The research implications of the selection of a gestational age estimation method

Socioeconomic and ethnic disparities in preterm births in an English maternity setting: a population-based study of 1.3 million births

BACKGROUND

Preterm birth is a major cause of infant mortality and morbidity and accounts for 7-8% of births in the UK. It is more common in women from socially deprived areas and from minority ethnic groups, but the reasons for this disparity are poorly understood. To inform interventions to improve child survival and their quality of life, this study examined the socioeconomic and ethnic inequalities in preterm births (< 37 weeks of gestation at birth) within Health Trusts in England.

METHODS

This study investigated socioeconomic and ethnic inequalities in preterm birth rates across the National Health Service (NHS) in England. The NHS in England can be split into different units known as Trusts. We visualised between-Trust differences in preterm birth rates. Health Trusts were classified into five groups based on their standard deviation (SD) variation from the average national preterm birth rate. We used modified Poisson regression to compute risk ratios (RR) and 95% confidence intervals (95% CI) with generalised estimating equations.

RESULTS

The preterm birth rate ranged from 6.8/100 births for women living in the least deprived areas to 8.8/100 births for those living in the most deprived areas. Similarly, the preterm birth rate ranged from 7.8/100 births for white women, up to 8.6/100 births for black women. Some Health Trusts had lower than average preterm birth rates in white women whilst concurrently having higher than average preterm birth rates in black and Asian women. The risk of preterm birth was higher for women living in the most deprived areas and ethnicity (Asian).

CONCLUSIONS

There was evidence of variation in rates of preterm birth by ethnic group, with some Trusts reporting below average rates in white ethnic groups whilst concurrently reporting well above average rates for women from Asian or black ethnic groups. The risk of preterm birth varied substantially at the intersectionality of maternal ethnicity and the level of socioeconomic deprivation of their residency. In the absence of other explanations, these findings suggest that even within the same Health Trust, maternity care may vary depending on the women's ethnicity and/or whether she lives in an area of high socioeconomic deprivation. Thus, social factors are likely key determinants of inequality in preterm birth rather than provision of maternity care alone.

Pregnancy complications and new-onset maternal autoimmune disease

BACKGROUND

Autoimmune diseases disproportionately impact women and female-specific aspects of reproduction are thought to play a role. We investigated the time-varying association between pregnancy complications and new-onset autoimmune disease in females during the reproductive and midlife years.

METHODS

We conducted a population-based cohort study of 1 704 553 singleton births to 1 072 445 females in Ontario, Canada (2002-17) with no pre-existing autoimmune disease. Pregnancy complications were preeclampsia, stillbirth, spontaneous preterm birth and severe small for gestational age (SGA). Royston-Parmar models were used to estimate the time-varying association between pregnancy complications and a composite of 25 autoimmune diseases from date of delivery to date of autoimmune disease diagnosis or censoring at death, loss of health insurance, or 31 March 2021. Models were adjusted for baseline socio-demographics, parity and comorbidities.

RESULTS

At 19 years (median = 10.9 years of follow-up), cumulative incidence of autoimmune disease was 3.1% in those with a pregnancy complication and 2.6% in those without complications. Adjusted hazard ratio (AHR) curves as a function of time since birth were generally L-shaped. Universally, risks were most elevated within the first 3 years after birth [at 1 year: preeclampsia AHR 1.22, 95% confidence interval (CI) 1.09-1.36; stillbirth AHR 1.36, 95% CI 0.99-1.85; spontaneous preterm birth AHR 1.30, 95% CI 1.18-1.44; severe SGA AHR 1.14, 95% CI 0.99-1.31] and plateaued but remained elevated thereafter.

CONCLUSIONS

Prior history of pregnancy complications may be an important female-specific risk factor to consider during clinical assessment of females for possible autoimmune disease to facilitate timely detection and treatment.

Young Infant Mortality Associated with Preterm and Small-for-Gestational-Age Births in Rural Bangladesh: A Prospective Cohort Study

OBJECTIVE

To assess the relative risk of mortality in infants born preterm and small for gestational age (SGA) during the first and second months of life in rural Bangladesh.

STUDY DESIGN

We analyzed data from a cohort of pregnant women and their babies in Sylhet, Bangladesh, assembled between 2011 and 2014. Community health workers visited enrolled babies up to 10 times from birth to age 59 days. Survival status was recorded at each visit. Gestational age was estimated from mother's reported last menstrual period. Birth weights were measured within 72 hours of delivery. SGA was defined using the INTERGROWTH-21st standard. We estimated unadjusted and adjusted hazard ratios (HRs) and corresponding 95% CIs for babies born preterm and SGA separately for the first and second month of life using bivariate and multivariable weighted Cox regression models.

RESULTS

The analysis included 17 643 singleton live birth babies. Compared with infants born at term-appropriate for gestational age, in both unadjusted and adjusted analyses, infants born preterm-SGA had the greatest risk of death in the first (HR 13.25, 95% CI 8.65-20.31; adjusted HR 12.05, 95% CI 7.82-18.57) and second month of life (HR 4.65, 95% CI 1.93-11.23; adjusted HR 4.1, 95% CI 1.66-10.15), followed by infants born preterm-appropriate for gestational age and term-SGA.

CONCLUSIONS

The risk of mortality in infants born preterm and/or SGA is increased and extends through the second month of life. Appropriate interventions to prevent and manage complications caused by prematurity and SGA could improve survival during and beyond the neonatal period.

Computational Approaches for Predicting Preterm Birth and Newborn Outcomes

Preterm birth (PTB) and its associated morbidities are a leading cause of infant mortality and morbidity. Accurate predictive models and a better biological understanding of PTB-associated morbidities are critical in reducing their adverse effects. Increasing availability of multimodal high-dimensional data sets with concurrent advances in artificial intelligence (AI) have created a rich opportunity to gain novel insights into PTB, a clinically complex and multifactorial disease. Here, the authors review the use of AI to analyze 3 modes of data: electronic health records, biological omics, and social determinants of health metrics.

The impact of late pregnancy dating on the detection of fetal growth restriction at term

INTRODUCTION

The inaccuracy of late pregnancy dating is often discussed, and the impact on diagnosis of fetal growth restriction is a concern. However, the magnitude and direction of this effect has not previously been demonstrated. In this study, we aimed to investigate the effect of late pregnancy dating by head circumference on the detection of late onset growth restriction, compared to first trimester crown-rump length dating.

MATERIAL AND METHODS

This was a cohort study of 14 013 pregnancies receiving obstetric care at a tertiary center over a three-year period. Universal scans were performed at 12 weeks, including crown-rump length; at 20 weeks including fetal biometry; and at 36 weeks, where biometry, umbilical artery doppler and cerebroplacental ratio were used to determine the incidence of fetal growth restriction according to the Delphi consensus. For the entire cohort, the gestational age was first calculated using T1 dating; and was then recalculated using head circumference at 20 weeks (T2 dating); and at 36 weeks (T3 dating). The incidence of fetal growth restriction following T2 and T3 dating was compared to T1 dating using four-by-four sensitivity tables.

RESULTS

When the cohort was redated from T1 to T2, the median gestation at delivery changed from 40 + 0 to 40 + 2 weeks (p < 0.001). When the cohort was redated from T1 to T3, the median gestation at delivery changed from 40 + 0 to 40 + 3 weeks (p < 0.001). T2 dating resulted in fetal growth restriction sensitivity of 80.2% with positive predictive value of 78.8% compared to T1 dating. T3 dating resulted in sensitivity of 8.6% and positive predictive value of 27.7%, respectively. The sensitivity of abnormal CPR remained high despite T2 and T3 redating; 98.0% and 89.4%, respectively.

CONCLUSIONS

Although dating at 11-14 weeks is recommended, late pregnancy dating is sometimes inevitable, and this can prolong the estimated due date by an average of two to three days. One in five pregnancies which would be classified as growth restricted if the pregnancy was dated in the first trimester, will be reclassified as nongrowth restricted following dating at 20 weeks, whereas nine out of 10 pregnancies will be reclassified as non-growth restricted with 36-week dating.

Problems with gestational age estimation by last menstrual period and ultrasound among late antenatal care attendant women in a low-resource setting in Africa, Sudan

INTRODUCTION

Accurate estimation of gestational age is essential to interpret and manage several maternal and perinatal indicators. Last menstrual period (LMP) and ultrasound are the two most common methods used for estimating gestational age. There are few published studies comparing the use of LMP and ultrasound in Sub-Saharan Africa to estimate gestational age and no studies on this topic in Sudan.

MATERIAL AND METHODS

A cross-sectional study was conducted in Gadarif Maternity Hospital in Sudan during November through December 2022. Sociodemographic information was collected, and the date of the first day of each participant's LMP was recorded. Ultrasound examinations were performed (measuring crown-rump length in early pregnancy and biparietal diameter and femur length in late pregnancy) using a 3.5-MHz electronic convex sector probe. Bland-Altman analysis was performed.

RESULTS

Four-hundred seventy-six pregnant women were enrolled. The median (interquartile range [IQR]) age and gravidity was 24.0 (20.0‒29.0) years and 2 (1‒4), respectively. There was a strong positive correlation between gestational age determined by LMP and ultrasound (r = 0.921, P < 0.001). The mean gestational age estimate according to LMP was higher than that determined by ultrasound, with a difference, on average, of 0.01 week (95% confidence interval [CI]: - 0.05, 0.07). Bland-Altman analysis showed the limits of agreement varied from - 1.36 to 1.38 weeks. A linear regression analysis showed proportional bias. The coefficient of difference of the mean was equal to 0.26 (95% CI: 0.01, 0.03, P < 0.001).

CONCLUSION

Based on our results, there was a bias in LMP-based gestational age estimates when compared with the reproducible method (ultrasound).

Leveraging External Validation Data: The Challenges of Transporting Measurement Error Parameters

Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account for such covariates: (1) standardize by these covariates or (2) iteratively model the outcome. If conditioning on a covariate for transporting the misclassification parameters induces bias of the causal effect (e.g., M-bias), the former but not the latter approach is biased. We provide proof of identification, describe estimation using parametric models, and assess performance in simulations. We also illustrate implementation to estimate the risk of preterm birth and the effect of maternal HIV infection on preterm birth. Measurement error should not be ignored and it can be addressed using external validation data via transportability methods.

Maternal health outcomes in the context of fragility: a retrospective study from Lebanon

BACKGROUND AND AIMS

The Lebanese healthcare system faces multiple challenges including limited capacities, shortage of skilled professionals, and inadequate supplies, in addition to hosting a significant number of refugees. While subsidized services are available for pregnant women, representing the majority of the refugee population in Lebanon, suboptimal access to antenatal care (ANC) and increased maternal mortality rates are still observed, especially among socioeconomically disadvantaged populations. This study aimed to review the maternal health outcomes of disadvantaged Lebanese and refugee pregnant women seeking ANC services at primary healthcare centers (PHCs) in Lebanon.

METHODS

A retrospective chart review was conducted at twenty PHCs in Lebanon, including Ministry of Public Health (MOPH) and United Nations Relief and Works Agency for Palestine refugees (UNRWA) facilities. Data was collected from medical charts of pregnant women who visited the centers between August 2018 and August 2020. Statistical analysis was performed to explore outcomes such as the number of ANC visits, delivery type, and onset of delivery, using bivariate and multivariable logistic regression models.

RESULTS

In the study, 3977 medical charts were analyzed. A multivariate logistic regression analysis, revealed that suboptimal ANC visits were more common in the Beqaa region and among women with current abortion or C-section. Syrians had reduced odds of C-sections, and Beqaa, Mount Lebanon, and South Lebanon regions had reduced odds of abortion. Suboptimal ANC visits and history of C-section increased the odds of C-section and abortion in the current pregnancy. As for preterm onset, the study showed an increased likelihood for it to occur when being Palestinian, having current C-section delivery, experiencing previous preterm onset, and enduring complications at the time of delivery.

CONCLUSION

This study suggests the need for low-cost interventions aiming at enhancing access to ANC services, especially among pregnant women in fragile settings.

Correcting for measurement error in assessing gestational age in a low-resource setting: a regression calibration approach

Introduction

Measurement error in gestational age (GA) may bias the association of GA with a health outcome. Ultrasound-based GA is considered the gold standard and is not readily available in low-resource settings. We corrected for measurement error in GA based on fundal height (FH) and date of last menstrual period (LMP) using ultrasound from the sub-cohort and adjusted for the bias in associating GA with neonatal mortality and low birth weight (< 2,500 grams, LBW).

Methods

We used data collected from 01/2015 to 09/2019 from pregnant women enrolled at two public hospitals in Siaya county, Kenya (N = 2,750). We used regression calibration to correct for measurement error in FH- and LMP-based GA accounting for maternal and child characteristics. We applied logistic regression to associate GA with neonatal mortality and low birth weight, with and without calibrating FH- and LMP-based GA.

Results

Calibration improved the precision of LMP (correlation coefficient, ρ from 0.48 to 0.57) and FH-based GA (ρ from 0.82 to 0.83). Calibrating FH/LMP-based GA eliminated the bias in the mean GA estimates. The log odds ratio that quantifies the association of GA with neonatal mortality increased by 29% (from -0.159 to -0.205) by calibrating FH-based GA and by more than twofold (from -0.158 to -0.471) by calibrating LMP-based GA.

Conclusion

Calibrating FH/LMP-based GA improved the accuracy and precision of GA estimates and strengthened the association of GA with neonatal mortality/LBW. When assessing GA, neonatal public health and clinical interventions may benefit from calibration modeling in settings where ultrasound may not be fully available.

Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age

BACKGROUND

Despite previous research findings on higher risks of stillbirth among pregnant individuals with SARS-CoV-2 infection, it is unclear whether the gestational timing of viral infection modulates this risk.

OBJECTIVE

This study aimed to examine the association between timing of SARS-CoV-2 infection during pregnancy and risk of stillbirth.

STUDY DESIGN

This retrospective cohort study used multilevel logistic regression analyses of nationwide electronic health records in the United States. Data were from 75 healthcare systems and institutes across 50 states. A total of 191,403 pregnancies of 190,738 individuals of reproductive age (15-49 years) who had childbirth between March 1, 2020 and May 31, 2021 were identified and included. The main outcome was stillbirth at ≥20 weeks of gestation. Exposures were the timing of SARS-CoV-2 infection: early pregnancy (<20 weeks), midpregnancy (21-27 weeks), the third trimester (28-43 weeks), any time before delivery, and never infected (reference).

RESULTS

We identified 2342 (1.3%) pregnancies with COVID-19 in early pregnancy, 2075 (1.2%) in midpregnancy, and 12,697 (6.9%) in the third trimester. After adjusting for maternal and clinical characteristics, increased odds of stillbirth were observed among pregnant individuals with SARS-CoV-2 infection only in early pregnancy (odds ratio, 1.75, 95% confidence interval, 1.25-2.46) and midpregnancy (odds ratio, 2.09; 95% confidence interval, 1.49-2.93), as opposed to pregnant individuals who were never infected. Older age, Black race, hypertension, acute respiratory distress syndrome or acute respiratory failure, and placental abruption were found to be consistently associated with stillbirth across different trimesters.

CONCLUSION

Increased risk of stillbirth was associated with COVID-19 only when pregnant individuals were infected during early and midpregnancy, and not at any time before the delivery or during the third trimester, suggesting the potential vulnerability of the fetus to SARS-CoV-2 infection in early pregnancy. Our findings underscore the importance of proactive COVID-19 prevention and timely medical intervention for individuals infected with SARS-CoV-2 during early and midpregnancy.

In Utero Exposure to Metals and Birth Outcomes in an Artisanal and Small-Scale Gold Mining Birth Cohort in Madre de Dios, Peru

BACKGROUND

Few birth cohorts in South America evaluate the joint effect of minerals and toxic metals on neonatal health. In Madre de Dios, Peru, mercury exposure is prevalent owing to artisanal gold mining, yet its effect on neonatal health is unknown.

OBJECTIVES

We aimed to determine whether toxic metals are associated with lower birth weight and shorter gestational age independently of antenatal care and other maternal well-being factors.

METHODS

Data are from the COhorte de NAcimiento de MAdre de Dios (CONAMAD) birth cohort, which enrolled pregnant women in Madre de Dios prior to their third trimester and obtained maternal and cord blood samples at birth. We use structural equation models (SEMs) to construct latent variables for the maternal metals environment (ME) and the fetal environment (FE) using concentrations of calcium, iron, selenium, zinc, magnesium, mercury, lead, and arsenic measured in maternal and cord blood, respectively. We then assessed the relationship between the latent variables ME and FE, toxic metals, prenatal visits, hypertension, and their effect on gestational age and birth weight.

RESULTS

Among 198 mothers successfully enrolled and followed at birth, 29% had blood mercury levels that exceeded the U.S. Centers for Disease Control and Prevention threshold of 5.8 μ g / L and 2 mothers surpassed the former 5 - μ g / dL threshold for blood lead. The current threshold value is 3.5 μ g / dL . Minerals and toxic metals loaded onto ME and FE latent variables. ME was associated with FE (β = 0.24; 95% CI: 0.05, 0.45). FE was associated with longer gestational age (β = 2.31; 95% CI: - 0.3 , 4.51) and heavier birth weight. Mercury exposure was not directly associated with health outcomes. A 1% increase in maternal blood lead shortened gestational age by 0.05 d (β = - 0.75 ; 95% CI: - 1.51 , - 0.13 ), which at the 5 - μ g / dL threshold resulted in a loss of 3.6 gestational days and 76.5 g in birth weight for newborns. Prenatal care visits were associated with improved birth outcomes, with a doubling of visits from 6 to 12 associated with 5.5 more gestational days (95% CI: 1.6, 9.4) and 319 g of birth weight (95% CI: 287.6, 350.7).

DISCUSSION

Maternal lead, even at low exposures, was associated with shorter gestation and lower birth weight. Studies that focus only on harmful exposures or nutrition may mischaracterize the dynamic maternal ME and FE. SEMs provide a framework to evaluate these complex relationships during pregnancy and reduce overcontrolling that can occur with linear regression. https://doi.org/10.1289/EHP10557.

Substantial increase in stillbirth rate during the COVID-19 pandemic: results from a population-based study in the Indian state of Bihar

INTRODUCTION

We report on the stillbirth rate (SBR) and associated risk factors for births during the COVID-19 pandemic, and change in SBR between prepandemic (2016) and pandemic periods in the Indian state of Bihar.

METHODS

Births between July 2020 and June 2021 (91.5% participation) representative of Bihar were listed. Stillbirth was defined as fetal death with gestation period of ≥7 months where the fetus did not show any sign of life. Detailed interviews were conducted for all stillbirths and neonatal deaths, and for 25% random sample of surviving live births. We estimated overall SBR, and during COVID-19 peak and non-peak periods per 1000 births. Multiple logistic regression models were run to assess risk factors for stillbirth. The change in SBR for Bihar from 2016 to 2020-2021 was estimated.

RESULTS

We identified 582 stillbirths in 30 412 births with an estimated SBR of 19.1 per 1000 births (95% CI 17.7 to 20.7); SBR was significantly higher in private facility (38.4; 95% CI 34.3 to 43.0) than in public facility (8.6; 95% CI 7.3 to 10.1) births, and for COVID-19 peak (21.2; 95% CI 19.2 to 23.4) than non-peak period (16.3; 95% CI 14.2 to 18.6) births. Pregnancies with the last pregnancy trimester during the COVID-19 peak period had 40.4% (95% CI 10.3% to 70.4%) higher SBR than those who did not. Risk factor associations for stillbirths were similar between the COVID-19 peak and non-peak periods, with gestation age of <8 months with the highest odds of stillbirth followed by referred deliveries and deliveries in private health facilities. A statistically significant increase of 24.3% and 68.9% in overall SBR and intrapartum SBR was seen between 2016 and 2020-2021, respectively.

CONCLUSIONS

This study documented an increase in SBR during the COVID-19 pandemic as compared with the prepandemic period, and the varied SBR based on the intensity of the COVID-19 pandemic and by the place of delivery.

Association between prenatal socioeconomic disadvantage, adverse birth outcomes, and inflammatory response at birth

Prenatal socioeconomic disadvantage is associated with inflammation in mid- to late-life, yet whether a pro-inflammatory phenotype is present at birth and the role of adverse birth outcomes in this pathway remains unclear. We utilized data on prenatal socioeconomic disadvantage at the individual- (i.e., mother's and father's education level, insurance type, marital status, and Women, Infants, and Children benefit receipt) and census-tract level as well as preterm (< 37 weeks gestation) and small-for-gestational-age (SGA) (i.e., < 10th percentile of sex-specific birth weight for gestational age) birth status, and assessed inflammatory markers (i.e., C-reactive protein, serum amyloid p, haptoglobin, and α-2 macroglobulin) in archived neonatal bloodspots from a Michigan population-based cohort of 1000 neonates. Continuous latent variables measuring individual- and combined individual- and neighborhood-level prenatal socioeconomic disadvantage were constructed and latent profile analysis was used to create a categorical inflammatory response variable (high versus low) based on continuous inflammatory marker levels. Structural equation models were used to estimate the total and direct effect of prenatal socioeconomic disadvantage on the inflammatory response at birth as well as indirect effect via preterm or SGA birth (among term neonates only), adjusting for mother's age, race/ethnicity, body mass index, smoking status, comorbidities, and antibiotic use/infection as well as grandmother's education level. There was a statistically significant total effect of both individual- and combined individual- and neighborhood-level prenatal socioeconomic disadvantage on high inflammatory response among all neonates as well as among term neonates only, and a positive but not statistically significant direct effect in both groups. The indirect effects via preterm and SGA birth were both negative, but not statistically significant. Our findings suggest prenatal socioeconomic disadvantage contributes to elevated neonatal inflammatory response, but via pathways outside of these adverse birth outcomes.

Hierarchical Attentive Network for Gestational Age Estimation in Low-Resource Settings

Assessing fetal development is essential to the provision of healthcare for both mothers and fetuses. In low- and middle-income countries, conditions that increase the risk of fetal growth restriction (FGR) are often more prevalent. In these regions, barriers to accessing healthcare and social services exacerbate fetal maternal health problems. One of these barriers is the lack of affordable diagnostic technologies. To address this issue, this work introduces an end-to-end algorithm applied to a low-cost, hand-held Doppler ultrasound device for estimating gestational age (GA), and by inference, FGR. The Doppler ultrasound signals used in this study were collected from 226 pregnancies (45 low birth weight at delivery) between 5 and 9 months GA by lay midwives in highland Guatemala. We designed a hierarchical deep sequence learning model with an attention mechanism to learn the normative dynamics of fetal cardiac activity in different stages of development. This resulted in a state-of-the-art GA estimation performance, with an average error of 0.79 months. This is close to the theoretical minimum for the given quantization level of one month. The model was then tested on Doppler recordings of the fetuses with low birth weight and the estimated GA was shown to be lower than the GA calculated from last menstruation. Thus, this could be interpreted as a potential sign of developmental retardation (or FGR) associated with low birth weight, and referral and intervention may be necessary.

Accuracy of serum human chorionic gonadotrophin for estimating gestational age in the first trimester of pregnancy: population-based study

This study determined the accuracy of first trimester serum Human Chorionic Gonadotrophin (HCG) for estimating gestational age (GA). We included 273,584 singleton livebirths that had a first trimester ultrasound and measured serum HCG at 4-12 weeks gestation in XXX from 2012-2018. We estimated HCG accuracy compared to known GA, within a boundary of +/- 1 week. Between 4-8 weeks gestation, sensitivity of HCG was over 88%, and specificity over 51%. However, at 9-12 weeks, sensitivity declined from 72% to 0%, and specificity rose from 86% to 100%. At all GA, the Positive Predictive Value was consistently under 42%, while Negative Predictive Values were over 96%. Within epidemiological studies in which GA is otherwise unknown, first trimester serum HCG may aid somewhat in estimating GA between 4 to 6 weeks gestation, but much less so thereafter. Thus, there remains an ongoing need for an accurate method for estimating missing GA within large datasets.

Gestational age: comparing estimation methods and live births' profile

OBJECTIVE

To identify factors associated with the definition of the gestational age (GA) estimation method recorded in the live birth certificate (LBC), and to compare the results obtained according to the method in the city of São Paulo (CSP), between 2012 and 2019.

METHODS

Cross-sectional population-based study using the Live Birth Information System. Descriptive and comparative analysis was performed according to the GA estimation method, followed by a univariate and multivariate logistic regression model to identify the predictor variables of the method used.

RESULTS

The estimation of GA by the date of the last menstrual period (LMP) (39.9%) was lower than that obtained by other methods (OM) (60.1%) - physical examination and ultrasound, between 2012-2019. LMP registration in the LBC increased with the mother's age, it was higher among women who were white, more educated and with partners, in cesarean sections and with private funding. In the logistic regression, public funding was 2.33 times more likely than private funding to use OM. The proportion of preterm infants (<37 weeks) with GA by LMP was 26.5% higher than that obtained by OM. Median birth weight was higher among preterm infants with GA estimated by LMP.

CONCLUSION

Prematurity was higher with the GA estimated by LMP in the CSP, which may indicate overestimation by this method. The source of funding was the most explanatory variable for defining the GA estimator method at the LBC. The results point to the need for caution when comparing the GA obtained by different methods.

Gestational age: comparing estimation methods and live births’ profile

RESUMO Objetivo: Identificar fatores associados à definição do método estimador da idade gestacional (IG) registrado na declaração de nascido vivo (DNV) e comparar os resultados obtidos segundo método no município de São Paulo, entre 2012 e 2019. Métodos: Estudo transversal de base populacional utilizando o Sistema de Informações sobre Nascidos Vivos. Realizou-se análise descritiva e comparativa segundo método de estimativa da IG, seguida de modelo de regressão logística uni e multivariada para identificar as variáveis preditoras do método utilizado. Resultados: A estimativa da IG pela data da última menstruação (DUM) (39,9%) foi inferior à obtida por outros métodos (OM) (60,1%) — exame físico e ultrassonografia, entre 2012-2019. O registro da DUM na DNV aumentou com a idade da mãe, foi maior entre as brancas, mais escolarizadas e com companheiro, nas cesarianas e nos partos realizados com financiamento privado. Na regressão logística, o financiamento público apresentou chance 2,33 vezes maior que o privado para uso de OM. A proporção de prematuros (<37 semanas) com IG pela DUM foi 26,5% maior do que a obtida por OM. A mediana de peso ao nascer foi maior entre prematuros com IG estimada pela DUM. Conclusão: A prematuridade foi mais elevada com a IG estimada pela DUM no MSP, o que pode indicar superestimação por este método. A fonte de financiamento foi a variável mais explicativa para definição do método estimador da IG na DNV. Os resultados apontam a necessidade de cautela ao comparar a IG obtida por métodos diferentes.

Identifying neonatal intensive care (NICU) admissions using administrative claims data

BACKGROUND

To define a method for identifying neonatal intensive care unit (NICU) admissions using administrative claims data.

METHODS

This was a retrospective cohort study using claims from Optum's de-identified Clinformatics® Data Mart Database (CDM) from 2016 -2020. We developed a definition to identify NICU admissions using a list of codes from the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), Current Procedural Terminology (CPT), and revenue codes frequently associated with NICU admissions. We compared agreement between codes using Kappa statistics and calculated positive predictive values (PPV) and 95% confidence intervals (CI).

RESULTS

On average, revenue codes (3.3%) alone identified more NICU hospitalizations compared to CPT codes alone (1.5%), whereas the use of CPT and revenue (8.9%) and CPT or revenue codes (13.7%) captured the most NICU hospitalizations, which aligns with rates of preterm birth. Gestational age alone (4.2%) and birthweight codes alone (2.0%) identified the least number of potential NICU hospitalizations. Setting CPT codes as the standard and revenue codes as the "test,", revenue codes resulted in identifying 86% of NICU admissions (sensitivity) and 97% of non-NICU admissions (specificity).

CONCLUSIONS

Using administrative data, we developed a robust definition for identifying neonatal admissions. The identified definition of NICU codes is easily adaptable, repeatable, and flexible for use in other datasets.

Delivery prediction by quantitative analysis of four steroid metabolites with liquid chromatography tandem mass spectrometry in asymptomatic pregnant women

BACKGROUND

Prediction of delivery is important for assessing due dates, providing adequate prenatal care, and suggesting appropriate interventions in preterm and post-term pregnancies. Recent metabolomic findings suggested that the temporal abundance information of metabolome can be used to predict delivery timing with high accuracy in a cohort of healthy women. However, a targeted and quantitative assay is required to further validate the clinical performance and utility of this group of metabolomic candidates in delivery prediction with a larger and independent cohort.

METHOD

LC-MS/MS quantitative assays were applied to determine the plasma concentrations of four steroid metabolites, including oestriol-16-glucuronide (E3-16-Gluc), 17-alpha-hydroxyprogesterone (17-OHP), tetrahydrodeoxycorticosterone (THDOC), and androstane-3,17-diol (A-3,17-Diol) in asymptomatic women of singleton pregnancies (≥30^th gestational weeks). Subsequent statistical analysis was conducted to assess the performance of the above candidates in delivery prediction.

RESULT

Using LC-MS/MS, four steroids were separated and quantified in 5.5 min. The coefficients of variation (CVs) of the four analytes at the lower limit of quantification ranged from 7.9% to 14.6%, with the R² values greater than 0.990 in the calibration curves. Of the 585 recruited pregnant women who ended up with spontaneous delivery, 17.1% and 82.9% of the subjects delivered within and after 7 days since plasma collection, respectively. In the receiver operator curve analysis, the gestational age-adjusted area under the curve of the combined measurements of E3-16-Gluc and 17-OHP was 0.69 (95% CI: 0.60-0.76), with the sensitivity of 87.0% (95% CI: 78.8%-92.9%) and specificity of 60.2% (95% CI: 55.7%-64.6%). Moreover, the positive and the negative predictive values were 28.3%-34.0% and 93.1%-97.4% respectively for this combined panel.

CONCLUSION

We performed analytical and clinical validation of a quantitation LC-MS/MS panel for the four steroids in the plasma of pregnant women. The steroid metabolites panel of E3-16-Gluc and 17-OHP was potentially useful for predicting delivery within one week in asymptomatic women of singleton pregnancies. Key messagesA quantitative LC-MS/MS assay for determining the plasma levels of 17-OHP, THDOC, A-3,17-Diol and E3-16-Gluc was developed and validated, in order to evaluate their predictive performance in asymptomatic delivery of singleton pregnancy. The levels of E3-16-Gluc and 17-OHP were found to be significantly elevated at the time of sampling in women that delivered within one week and their combinational testing may be potentially useful in delivery prediction.

A Bayesian latent class model for predicting gestational age in health administrative data

Health administrative data are oftentimes of limited use in epidemiological study on drug safety in pregnancy, due to lacking information on gestational age at birth (GAB). Although several studies have proposed algorithms to estimate GAB using claims database, failing to incorporate the unique distributional shape of GAB, can introduce bias in estimates and subsequent modeling. Hence, we develop a Bayesian latent class model to predict GAB. The model employs a mixture of Gaussian distributions with linear covariates within each class. This approach allows modeling heterogeneity in the population by identifying latent subgroups and estimating class-specific regression coefficients. We fit this model in a Bayesian framework conducting posterior computation with Markov Chain Monte Carlo methods. The method is illustrated with a dataset of 10,043 Rhode Island Medicaid mother-child pairs. We found that the three-class and six-class mixture specifications maximized prediction accuracy. Based on our results, Medicaid women were partitioned into three classes, featured by extreme preterm or preterm birth, preterm or" early" term birth, and" late" term birth. Obstetrical complications appeared to pose a significant influence on class-membership. Altogether, compared to traditional linear models our approach shows an advantage in predictive accuracy, because of superior flexibility in modeling a skewed response and population heterogeneity.

Application and potential of artificial intelligence in neonatal medicine

Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies. Key to the integration of AI in neonatal medicine is the understanding of its limitations and a standardised critical appraisal of AI tools. Barriers and challenges to this include the quality of datasets used, performance assessment, and appropriate external validation and clinical impact studies. Improving digital literacy amongst healthcare professionals and cross-disciplinary collaborations are needed to harness the full potential of AI to help take the next significant steps in improving neonatal outcomes for high-risk infants.

Active Treatment of Infants Born at 22-25 Weeks of Gestation in California, 2011-2018

OBJECTIVE

To determine the rate and trend of active treatment in a population-based cohort of infants born at 22-25 weeks of gestation and to examine factors associated with active treatment.

STUDY DESIGN

This observational study evaluated 8247 infants born at 22-25 weeks of gestation at hospitals in the California Perinatal Quality Care Collaborative between 2011 and 2018. Multivariable logistic regression was used to relate maternal demographic and prenatal factors, fetal characteristics, and hospital level of care to the primary outcome of active treatment.

RESULTS

Active treatment was provided to 6657 infants. The rate at 22 weeks was 19.4% and increased with each advancing week, and was significantly higher for infants born between days 4 and 6 at 22 or 23 weeks of gestation compared with those born between days 0 and 3 (26.2% and 78.3%, respectively, vs 14.1% and 65.9%, respectively; P < .001). The rate of active treatment at 23 weeks increased from 2011 to 2018 (from 64.9% to 83.4%; P < .0001) but did not change significantly at 22 weeks. Factors associated with increased odds of active treatment included maternal Hispanic ethnicity and Black race, preterm premature rupture of membranes, obstetrical bleeding, antenatal steroids, and cesarean delivery. Factors associated with decreased odds included lower gestational age and small for gestational age birth weight.

CONCLUSIONS

In California, active treatment rates at 23 weeks of gestation increased between 2011 and 2018, but rates at 22 weeks did not. At 22 and 23 weeks, rates increased during the latter part of the week. Several maternal and infant factors were associated with the likelihood of active treatment.

Perinatal Health Inequalities in the Industrial Region of Estonia: A Birth Registry-Based Study

Despite the increasing number of studies on industrially contaminated sites (ICS) and their health effects, there are very few studies on perinatal health outcomes in ICSs. In the present study, we examined the perinatal health inequalities by comparing adverse birth outcomes (ABOs) in the oil shale industry region of Ida-Viru County in Estonia with national-level figures and investigated the effects of maternal environmental and sociodemographic factors. Based on the 208,313 birth records from 2004-2018, Ida-Viru ICS has a birth weight 124.5 g lower than the average of 3544 g in Estonia. A higher prevalence of preterm birth (4.3%) and low birth weight (4.8%) in Ida-Viru ICS is found compared to 3.3% on both indicators at the national level. Multiple logistic regression analysis shows the statistically significant association of ABOs with fine particle (PM_2.5) air pollution, mother's ethnicity, and education throughout Estonia. However, in Ida-Viru ICS, the ABOs odds are remarkably higher in these characteristics except for the mother's ethnicity. Furthermore, the ABOs are associated with the residential proximity to ICS. Thus, the Ida-Viru ICS has unequally higher odds of adverse perinatal health across the environmental and sociodemographic factors. In addition to reducing the air pollutants, policy actions on social disparities are vital to address the country's unjustly higher perinatal health inequalities, especially in the Ida-Viru ICS.

A Scoping Review of Preterm Births in Sub-Saharan Africa: Burden, Risk Factors and Outcomes

Preterm births (PTB) are the leading cause of neonatal deaths, the majority of which occur in low- and middle-income countries, particularly those in Sub-Saharan Africa (SSA). Understanding the epidemiology of prematurity is an essential step towards tackling the challenge of PTB in the sub-continent. We performed a scoping review of the burden, predictors and outcomes of PTB in SSA. We searched PubMed, Embase, and three other databases for articles published from the database inception to 10 July 2021. Studies reporting the prevalence of PTB, the associated risk factors, and/or its outcomes were eligible for inclusion in this review. Our literature search identified 4441 publications, but only 181 met the inclusion criteria. Last menstrual period (LMP) was the most commonly used method of estimating gestational age. The prevalence of PTB in SSA ranged from 3.4% to 49.4%. Several risk factors of PTB were identified in this review. The most frequently reported risk factors (i.e., reported in ≥10 studies) were previous history of PTB, underutilization of antenatal care (<4 visits), premature rupture of membrane, maternal age (≤20 or ≥35 years), inter-pregnancy interval, malaria, HIV and hypertension in pregnancy. Premature babies had high rates of hospital admissions, were at risk of poor growth and development, and were also at a high risk of morbidity and mortality. There is a high burden of PTB in SSA. The true burden of PTB is underestimated due to the widespread use of LMP, an unreliable and often inaccurate method for estimating gestational age. The associated risk factors for PTB are mostly modifiable and require an all-inclusive intervention to reduce the burden and improve outcomes in SSA.

Clinical implications of first-trimester ultrasound dating in singleton pregnancies obtained through in vitro fertilization

Background

In pregnancies obtained by in-vitro fertilization (IVF) the exact day of conception is known. For that reason, IVF pregnancies are currently dated according to the day of oocytes retrieval and consequent embryo transfer. The aim of the present study is to determine whether the knowledge of the exact day of conception in IVF pregnancies is a sufficient argument against dating these pregnancies by first trimester ultrasound measurement of the crown-rump length (CRL), as it is recommended in natural conceptions.

Methods

A retrospective study was performed, including all women with singleton pregnancies conceived by IVF who underwent the first-trimester ultrasound scan for the screening of aneuploidies between January 2014 and June 2019. For each pregnancy GA was determined using two alternative methods: one based on the date of embryo transfer (GA_IVF), and one based on ultrasound measurement of CRL (GA_US). GA were compared to search for any discrepancy. The impact of pregnancy dating on obstetric outcome was evaluated.

Results

Overall, 249 women were included. Comparing GA_US and GA_IVF, a median difference of 1 [0 – 2] days emerged (p<0.001), with GA_US being in advance compared to GA_IVF. This discrepancy persisted when subgroups were analyzed comparing different IVF procedures (conventional IVF versus ICSI, cleavage versus blastocyst transfer, frozen versus fresh transfer). No impact of the dating method on obstetric outcomes was observed, being no differences in the rate of preterm birth or abnormal fetal growth.

Conclusions

In IVF pregnancies GA_US and GA_IVF are not overlapping, since GA_US is mildly greater than GA_IVF. This could be due to an anticipated ovulation and fertilization in IVF pregnancy, rather than an accelerated embryo development. For that reason, it would be appropriate to date IVF pregnancies according to GA_US, despite a known date of conception, to re-align IVF pregnancies to natural ones.

Estimation of gestational age in neonates using clavicular-pubis length on routine chest-abdomen radiographs

BACKGROUND

Neonatal diseases differ depending on gestational age and weight. In the setting of an emergency in the neonatal intensive care unit (NICU), relevant clinical information is often not available when the first neonatal radiograph is obtained. When reading an initial chest-abdomen radiograph, the paediatric radiologist needs gestational age data for best radiologic practice. A transverse diameter of the chest has been previously described to estimate gestational age (GA).

OBJECTIVES

To determine the strength of the correlation between GA/weight and clavicular-pubis length (CPL) on admission radiographs; to obtain a quadratic formula based on the correlation of CPL with GA and to demonstrate if a more simplified formula used by our group works as efficiently as the formula provided by the regression analysis.

MATERIALS AND METHODS

A retrospective study was approved by the institutional review board and informed consent was waived. The length from the medial aspect of the clavicle to the pubic bone was measured on the initial portable chest-abdomen radiographs of 260 patients admitted to the NICU in 2016. Regression analysis was performed to investigate the association between CPL and GA/birth weight.

RESULTS

One hundred eleven females and 149 males with GA between 23 and 42 weeks were evaluated. CPL was statistically associated with both GA (P<0,01) and birth weight. The estimation can be expressed with an equation of the model: GA (weeks) = (CPL in cm - 1.98)/0.42. A simplified formula: GA (weeks) = (CPL in cm) ×2+2, strongly correlates with the equation model.

CONCLUSION

In patients in whom it is not known, GA can be estimated by measuring the length between medial clavicle and symphysis pubis using the formulae we propose.

A data-driven health index for neonatal morbidities

Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.

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Traditional definitions of prematurity based on gestational age need to be updated

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Deep learning of maternal clinical data improves predictions of neonatal morbidity

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Data-driven model leverages birthweight, type of delivery and maternal race

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Accurate risk prediction can inform clinical decisions

Associations of maternal birth weight, childhood height, BMI, and change in height and BMI from childhood to pregnancy with risks of preterm delivery

BACKGROUND

It remains unknown whether maternal early life body size and changes in height and BMI from childhood to pregnancy are associated with risks of having a preterm delivery.

OBJECTIVES

We investigated whether a woman's birth weight, childhood height, BMI, and changes in height and BMI from childhood to pregnancy were associated with preterm delivery.

METHODS

We studied 47,947 nulliparous women born from 1940 to 1996 who were included in the Copenhagen School Health Records Register with information on birth weight and childhood heights and weights at ages 7 and/or 13 years. Gestational age was obtained from the Danish Birth Register, as was prepregnancy BMI, for 13,114 women. Deliveries were classified as very (22 to <32 weeks) or moderately (32 to <37 weeks) preterm. Risk ratios (RRs) and 95% CIs were estimated using binomial regression.

RESULTS

A woman's birth weight and childhood height were inversely associated with having very and moderately preterm delivery. Childhood BMI had a U-shaped association with having a very preterm delivery; at age 7 years, compared to a BMI z score of 0, the RRs were 1.31 (95% CI, 1.11-1.54) for a z score of -1 and 1.18 (95% CI, 1.01-1.38) for a z score of +1. Short stature in childhood and adulthood was associated with higher risks of very and moderately preterm delivery. Changing from a BMI at the 85th percentile at 7 years (US CDC reference) to a prepregnancy BMI of 22.5 kg/m2 was associated with RRs of 1.12 (95% CI, 0.91-1.37) and 0.88 (95% CI, 0.78-0.99) for very and moderately preterm delivery, respectively, compared to a reference woman at the 50th percentile at 7 years (22.5 kg/m2 prepregnancy BMI).

CONCLUSIONS

Maternal birth weight, childhood height, and BMI are associated with very and moderately preterm delivery, although in different patterns. Consistent short stature is associated with very and moderately preterm delivery, whereas normalizing BMI from childhood to pregnancy may reduce risks of having a very preterm delivery.

Maternal exposures to fine and ultrafine particles and the risk of preterm birth from a retrospective study in Beijing, China

Maternal exposure to fine particulate matter (PM_2.5) has been associated with increased risk of preterm birth (PTB), but evidence on particles in smaller sizes and PTB risk remains limited. In this retrospective analysis, we included birth records of 24,001 singleton live births from Haidian Maternal and Child Health Hospital in Beijing, China, 2014-2017. Concurrently, number concentrations of size-fractioned particles in size ranges of 5-560 nm (PNC_5-560) and mass concentrations of PM_2.5, black carbon (BC) and gaseous pollutants were measured from a fixed-location monitoring station in central Haidian District. Logistic regression models were used to estimate the odds ratio (OR) of air pollutants on PTB risk after controlling for temperature, relative humidity, and individual covariates (e.g., maternal age, ethnicity, gravidity, parity, gestational weight gain, fetal gender, the year and season of conception). Positive matrix factorization models were then used to apportion the sources of PNC_5-560. Among the 1062 (4.4%) PTBs, increased PTB risk was observed during the third trimester of pregnancy per 10 μg/m³ increase in PM_2.5 [OR = 1.92; 95% Confidence Interval (95% CI): 1.76, 2.09], per 1000 particles/cm³ increase in PNC_25-100 (OR = 1.09; 95% CI: 1.03, 1.15) and PNC_100-560 (OR = 1.22; 95% CI: 1.05, 1.42). Among the identified sources of PNC_5-560, emissions from gasoline and diesel vehicles were significantly associated with increased PTB risk, with ORs of 1.14 (95% CI: 1.01, 1.29) and 1.11 (95% CI: 1.04, 1.18), respectively. Exposures to other traffic-related air pollutants, such as BC and nitrogen dioxide (NO₂) were also significantly associated with increased PTB risk. Our findings highlight the importance of traffic emission reduction in urban areas.

Population-based rates, risk factors and consequences of preterm births in South-Asia and sub-Saharan Africa: A multi-country prospective cohort study

BACKGROUND

Preterm birth is the leading cause of neonatal deaths in low middle-income countries (LMICs), yet there exists a paucity of high-quality data from these countries. Most modelling estimates are based on studies using inaccurate methods of gestational age assessment. We aimed to fill this gap by measuring the population-based burden of preterm birth using early ultrasound dating in five countries in South-Asian and sub-Saharan Africa.

METHODS

We identified women early in pregnancy (<20 weeks based on last menstrual period) by home visits every 2-3 months (except in Zambia where they were identified at antenatal care clinics) in 5 research sites in South-Asia and sub-Saharan Africa between July 2012 and September 2016. Trained sonographers performed an ultrasound scan for gestational age dating. Women were enrolled if they were 8-19 weeks pregnant on ultrasound. Women <8 weeks were rescheduled for repeat scans after 4 weeks, and identified women were followed through pregnancy until 6 weeks postpartum. Site-specific rates and proportions were calculated and a logistic regression model was used to predict the risk factors of preterm birth.

RESULTS

Preterm birth rates ranged from 3.2% in Ghana to 15.7% in Pakistan. About 46% of all neonatal deaths occurred among preterm infants, 49% in South Asia and 40% in sub-Saharan Africa. Fourteen percent of all preterm infants died during the neonatal period. The mortality was 37.6% for early preterm babies (<34 weeks), 5.9% for late preterm babies (34 to <37 weeks), and 1.7% for term babies (37 to <42 weeks). Factors associated lower gestation at birth included South-Asian region (adjusted mean difference (Adj MD) = -6.2 days, 95% confidence interval (CI) = -5.5, -6.9), maternal morbidities (Adj MD = -3.4 days, 95% CI = -4.6, -2.2), multiple pregnancies (Adj MD = -17.8 days, 95% CI = -19.9,-15.8), adolescent pregnancy (Adj MD = -2.7 days, 95% CI = -3.7, -1.6) and lowest wealth quintile (Adj MD = -1.3 days, 95% CI = -2.4, -0.3).

CONCLUSIONS

Preterm birth rates are higher in South Asia than in sub-Saharan Africa and contribute to 49% and 40% of all neonatal deaths in the two regions, respectively. Adolescent pregnancy and maternal morbidities are modifiable risk factors associated with preterm birth.

Targeted newborn metabolomics: prediction of gestational age from cord blood

OBJECTIVE

Our study sought to determine whether metabolites from a retrospective collection of banked cord blood specimens could accurately estimate gestational age and to validate these findings in cord blood samples from Busia, Uganda.

STUDY DESIGN

Forty-seven metabolites were measured by tandem mass spectrometry or enzymatic assays from 942 banked cord blood samples. Multiple linear regression was performed, and the best model was used to predict gestational age, in weeks, for 150 newborns from Busia, Uganda.

RESULTS

The model including metabolites and birthweight, predicted the gestational ages within 2 weeks for 76.7% of the Ugandan cohort. Importantly, this model estimated the prevalence of preterm birth <34 weeks closer to the actual prevalence (4.67% and 4.00%, respectively) than a model with only birthweight which overestimates the prevalence by 283%.

CONCLUSION

Models that include cord blood metabolites and birth weight appear to offer improvement in gestational age estimation over birth weight alone.

Antenatal dexamethasone for late preterm birth: A multi-centre, two-arm, parallel, double-blind, placebo-controlled, randomized trial

BACKGROUND

There is currently insufficient evidence on the safety and efficacy of antenatal corticosteroids in preventing mortality and severe morbidity amongst late preterm newborns in low-resource countries.

METHODS

We conducted a double-blind, randomized trial in four hospitals in India between 26 December 2017 to 21 May 2020. Pregnant women at risk of imminent preterm birth between 34 weeks 0 days and 36 weeks 0 days of gestation were recruited. Women were randomly assigned (1:1) to a course of 6 mg intramuscular dexamethasone or an identical placebo. All trial participants, research staff and outcome assessors were masked to allocation. Primary outcomes were neonatal death, any baby death (stillbirth or neonatal death), severe neonatal respiratory distress and possible maternal bacterial infection. The study was registered with ANZCTR (ACTRN12617001494325) and CTRI (CTRI/2017/05/008721).

FINDINGS

We randomized 782 women, 391 to each arm. Neonatal death occurred in 11 of 412 liveborn babies (2.7%) in the dexamethasone group and 12 of 425 liveborn babies (2.8%) in the placebo group (RR 0.95; 95% CI 0.42-2.12). Any baby death occurred in 16 of 417 infants (3.8%) in the dexamethasone group and 19 of 432 infants (4.4%) in the placebo group (RR 0.87; 95% CI 0.45-1.67). Severe neonatal respiratory distress was infrequent in both groups (0.8% vs 0.5%; RR 1.56; 95% CI 0.26-9.29). Possible maternal bacterial infection did not differ between groups (2.3% vs. 3.8%, RR 0.60; 95% CI 0.27-1.35). Fewer neonates in the dexamethasone group required resuscitation at birth (RR 0.38, CI 0.15-0.97). Other secondary outcomes were similar in the two arms. The trial was stopped due to lower than expected prevalence of primary outcomes and slow recruitment.

INTERPRETATION

Antenatal dexamethasone did not result in a reduction in neonatal death, stillbirth or neonatal death, or severe neonatal respiratory distress in this trial. The overall trend of effects suggests that potential benefit of dexamethasone in late preterm cannot be excluded, and further trials are required.

FUNDING

This trial was primarily funded by the Bill and Melinda Gates Foundation (Grant OPP1136821). Additional support was provided by UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), Department of Sexual and Reproductive Health and Research; and Department of Maternal, Newborn, Child, Adolescent Health, and Ageing, of the World Health Organization, Geneva, Switzerland.

Methodological Considerations for Preterm Birth Research

Complications from preterm birth are a leading cause of infant mortality, with long-term implications for morbidity and quality of life of preterm infants. There are many important risk factors for preterm births however in this article, we focus on the maternal infection etiological pathway, given its significance in low-to-middle income countries. In high preterm birth settings such as sub-Saharan Africa, maternal HIV infection and antiretroviral therapy (ART) use have been associated with an increased risk of preterm births. Consequently, we highlight methodological considerations related to selection and measurement bias in preterm birth research. We further illustrate the potential impact of these biases in studies investigating the relationship between HIV/ART and preterm births. We also briefly discuss issues related to population-level estimations based on routinely collected clinical or civil registration data. We conclude by emphasizing the importance of strengthening of antenatal care services to improve quality of population data as well as optimizing current and future study designs, by taking into account the important methodological considerations described in this article.

Gestational age at term delivery and children's neurocognitive development

BACKGROUND

Preterm birth is associated with lower neurocognitive performance. However, whether children's neurodevelopment improves with longer gestations within the full-term range (37-41 weeks) is unclear. Given the high rate of obstetric intervention in the USA, it is critical to determine whether long-term outcomes differ for children delivered at each week of term.

METHODS

This secondary analysis included 39 199 live-born singleton children of women who were admitted to the hospital in spontaneous labour from the US Collaborative Perinatal Project (1959-76). At each week of term gestation, we evaluated development at 8 months using the Bayley Scales of Infant Development, 4 years using the Stanford-Binet IQ (SBIQ) domains and 7 years using the Wechsler Intelligence Scales for Children (WISC) and Wide-Range Achievement Tests (WRAT).

RESULTS

Children's neurocognitive performance improved with each week of gestation from 37 weeks, peaking at 40 or 41 weeks. Relative to those delivered at 40 weeks, children had lower neurocognitive scores at 37 and 38 weeks for all assessments except SBIQ and WISC Performance IQ. Children delivered at 39 weeks had lower Bayley Mental (β = -1.18; confidence interval -1.77, -0.58) and Psychomotor (β = -1.18; confidence interval -1.90, -0.46) scores. Results were similar for within-family analyses comparing siblings, with the addition of lower WRAT scores at 39 weeks.

CONCLUSIONS

The improvement in development scores across assessment periods indicates that each week up to 40 or 41 weeks of gestation is important for short- and long-term cognitive development, suggesting 40-41 weeks may be the ideal delivery window for optimal neurodevelopmental outcomes.

Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa

BACKGROUND

Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings.

METHODS

This study uses data from AMANHI-ACT, a prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasonography estimated GA and birth weight are available and metabolite screening data in a subset of 1318 new-borns were also available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed.

RESULTS

Overall model estimated GA had MAE of 5.2 days (95% CI 4.6-6.8), which was similar to performance in SGA, MAE 5.3 days (95% CI 4.6-6.2). GA was correctly estimated to within 1 week for 85.21% (95% CI 72.31-94.65). For preterm birth classification, AUC in ROC analysis was 98.1% (95% CI 96.0-99.0; p < 0.001). This model performed better than Iowa regression, AUC Difference 14.4% (95% CI 5-23.7; p = 0.002).

CONCLUSIONS

Machine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMICs settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation.

Simplified models to assess newborn gestational age in low-middle income countries: findings from a multicountry, prospective cohort study

INTRODUCTION

Preterm birth is the leading cause of child mortality. This study aimed to develop and validate programmatically feasible and accurate approaches to estimate newborn gestational age (GA) in low resource settings.

METHODS

The WHO Alliance for Maternal and Newborn Health Improvement (AMANHI) study recruited pregnant women from population-based cohorts in five countries (Bangladesh, Ghana, Pakistan, Tanzania and Zambia). Women <20 weeks gestation by ultrasound-based dating were enrolled. Research staff assessed newborns for: (1) anthropometry, (2) neuromuscular/physical signs and (3) feeding maturity. Machine-learning techniques were used to construct ensemble models. Diagnostic accuracy was assessed by areas under the receiver operating curve (AUC) and Bland-Altman analysis.

RESULTS

7428 liveborn infants were included (n=536 preterm, <37 weeks). The Ballard examination was biased compared with ultrasound dating (mean difference: +9 days) with 95% limits of agreement (LOA) -15.3 to 33.6 days (precision ±24.5 days). A model including 10 newborn characteristics (birth weight, head circumference, chest circumference, foot length, breast bud diameter, breast development, plantar creases, skin texture, ankle dorsiflexion and infant sex) estimated GA with no bias, 95% LOA ±17.3 days and an AUC=0.88 for classifying the preterm infant. A model that included last menstrual period (LMP) with the 10 characteristics had 95% LOA ±15.7 days and high diagnostic accuracy (AUC 0.91). An alternative simpler model including birth weight and LMP had 95% LOA of ±16.7 and an AUC of 0.88.

CONCLUSION

The best machine-learning model (10 neonatal characteristics and LMP) estimated GA within ±15.7 days of early ultrasound dating. Simpler models performed reasonably well with marginal increases in prediction error. These models hold promise for newborn GA estimation when ultrasound dating is unavailable.

Accuracy of self-reported birth outcomes relative to birth certificate data in an Internet-based prospective cohort study

BACKGROUND

The accuracy of birth outcome data provided by Internet-based cohort study participants has not been well studied.

METHODS

We compared self-reported data on birth characteristics in Pregnancy Study Online (PRESTO), an Internet-based prospective cohort study of North American pregnancy planners, with birth certificate data. At enrolment, participants were aged 21-45 years, attempting conception, and not using fertility treatment. Women completed online questionnaires during preconception, early and late pregnancy, and postpartum. We requested birth certificate data during 2014-2019 from seven health departments in states with the most participants. After restricting to singleton births, we assessed specificity, sensitivity, and agreement comparing self-reported data from postpartum questionnaires with birth certificate data for gestational age at delivery (GA) and birthweight (grams). Our primary measure of self-reported GA (weeks) was calculated as [280-(due date-birth date)]/7. We used log-binomial regression to assess predictors of agreement.

RESULTS

We linked 85% (771/909) of women in selected states. Median age of women was 30 years (range: 21-42), 84% had ≥ 16 years of education, nearly 96% were married, 12% had household incomes <$50 000, 32% were parous, and 85% identified as non-Hispanic White. Median recall interval was 6 months. Among those with self-reported data, 89% reported the same GA as the birth certificate and 98% reported GA within 1 week of the birth certificate. Self-report of preterm birth (GA < 37 weeks) agreed with information from birth certificates for 100% of women; sensitivity was 100%, and specificity was 99%. Self-reported low birthweight (<2500 grams) agreed with birth certificates for 93% of women; sensitivity and specificity were 93% and ≥99%, respectively. Predictors of poorer agreement included higher parity and longer pregnancy attempt time for GA, and lower education and longer recall interval for birthweight.

CONCLUSION

Self-reported data on GA and birthweight from an Internet-based cohort showed high accuracy compared with birth certificates.

Reliability of last menstrual period recall, an early ultrasound and a Smartphone App in predicting date of delivery and classification of preterm and post-term births

Background

A reliable expected date of delivery (EDD) is important for pregnant women in planning for a safe delivery and critical for management of obstetric emergencies. We compared the accuracy of LMP recall, an early ultrasound (EUS) and a Smartphone App in predicting the EDD in South African pregnant women. We further evaluated the rates of preterm and post-term births based on using the different measures.

Methods

This is a retrospective sub-study of pregnant women enrolled in a randomized controlled trial between October 2017-December 2019. EDD and gestational age (GA) at delivery were calculated from EUS, LMP and Smartphone App. Data were analysed using SPSS version 25. A Bland–Altman plot was constructed to determine the limits of agreement between LMP and EUS.

Results

Three hundred twenty-five pregnant women who delivered at term (≥ 37 weeks by EUS) and without pregnancy complications were included in this analysis. Women had an EUS at a mean GA of 16 weeks and 3 days). The mean difference between LMP dating and EUS is 0.8 days with the limits of agreement 31.4–30.3 days (Concordance Correlation Co-efficient 0.835; 95%CI 0.802, 0.867). The mean(SD) of the marginal time distribution of the two methods differ significantly (p = 0.00187). EDDs were < 14 days of the actual date of delivery (ADD) for 287 (88.3%;95%CI 84.4–91.4), 279 (85.9%;95%CI 81.6–89.2) and 215 (66.2%;95%CI 60.9–71.1) women for EUS, Smartphone App and LMP respectively but overall agreement between EUS and LMP was only 46.5% using a five category scale for EDD-ADD with a kappa of .22. EUS 14–24 weeks and EUS < 14 weeks predicted EDDs < 14 days of ADD in 88.1% and 79.3% of women respectively. The proportion of births classified as preterm (< 37 weeks) was 9.9% (95%CI 7.1–13.6) by LMP and 0.3% (95%CI 0.1–1.7) by Smartphone App. The proportion of post-term (> 42 weeks gestation) births was 11.4% (95%CI 8.4–15.3), 1.9% (95%CI 0.9–3.9) and 3.4% (95%CI 1.9–5.9) by LMP, EUS and Smartphone respectively.

Conclusions

EUS and Smartphone App were the most accurate to estimate the EDD in pregnant women. LMP-based dating resulted in misclassification of a significantly greater number of preterm and post-term deliveries compared to EUS and the Smartphone App.

Maternal psychosocial risk factors and child gestational epigenetic age in a South African birth cohort study

Accelerated epigenetic aging relative to chronological age has been found to be associated with higher risk of mortality in adults. However, little is known about whether and how in utero exposures might shape child gestational epigenetic age (EA) at birth. We aimed to explore associations between maternal psychosocial risk factors and deviation in child gestational EA at birth (i.e., greater or lower EA relative to chronological age) in a South African birth cohort study-the Drakenstein Child Health Study. Maternal psychosocial risk factors included trauma/stressor exposure; posttraumatic stress disorder (PTSD); depression; psychological distress; and alcohol/tobacco use. Child gestational EA at birth was calculated using an epigenetic clock previously devised for neonates; and gestational EA deviation was calculated as the residuals of the linear model between EA and chronological gestational age. Bivariate linear regression was then used to explore unadjusted associations between maternal/child risk factors and child gestational EA residuals at birth. Thereafter, a multivariable regression method was used to determine adjusted associations. Data from 271 maternal-child dyads were included in the current analysis. In the multivariable regression model, maternal PTSD was significantly and negatively associated with child gestational EA residuals at birth (β = -1.95; p = 0.018), controlling for study site, sex of the child, head circumference at birth, birthweight, mode of delivery, maternal estimated household income, body mass index (BMI) at enrolment, HIV status, anaemia, psychological distress, and prenatal tobacco or alcohol use. Given the novelty of this preliminary finding, and its potential translational relevance, further studies to delineate underlying biological pathways and to explore clinical implications of EA deviation are warranted.

Association between the use of antidepressants and the risk of preterm birth among pregnant women with depression: a retrospective cohort study in Taiwan

Our study was aimed to investigate the association between the use of antidepressants and the risk of preterm birth in pregnant women who have had perinatal depression. We extracted data from the Taiwanese National Health Insurance Research Database (NHIRD) and analyzed them using multivariate Cox proportional hazard regression models. Identified from the NHIRD, we matched 1789 women aged 18-55 years who were using antidepressants during pregnancy and 1789 women who were experiencing depression but who were not using antidepressants during pregnancy for age, index date, and medical comorbidities. We enrolled the women in our study, which we conducted using 12 years' worth of data between 2000 and 2012, and then followed up individually with them for up to 1 year to identify any occurrence of preterm birth. Results highlighted that, compared with the women with perinatal depression who were not using antidepressants during pregnancy, the women taking antidepressants had a 1.762-fold risk of preterm birth (adjusted HR=1.762, 95% CI 1.351 to 2.294, p<0.001). The use of antidepressants in women with perinatal depression may increase the risk of preterm birth. However, the decision to start, stop, or change the use of antidepressants during pregnancy requires evaluating the risks of treatment versus untreated depression for both mother and child.

The impact of optimal dating on the assessment of fetal growth

BACKGROUND

The impact of using the Intergrowth (IG) dating formulae in comparison to the commonly used Robinson dating on the evaluation of biometrics and estimated fetal weight (EFW) has not been evaluated.

METHODS

Nationwide cross-sectional study of routine fetal ultrasound biometry in low-risk pregnant women whose gestational age (GA) had been previously assessed by a first trimester CRL measurement. We compared the CRL-based GA according to the Robinson formula and the IG formula. We evaluated the fetal biometric measurements as well as the EFW taken later in pregnancy depending on the dating formula used. Mean and standard deviation of the Z scores as well as the number and percentage of cases classified as <3rd, < 10th, >90th and > 97th percentile were compared.

RESULTS

Three thousand five hundred twenty-two low-risk women with scans carried out after 18 weeks were included. There were differences of zero, one and 2 days in 642 (18.2%), 2700 (76.7%) and 180 (5%) when GA was estimated based on the Robinson or the IG formula, respectively. The biometry Z scores assessed later in pregnancy were all statistically significantly lower when the Intergrowth-based dating formula was used (p < 10- 4). Likewise, the number and percentage of foetuses classified as <3rd, < 10th, >90th and > 97th percentile demonstrated significant differences. As an example, the proportion of SGA foetuses varied from 3.46 to 4.57% (p = 0.02) and that of LGA foetuses from 17.86 to 13.4% (p < 10- 4).

CONCLUSION

The dating formula used has a quite significant impact on the subsequent evaluation of biometry and EFW. We suggest that the combined and homogeneous use of a recent dating standard, together with prescriptive growth standards established on the same low-risk pregnancies, allows an optimal assessment of fetal growth.

Preterm disparities between foreign and Swedish born mothers depend on the method used to estimate gestational age. A Swedish population-based register study

This study aims to examine whether disparities in gestational age outcomes between foreign and Swedish-born mothers are contingent on the measure used to estimate gestational age and, if so, to identify which maternal factors are associated with the discrepancy. Using population register data, we studied all singleton live births in Sweden from 1992–2012 (n = 1,317,265). Multinomial logistic regression was performed to compare gestational age outcomes classified into very (<32 weeks) and late preterm (32–36 weeks), term and post-term derived from the last menstrual period (LMP) and ultrasound estimates in foreign- and Swedish-born women. Compared to Swedish-born women, foreign-born women had similar odds of very preterm birth (OR: 0.98 [95% CI: 0.98, 1.01]) and lower odds of moderately preterm birth (OR: 0.95 [95% CI: 0.92, 0.98]) based on ultrasound, while higher risks based on LMP (respectively, OR: 1.10 [95% CI: 1.07, 1.14] and 1.09 [95% CI: 1.06, 1.13]). Conclusions on disparities in gestational age-related outcomes by mother’s country of origin depend on the method used to estimate gestational age. Except for very preterm, foreign-born women had a health advantage when gestational age is based on ultrasound, but a health disadvantage when based on LMP. Studies assessing disparities in very preterm birth by migration status are not affected by the estimation method but caution should be taken when interpreting disparities in moderately preterm and preterm birth rates.

Prenatal care and preterm birth in the Western Brazilian Amazon: A population-based study

Brazil is among the top ten countries in preterm delivery worldwide. This study assesses the factors associated with preterm birth in the Western Brazilian Amazon. A population-based cross-sectional study was held between July 2015 to June 2016 in Cruzeiro do Sul, Brazilian Amazon. A total of 1525 births were included in this analysis. Preterm birth was defined as births at gestational age < 37 weeks. A stepwise multiple logistic regression was used to identify factors associated with preterm delivery. The prevalence rate of preterm birth was 7.9% (n = 120; 95% CI: 6.5-9.3). After adjusting for confounding factors, a positive association with preterm birth was observed for pregnant women who completed less than six antenatal care visits (OR: 2.93; 95% CI: 1.89-4.56), who had a birth interval of < 18 months (OR: 2.65; 95% CI: 1.04-6.75), and who experienced bleeding (OR: 2.17; 95% CI: 1.39-3.38) and hypertension during pregnancy (OR: 1.74; 95% CI: 1.07-2.82). Factors associated with preterm birth in the Western Brazilian Amazon were mostly related to the aspects of health care provided to women, and thus could be prevented. Proper, timely, and regular antenatal care visits can help reduce adverse outcomes, such as hypertension and bleeding.

Estimating Fetal Age by Fetal Maternal Heart Rate Coupling Parameters

Beat-by-beat maternal and fetal heart couplings were reported to be evident throughout the fetal development. However, it is still unknown whether maternal-fetal heartbeat coupling parameters are associated with fetal development, and the potential interrelationships. Therefore, this study aims to investigate the associations of coupling parameters with fetal gestational age by multivariate regression models. Ten min abdominal lead-based maternal and fetal ECG signals were collected from 16 healthy pregnant women with healthy singleton pregnancies (19-32 weeks). Maternal and Fetal Heart Rate Variability (MHRV and FHRV) values as well as maternal-fetal heart rate coupling (strength, measured by A) parameters at various coupling ratios (associated with different Maternal:Fetal heartbeat ratios of 1:2, 1:3, 2:3, 2:4, 3:4, and 3:5) were calculated. Based on those features stepwise multivariate regression models were constructed by validating against the gold standard gestational age identified by crown-rump length from doppler echocardiogram. Among all models, the best model (Root Mean Square Error, RMSE=1.92) was found to be significantly (p<0.05) associated with mean fetal heart rate, mean maternal heart rate, standard deviation of maternal heart rate, λ[1:3], λ[2:3], λ[2:4]. Correlation coefficients and Bland Altman plots were constructed to statistically validate the results. The model developed based on coupling parameters only, showed the second-best performance (RMSE=2.50). Therefore, combining maternal and fetal heart rate variability parameters with maternal-fetal heart rate coupling values (rather than considering FHRV or MHRV parameters only) is found to be better associated with fetal development.Clinical relevance- This is a brief additional statement on why this might be of interest to practicing clinicians. Example: This establishes the anesthetic efficacy of 10% intraosseous injections with epinephrine to positively influence cardiovascular function.

Guideline No. 388-Determination of Gestational Age by Ultrasound

OBJECTIVE

To assist clinicians in assigning gestational age based on ultrasound biometry.

OUTCOMES

To determine whether ultrasound dating provides more accurate gestational age assessment than menstrual dating with or without the use of ultrasound. To provide maternity health care providers and researchers with evidence-based guidelines for the assignment of gestational age. To determine which ultrasound biometric parameters are superior when gestational age is uncertain. To determine whether ultrasound gestational age assessment is cost effective.

EVIDENCE

Published literature was retrieved through searches of PubMed or MEDLINE and The Cochrane Library in 2013 using appropriate controlled vocabulary and key words (gestational age, ultrasound biometry, ultrasound dating). Results were restricted to systematic reviews, randomized control trials/controlled clinical trials, and observational studies written in English. There were no date restrictions. Searches were updated on a regular basis and incorporated in the guideline to July 31, 2013. Grey (unpublished) literature was identified through searching the websites of health technology assessment and health technology-related agencies, clinical practice guideline collections, clinical trial registries, and national and international medical specialty societies.

VALUES

The quality of evidence in this document was rated using the criteria described in the Report of the Canadian Task Force on Preventive Health Care (Table 1).

BENEFITS, HARMS, AND COSTS

Accurate assignment of gestational age may reduce post-dates labour induction and may improve obstetric care through allowing the optimal timing of necessary interventions and the avoidance of unnecessary ones. More accurate dating allows for optimal performance of prenatal screening tests for aneuploidy. A national algorithm for the assignment of gestational age may reduce practice variations across Canada for clinicians and researchers. Potential harms include the possible reassignment of dates when significant fetal pathology (such as fetal growth restriction or macrosomia) result in a discrepancy between ultrasound biometric and clinical gestational age. Such reassignment may lead to the omission of appropriate-or the performance of inappropriate-fetal interventions.

SUMMARY STATEMENTS

RECOMMENDATIONS.

"I can guess the month … but beyond that, I can't tell" an exploratory qualitative study of health care provider perspectives on gestational age estimation in Rajasthan, India

BACKGROUND

Accurately estimating gestational age is essential to the provision of time-sensitive maternal and neonatal interventions, including lifesaving measures for imminent preterm birth and trimester-specific health messaging.

METHODS

We explored healthcare provider perspectives on gestational age estimation in the state of Rajasthan, India, including the methods they use (last menstrual period [LMP] dating, ultrasound, or fundal height measurement); barriers to making accurate estimates; how gestational age estimates are documented and used for clinical decision-making; and what could help improve the accuracy and use of these estimates. We interviewed 20 frontline healthcare providers and 10 key informants. Thematic network analysis guided our coding and synthesis of findings.

RESULTS

Health care providers reported that they determined gestational age using some combination of LMP, fundal height, and ultrasound. Their description of their practices showed a lack of standard protocol, varying levels of confidence in their capacity to make accurate estimates, and differing strategies for managing inconsistencies between estimates derived from different methods. Many frontline healthcare providers valued gestational age estimation more to help women prepare for childbirth than as a tool for clinical decision making. Feedback on accuracy was rare. The providers sampled could not offer ultrasound directly, and instead could only refer women to ultrasound at higher level facilities, and usually only in the second or third trimesters because of late antenatal care-seeking. Low recall among pregnant women limited the accuracy of LMP. Fundal height was heavily relied upon, despite its lack of precision.

CONCLUSION

The accuracy of gestational age estimates is influenced by factors at four levels: 1. health system (protocols to guide frontline workers, interventions that make use of gestational age, work environment, and equipment); 2. healthcare provider (technical understanding of and capacity to apply the gestational age estimation methods, communication and rapport with clients, and value assessment of gestational age); 3. client (time of first antenatal care, migration status, language, education, cognitive approach to recalling dates, and experience with biomedical services); and, 4. the inherent limitations and ease of application of the methods themselves.

Fetal brain age estimation and anomaly detection using attention-based deep ensembles with uncertainty

MRI-based brain age prediction has been widely used to characterize normal brain development, and deviations from the typical developmental trajectory are indications of brain abnormalities. Age prediction of the fetal brain remains unexplored, although it can be of broad interest to prenatal examination given the limited diagnostic tools available for assessment of the fetal brain. In this work, we built an attention-based deep residual network based on routine clinical T2-weighted MR images of 659 fetal brains, which achieved an overall mean absolute error of 0.767 weeks and R² of 0.920 in fetal brain age prediction. The predictive uncertainty and estimation confidence were simultaneously quantified from the network as markers for detecting fetal brain anomalies using an ensemble method. The novel markers overcame the limitations in conventional brain age estimation and demonstrated promising diagnostic power in differentiating several types of fetal abnormalities, including small head circumference, malformations and ventriculomegaly with the area under the curve of 0.90, 0.90 and 0.67, respectively. In addition, attention maps were derived from the network, which revealed regional features that contributed to fetal age estimation at each gestational stage. The proposed attention-based deep ensembles demonstrated superior performance in fetal brain age estimation and fetal anomaly detection, which has the potential to be translated to prenatal diagnosis in clinical practice.

Comparison of Current Swiss Fetal Biometry Reference Charts with Reference Charts from 1999. Are Fetuses Getting Bigger?

PURPOSE

 To create current fetal biometry reference ranges and to compare them with references published in 1999, from the same local area in order to generate data for secular trend in fetal size.

MATERIALS AND METHODS

 Applying the same methodology as previously published, we calculated reference ranges for biparietal diameter (BPD), occipitofrontal diameter (OFD), head circumference (HC), abdominal circumference (AC) and femur length (FL) in 7863 patients examined at the obstetric clinics in a cross-sectional, prospective study in a university setting from January 2008 to December 2014. In order to compare the new reference ranges with our previously published data, we used Z-Scores and displayed the pick-up of fetal biometry data below the 5^th and above the 95^th percentile using the previously published reference charts.

RESULTS

 The comparison of the charts showed a minimal but clinically relevant increase in mean fetal body measures (BPD, HC, AC). Applying the 1999 charts to the new dataset, we would classify only 162 of 339 fetuses (47.8 %) to be correctly below the 5^th percentile for AC and only 134 of 349 (38.4 %) fetuses were correctly below the 5^th percentile for HC. On the other hand, the 1999 charts classified 426 instead of 332 fetuses to be above the 95^th percentile for AC, which means an overestimation of 28.3 %.

CONCLUSION

 Applying a similar methodology, study collective and clinical setting, our new charts showed clinically relevant differences compared to the 1999 charts. The data suggest that within one generation fetuses are getting bigger and regular updates of fetal reference charts are needed.

Agreement between antenatal gestational age by ultrasound and clinical records at birth: A prospective cohort in the Brazilian Amazon

This study aimed to assess agreement between antenatal estimates of gestational age by ultrasound and clinical records at birth in the Brazilian Amazon. Ultrasound examinations were scheduled during the second trimester for 578 pregnant women prospectively screened at primary health care units, following a standardized protocol for image quality control. A multistage algorithm was used to assess the best estimate of gestational age during the antenatal period, considering reliability of last menstrual period (LMP) and acceptable differences in relation to ultrasound estimates derived from fetal biparietal diameter and femur length. Agreement of antenatal estimates of gestational age confirmed by ultrasound and clinical records at birth was analyzed with Bland-Altman plots and kappa coefficients (preterm and postterm births). Overall, ultrasound examinations presented high quality (>90% of satisfactory images), and were adopted as the best estimate of gestational age among 83.4% of pregnant women, confirming reliable LMP in the remaining proportion. On average, difference in gestational age between antenatal estimates and clinical records was 0.43 week (95% CI: 0.32, 0.53). Classification of preterm births had a good agreement (kappa: 0.82, p<0.001), but a poor performance was observed for postterm births (kappa: -0.06, p = 0.92). Higher differences in gestational age were noted for participants with >11 years of education and cases of caesarean deliveries. In conclusion, high-quality ultrasound images from the second trimester of pregnancy based the assessment of gestational age, while reliability of LMP was limited. Information from clinical records at birth presented an acceptable agreement on average and for classification of preterm births, which is relevant for properly interpreting perinatal outcomes. Discrepancies in caesarean deliveries may warrant further investigation.