z‐scores from centiles to simplify assessment of individuals and enable comparison with different populations. Significance testing and goodness‐of‐fit statistics are usually used to discriminate between models. However, these methods tend not to be useful when examining large data sets as very small differences are statistically significant even if the models are indistinguishable on actual centile plots. Choosing the best model from amongst many is therefore not trivial. Model choice should not be based on statistical considerations (or tests) alone as sometimes the best model may not necessarily offer the best fit to the raw data across gestational age. In this paper, we describe the most commonly applied methodologies available for the construction of age‐specific reference or standard centiles for cross‐sectional and longitudinal data: Fractional polynomial regression, LMS, LMST, LMSP, and multilevel regression methods. For illustration, we used data from the INTERGROWTH‐21st Project, ie, newborn weight (cross‐sectional) and fetal head circumference (longitudinal) data as examples.</p

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study

Background Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population\u27s general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18–36 days, even when the best ultrasound estimates are used. The aims of this study are to improve estimates of fetal gestational age and provide personalised predictions of future growth. Methods Using ultrasound-derived, fetal biometric data, we developed a machine learning approach to accurately estimate gestational age. The accuracy of the method is determined by reference to exactly known facts pertaining to each fetus—specifically, intervals between ultrasound visits—rather than the date of the mother\u27s last menstrual period. The data stem from a sample of healthy, well-nourished participants in a large, multicentre, population-based study, the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st). The generalisability of the algorithm is shown with data from a different and more heterogeneous population (INTERBIO-21st Fetal Study). Findings In the context of two large datasets, we estimated gestational age between 20 and 30 weeks of gestation with 95% confidence to within 3 days, using measurements made in a 10-week window spanning the second and third trimesters. Fetal gestational age can thus be estimated in the 20–30 weeks gestational age window with a prediction interval 3–5 times better than with any previous algorithm. This will enable improved management of individual pregnancies. 6-week forecasts of the growth trajectory for a given fetus are accurate to within 7 days. This will help identify at-risk fetuses more accurately than currently possible. At population level, the higher accuracy is expected to improve fetal growth charts and population health assessments. Interpretation Machine learning can circumvent long-standing limitations in determining fetal gestational age and future growth trajectory, without recourse to often inaccurately known information, such as the date of the mother\u27s last menstrual period. Using this algorithm in clinical practice could facilitate the management of individual pregnancies and improve population-level health. Upon publication of this study, the algorithm for gestational age estimates will be provided for research purposes free of charge via a web portal

Fetal growth velocity standards from the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project

Background Human growth is susceptible to damage from insults, particularly during periods of rapid growth. Identifying those periods and the normative limits that are compatible with adequate growth and development are the first key steps toward preventing impaired growth. Objective This study aimed to construct international fetal growth velocity increment and conditional velocity standards from 14 to 40 weeks’ gestation based on the same cohort that contributed to the INTERGROWTH-21st Fetal Growth Standards. Study Design This study was a prospective, longitudinal study of 4321 low-risk pregnancies from 8 geographically diverse populations in the INTERGROWTH-21st Project with rigorous standardization of all study procedures, equipment, and measurements that were performed by trained ultrasonographers. Gestational age was accurately determined clinically and confirmed by ultrasound measurement of crown-rump length at \u3c14 weeks’ gestation. Thereafter, the ultrasonographers, who were masked to the values, measured the fetal head circumference, biparietal diameter, occipitofrontal diameter, abdominal circumference, and femur length in triplicate every 5 weeks (within 1 week either side) using identical ultrasound equipment at each site (4–7 scans per pregnancy). Velocity increments across a range of intervals between measures were modeled using fractional polynomial regression. Results Peak velocity was observed at a similar gestational age: 16 and 17 weeks’ gestation for head circumference (12.2 mm/wk), and 16 weeks’ gestation for abdominal circumference (11.8 mm/wk) and femur length (3.2 mm/wk). However, velocity growth slowed down rapidly for head circumference, biparietal diameter, occipitofrontal diameter, and femur length, with an almost linear reduction toward term that was more marked for femur length. Conversely, abdominal circumference velocity remained relatively steady throughout pregnancy. The change in velocity with gestational age was more evident for head circumference, biparietal diameter, occipitofrontal diameter, and femur length than for abdominal circumference when the change was expressed as a percentage of fetal size at 40 weeks’ gestation. We have also shown how to obtain accurate conditional fetal velocity based on our previous methodological work. Conclusion The fetal skeleton and abdomen have different velocity growth patterns during intrauterine life. Accordingly, we have produced international Fetal Growth Velocity Increment Standards to complement the INTERGROWTH-21st Fetal Growth Standards so as to monitor fetal well-being comprehensively worldwide. Fetal growth velocity curves may be valuable if one wants to study the pathophysiology of fetal growth. We provide an application that can be used easily in clinical practice to evaluate changes in fetal size as conditional velocity for a more refined assessment of fetal growth than is possible at present (https://lxiao5.shinyapps.io/fetal_growth/). The application is freely available with the other INTERGROWTH-21st tools at https://intergrowth21.tghn.org/standards-tools/

Fetal growth velocity standards from the fetal growth longitudinal study of the INTERGROWTH-21 st project

Background: Human growth is susceptible to damage from insults, particularly during periods of rapid growth. Identifying those periods and the normative limits that are compatible with adequate growth and development are the first key steps toward preventing impaired growth.Objective: This study aimed to construct international fetal growth velocity increment and conditional velocity standards from 14 to 40 weeks\u27 gestation based on the same cohort that contributed to the INTERGROWTH-21st Fetal Growth Standards.Study design: This study was a prospective, longitudinal study of 4321 low-risk pregnancies from 8 geographically diverse populations in the INTERGROWTH-21st Project with rigorous standardization of all study procedures, equipment, and measurements that were performed by trained ultrasonographers. Gestational age was accurately determined clinically and confirmed by ultrasound measurement of crown-rump length at \u3c14 \u3eweeks\u27 gestation. Thereafter, the ultrasonographers, who were masked to the values, measured the fetal head circumference, biparietal diameter, occipitofrontal diameter, abdominal circumference, and femur length in triplicate every 5 weeks (within 1 week either side) using identical ultrasound equipment at each site (4-7 scans per pregnancy). Velocity increments across a range of intervals between measures were modeled using fractional polynomial regression.Results: Peak velocity was observed at a similar gestational age: 16 and 17 weeks\u27 gestation for head circumference (12.2 mm/wk), and 16 weeks\u27 gestation for abdominal circumference (11.8 mm/wk) and femur length (3.2 mm/wk). However, velocity growth slowed down rapidly for head circumference, biparietal diameter, occipitofrontal diameter, and femur length, with an almost linear reduction toward term that was more marked for femur length. Conversely, abdominal circumference velocity remained relatively steady throughout pregnancy. The change in velocity with gestational age was more evident for head circumference, biparietal diameter, occipitofrontal diameter, and femur length than for abdominal circumference when the change was expressed as a percentage of fetal size at 40 weeks\u27 gestation. We have also shown how to obtain accurate conditional fetal velocity based on our previous methodological work.Conclusion: The fetal skeleton and abdomen have different velocity growth patterns during intrauterine life. Accordingly, we have produced international Fetal Growth Velocity Increment Standards to complement the INTERGROWTH-21st Fetal Growth Standards so as to monitor fetal well-being comprehensively worldwide. Fetal growth velocity curves may be valuable if one wants to study the pathophysiology of fetal growth. We provide an application that can be used easily in clinical practice to evaluate changes in fetal size as conditional velocity for a more refined assessment of fetal growth than is possible at present (https://lxiao5.shinyapps.io/fetal_growth/). The application is freely available with the other INTERGROWTH-21st tools at https://intergrowth21.tghn.org/standards-tools/

Preterm feeding recommendations are achievable in large-scale research studies

BACKGROUND: The INTERGROWTH-21st Project aimed to produce international, prescriptive, postnatal growth standards for preterm infants born to healthy, well-nourished mothers receiving adequate antenatal care. There is little information available regarding optimal postnatal growth among uncomplicated preterm newborns. We describe how the preterm infants contributing to the standards followed evidence-based feeding recommendations. METHODS: In the Fetal Growth Longitudinal Study (FGLS), a component of the INTERGROWTH-21st Project, fetal growth was monitored by ultrasound from &lt;14 weeks’ gestation until birth in pregnancies at low risk of adverse outcomes. All preterms (≥26+0 and &lt;37+0 weeks’ gestation) were followed up during infancy. Internationally-accepted feeding recommendations for preterms, agreed by the INTERGROWTH-21st Neonatal Group, were implemented at each study site. Standardised questionnaires served to record information on their feeding practices. RESULTS: Feeding data were collected from 201 eligible preterms. The median (interquartile range, IQR) gestational age at birth was 36.0 (35.0 – 36.6) weeks. The prevalence of any breastfeeding was 82 % within 72 h of birth, 96 % at 2 weeks, 82 % at 4 months and 70 % at 8 months postnatal age. The figures for exclusive breastfeeding were 51 % within 72 h of birth, 72 % at hospital discharge, 49 % at 4 months, 38 % at 5 months and 12 % at 6 months. Complementary foods were introduced at a median (IQR) postnatal age of 6.0 (5.1 – 6.8) months. CONCLUSION: Most preterms were exclusively breastfed upon hospital discharge, and breastfeeding remained a substantial source of nutrition throughout the study. Recommendations, centred on breastfeeding, were adequately followed within the expected variation of such diverse settings

International Estimated Fetal Weight Standards of the INTERGROWTH-21(st) Project

BACKGROUND: Estimated fetal weight (EFW) and fetal biometry are complementary measures to screen for fetal growth disturbances. Our aim was to provide international EFW standards to complement the INTERGROWTH-21(st) Fetal Growth Standards that are available for use worldwide. METHODS: Women with an accurate gestational age assessment, who were enrolled in the prospective, international, multicentre, population-based, Fetal Growth Longitudinal Growth Study (FGLS) and INTERBIO-21(st) Fetal Study (FS), two of the components of the INTERGROWTH-21(st) Project, had ultrasound scans every 5 weeks from 9-14 weeks until 40 weeks&amp;apos; gestation. At each visit, blinded measurements of fetal head circumference (HC), biparietal diameter, occipitofrontal diameter, abdominal circumference (AC) and femur length were obtained using standardised methods, identical ultrasound machines and dedicated research sonographers. Birthweight measurements were taken within 12 h of birth using standardised methods, identical electronic scales and dedicated research anthropometrists. We selected live babies, without any congenital abnormalities, born within 14 days of their last ultrasound scan. As most births occurred around 40 weeks&amp;apos; gestation, we constructed a bootstrap model selection and estimation procedure based on resampling of the complete dataset under an approximately uniform distribution of birthweight, thus enriching the sample size at extremes of fetal sizes, to achieve consistent estimation across the full range of fetal weight. We then constructed reference centiles using second-degree fractional polynomial models. FINDINGS: Of the overall population, 2,404 babies were born within 14 days of their last ultrasound scan. The mean time between the last scan and birth was 7.7 days (range 0-14) and was uniformly distributed. Birthweight was best estimated as a function of AC and HC (without FL): log(EFW) = 5.084820 - 54.06633 × (AC/100)(3) - 95.80076 × (AC/100)(3)  × log(AC/100) + 3.136370 × (HC/100), All other measures, gestational age, symphysis-fundal height, amniotic fluid indices and interactions between biometric measures and gestational age were not retained in the selection process because they did not improve EFW prediction. Applying the formula to FGLS biometric data (n = 4,231), enabled gestational age-specific EFW to be constructed. At term, the EFW centiles match those of the INTERGROWTH-21(st) Newborn Size Standards but, at less than 37 weeks&amp;apos; gestation, the EFW centiles were, as expected, higher than those of babies born preterm. Comparing EFW cross-sectional values to the INTERGROWTH-21(st) Preterm Postnatal Growth Standards confirms that preterm postnatal growth is a different biological process to intrauterine growth. INTERPRETATION: We provide an assessment of EFW as an adjunct to routine ultrasound biometry from 22 to 40 weeks&amp;apos; gestation. However, we strongly encourage clinicians to evaluate fetal growth using separate biometric measures such as HC and AC, as well as EFW, so as to avoid the minimalist approach of focusing on a single value