Guideline No. 388-Determination of Gestational Age by Ultrasound

Periviability for the Ob-Gyn Hospitalist

Periviable birth refers to births occurring between 20 0/7 and 25 6/7 weeks gestational age. Management of pregnant people and neonates during this fragile time depends on the clinical status, as well as the patient's wishes. Providers should be prepared to counsel patients at the cusp of viability, being mindful of the uncertainty of outcomes for these neonates. While it is important to incorporate the data on projected morbidity and mortality into one's counseling, shared-decision making is most essential to caring for these patients and optimizing outcomes for all.

External validation of models to estimate gestational age in the second and third trimester using ultrasound: A prospective multicentre observational study

OBJECTIVES

Accurate assessment of gestational age (GA) is important at both individual and population levels. The most accurate way to estimate GA in women who book late in pregnancy is unknown. The aim of this study was to externally validate the accuracy of equations for GA estimation in late pregnancy and to identify the best equation for estimating GA in women who do not receive an ultrasound scan until the second or third trimester.

DESIGN

This was a prospective, observational cross-sectional study.

SETTING

57 prenatal care centres, France.

PARTICIPANTS

Women with a singleton pregnancy and a previous 11-14-week dating scan that gave the observed GA were recruited over an 8-week period. They underwent a standardised ultrasound examination at one time point during the pregnancy (15-43 weeks), measuring 12 foetal biometric parameters that have previously been identified as useful for GA estimation.

MAIN OUTCOME MEASURES

A total of 189 equations that estimate GA based on foetal biometry were examined and compared with GA estimation based on foetal CRL. Comparisons between the observed GA and the estimated GA were made using R2, calibration slope and intercept. RMSE, mean difference and 95% range of error were also calculated.

RESULTS

A total of 2741 pregnant women were examined. After exclusions, 2339 participants were included. In the 20 best performing equations, the intercept ranged from -0.22 to 0.30, the calibration slope from 0.96 to 1.03 and the RSME from 0.67 to 0.87. Overall, multiparameter models outperformed single-parameter models. Both the 95% range of error and mean difference increased with gestation. Commonly used models based on measurement of the head circumference alone were not amongst the best performing models and were associated with higher 95% error and mean difference.

CONCLUSIONS

We provide strong evidence that GA-specific equations based on multiparameter models should be used to estimate GA in late pregnancy. However, as all methods of GA assessment in late pregnancy are associated with large prediction intervals, efforts to improve access to early antenatal ultrasound must remain a priority.

TRIAL REGISTRATION

The proposal for this study and the corresponding methodological review was registered on PROSPERO international register of systematic reviews (registration number: CRD4201913776).

Spatiotemporal Characteristics and Risk Factors for All and Severity-Specific Preterm Births in Southern China, 2014-2021: Large Population-Based Study

BACKGROUND

The worldwide incidence of preterm births is increasing, and the risks of adverse outcomes for preterm infants significantly increase with shorter gestation, resulting in a substantial socioeconomic burden. Limited epidemiological studies have been conducted in China regarding the incidence and spatiotemporal trends of preterm births. Seasonal variations in risk indicate the presence of possible modifiable factors. Gender influences the risk of preterm birth.

OBJECTIVE

This study aims to assess the incidence rates of preterm birth, very preterm birth, and extremely preterm birth; elucidate their spatiotemporal distribution; and investigate the risk factors associated with preterm birth.

METHODS

We obtained data from the Guangdong Provincial Maternal and Child Health Information System, spanning from January 1, 2014, to December 31, 2021, pertaining to neonates with gestational ages ranging from 24 weeks to 42 weeks. The primary outcome measures assessed variations in the rates of different preterm birth subtypes over the course of the study, such as by year, region, and season. Furthermore, we examined the relationship between preterm birth incidence and per capita gross domestic product (GDP), simultaneously analyzing the contributing risk factors.

RESULTS

The analysis incorporated data from 13,256,743 live births. We identified 754,268 preterm infants and 12,502,475 full-term infants. The incidences of preterm birth, very preterm birth, and extremely preterm birth were 5.69 per 100 births, 4.46 per 1000 births, and 4.83 per 10,000 births, respectively. The overall incidence of preterm birth increased from 5.12% in 2014 to 6.38% in 2021. The incidence of extremely preterm birth increased from 4.10 per 10,000 births in 2014 to 8.09 per 10,000 births in 2021. There was a positive correlation between the incidence of preterm infants and GDP per capita. In more developed economic regions, the incidence of preterm births was higher. Furthermore, adjusted odds ratios revealed that advanced maternal age, multiple pregnancies, and male infants were associated with an increased risk of preterm birth, whereas childbirth in the autumn season was associated with a protective effect against preterm birth.

CONCLUSIONS

The incidence of preterm birth in southern China exhibited an upward trend, closely linked to enhancements in the care capabilities for high-risk pregnant women and critically ill newborns. With the recent relaxation of China's 3-child policy, coupled with a temporary surge in advanced maternal age and multiple pregnancies, the risk of preterm birth has risen. Consequently, there is a pressing need to augment public health investments aimed at mitigating the risk factors associated with preterm birth, thereby alleviating the socioeconomic burden it imposes.

Estimating Gestational Age and Prediction of Preterm Birth Using Metabolomics Biomarkers

Preterm birth (PTB) is a leading cause of morbidity and mortality in children aged under 5 years globally, especially in low-resource settings. It remains a challenge in many low-income and middle-income countries to accurately measure the true burden of PTB due to limited availability of accurate measures of gestational age (GA), first trimester ultrasound dating being the gold standard. Metabolomics biomarkers are a promising area of research that could provide tools for both early identification of high-risk pregnancies and for the estimation of GA and preterm status of newborns postnatally.

Cooking with liquefied petroleum gas or biomass and fetal growth outcomes: a multi-country randomised controlled trial

BACKGROUND

Household air pollution might lead to fetal growth restriction during pregnancy. We aimed to investigate whether a liquefied petroleum gas (LPG) intervention to reduce personal exposures to household air pollution during pregnancy would alter fetal growth.

METHODS

The Household Air Pollution Intervention Network (HAPIN) trial was an open-label randomised controlled trial conducted in ten resource-limited settings across Guatemala, India, Peru, and Rwanda. Pregnant women aged 18-34 years (9-19 weeks of gestation) were randomly assigned in a 1:1 ratio to receive an LPG stove, continuous fuel delivery, and behavioural messaging or to continue usual cooking with biomass for 18 months. We conducted ultrasound assessments at baseline, 24-28 weeks of gestation (the first pregnancy visit), and 32-36 weeks of gestation (the second pregnancy visit), to measure fetal size; we monitored 24 h personal exposures to household air pollutants during these visits; and we weighed children at birth. We conducted intention-to-treat analyses to estimate differences in fetal size between the intervention and control group, and exposure-response analyses to identify associations between household air pollutants and fetal size. This trial is registered with ClinicalTrials.gov (NCT02944682).

FINDINGS

Between May 7, 2018, and Feb 29, 2020, we randomly assigned 3200 pregnant women (1593 to the intervention group and 1607 to the control group). The mean gestational age was 14·5 (SD 3·0) weeks and mean maternal age was 25·6 (4·5) years. We obtained ultrasound assessments in 3147 (98·3%) women at baseline, 3052 (95·4%) women at the first pregnancy visit, and 2962 (92·6%) at the second pregnancy visit, through to Aug 25, 2020. Intervention adherence was high (the median proportion of days with biomass stove use was 0·0%, IQR 0·0-1·6) and pregnant women in the intervention group had lower mean exposures to particulate matter with a diameter less than 2·5 μm (PM2·5; 35·0 [SD 37·2] μg/m3vs 103·3 [97·9] μg/m3) than did women in the control group. We did not find differences in averaged post-randomisation Z scores for head circumference (0·30 vs 0·39; p=0·04), abdominal circumference (0·38 vs 0·39; p=0·99), femur length (0·44 vs 0·45; p=0·73), and estimated fetal weight or birthweight (-0·13 vs -0·12; p=0·70) between the intervention and control groups. Personal exposures to household air pollutants were not associated with fetal size.

INTERPRETATION

Although an LPG cooking intervention successfully reduced personal exposure to air pollution during pregnancy, it did not affect fetal size. Our findings do not support the use of unvented liquefied petroleum gas stoves as a strategy to increase fetal growth in settings were biomass fuels are used predominantly for cooking.

FUNDING

US National Institutes of Health and Bill & Melinda Gates Foundation.

TRANSLATIONS

For the Kinyarwanda, Spanish and Tamil translations of the abstract see Supplementary Materials section.

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.

Using simple clinical and ultrasound variables to develop a model to predict first trimester pregnancy viability

INTRODUCTION

Early prediction of pregnancies destined to miscarry will allow couples to prepare for this common but often unexpected eventuality, and clinicians to allocate finite resources. We aimed to develop a prediction model combining clinical, demographic, and sonographic data as a clinical tool to aid counselling about first trimester pregnancy outcome.

MATERIAL AND METHODS

This is a prospective, observational cohort study conducted at Queen Charlotte's and Chelsea Hospital, UK from March 2014 to May 2019. Women with confirmed intrauterine pregnancies between 5 weeks and their dating scan (11-14 weeks) were recruited. Participants attended serial ultrasound scans in the first trimester and at each visit recorded symptoms of vaginal bleeding, pelvic pain, nausea and vomiting using validated scoring tools. Pregnancies were followed up until the dating scan (11-14 weeks). Univariate and multivariate analyses were performed to predict first trimester viability. A model was developed with multivariable logistic regression, variables limited by feature selection, and bootstrapping with multiple imputation was used for internal validation.

RESULTS

1403 women were recruited and after exclusions, data were available for 1105. 160 women (14.5 %) experienced first trimester miscarriage and 945 women (85.5 %) had viable pregnancies at 11-14 weeks' gestation. The average gestational age at the initial visit (calculated from the menstrual dates) was 7 + 1 weeks (+/-12.2 days). A multivariable logistic regression model was developed to predict first trimester viability and included the variables: mean gestational sac diameter, presence of fetal heart pulsations, difference in gestational age from last menstrual period and from mean sac diameter on ultrasonography, current folic acid usage and maternal age. The model demonstrated good performance (optimism-corrected area under curve (AUC) 0.84, 95 % CI 0.81-0.87; optimism-corrected calibration slope 0.969).

CONCLUSION

We have developed and internally validated a model to predict first trimester viability with good accuracy prior to the 11-14 week dating scan, which now needs to be externally validated prior to clinical use.

Combining deep learning and intelligent biometry to extract ultrasound standard planes and assess early gestational weeks

OBJECTIVES

To develop and validate a fully automated AI system to extract standard planes, assess early gestational weeks, and compare the performance of the developed system to sonographers.

METHODS

In this three-center retrospective study, 214 consecutive pregnant women that underwent transvaginal ultrasounds between January and December 2018 were selected. Their ultrasound videos were automatically split into 38,941 frames using a particular program. First, an optimal deep-learning classifier was selected to extract the standard planes with key anatomical structures from the ultrasound frames. Second, an optimal segmentation model was selected to outline gestational sacs. Third, novel biometry was used to measure, select the largest gestational sac in the same video, and assess gestational weeks automatically. Finally, an independent test set was used to compare the performance of the system with that of sonographers. The outcomes were analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and mean similarity between two samples (mDice).

RESULTS

The standard planes were extracted with an AUC of 0.975, a sensitivity of 0.961, and a specificity of 0.979. The gestational sacs' contours were segmented with a mDice of 0.974 (error less than 2 pixels). The comparison showed that the relative error of the tool in assessing gestational weeks was 12.44% and 6.92% lower and faster (min, 0.17 vs. 16.6 and 12.63) than that of the intermediate and senior sonographers, respectively.

CONCLUSIONS

This proposed end-to-end tool allows automatic assessment of gestational weeks in early pregnancy and may reduce manual analysis time and measurement errors.

CLINICAL RELEVANCE STATEMENT

The fully automated tool achieved high accuracy showing its potential to optimize the increasingly scarce resources of sonographers. Explainable predictions can assist in their confidence in assessing gestational weeks and provide a reliable basis for managing early pregnancy cases.

KEY POINTS

• The end-to-end pipeline enabled automatic identification of the standard plane containing the gestational sac in an ultrasound video, as well as segmentation of the sac contour, automatic multi-angle measurements, and the selection of the sac with the largest mean internal diameter to calculate the early gestational week. • This fully automated tool combining deep learning and intelligent biometry may assist the sonographer in assessing the early gestational week, increasing accuracy and reducing the analyzing time, thereby reducing observer dependence.

Automatic biometry of fetal brain MRIs using deep and machine learning techniques

Linear biometric measurements on magnetic resonance images are important for the assessment of fetal brain development, which is expert knowledge dependent and laborious. This study aims to construct a segmentation-based method for automatic two-dimensional biometric measurements of fetal brain on magnetic resonance images that provides a fast and accurate measurement of fetal brain. A total of 268 volumes (5360 images) magnetic resonance images of normal fetuses were included. The automatic method involves two steps. First, the fetal brain was segmented into four parts with a deep segmentation network: cerebrum, cerebellum, and left and right lateral ventricles. Second, the measurement plane was determined, and the corresponding biometric parameters were calculated according to clinical guidelines, including cerebral biparietal diameter (CBPD), transverse cerebellar diameter (TCD), left and right atrial diameter (LAD/RAD). Pearson correlation coefficient and Bland-Altman plots were used to assess the correlation and agreement between computer-predicted values and manual measurements. Mean differences were used to evaluate the errors quantitatively. Analysis of fetal cerebral growth based on the automatic measurements was also displayed. The experiment results show that correlation coefficients for CBPD, TCD, LAD and RAD were as follows: 0.977, 0.990, 0.817, 0.719, mean differences were - 2.405 mm, - 0.008 mm, - 0.33 mm, - 0.213 mm, respectively. The correlation between the errors and gestational age was not statistically significant (p values were 0.2595, 0.0510, 0.1995, and 0.0609, respectively). The proposed automatic method for linear measurements on fetal brain MRI achieves excellent performance, which is expected to be applied in clinical practice and be helpful for prenatal diagnosis and clinical work efficiency improvement.

Directive clinique no 442 : Retard de croissance intra-utérin : Dépistage, diagnostic et prise en charge en contexte de grossesse monofœtale

OBJECTIF

Le retard de croissance intra-utérin est une complication obstétricale fréquente qui touche jusqu'à 10 % des grossesses dans la population générale et qui est le plus souvent due à une pathologie placentaire sous-jacente. L'objectif de la présente directive clinique est de fournir des déclarations sommaires et des recommandations pour appuyer un protocole clinique de dépistage, diagnostic et prise en charge du retard de croissance intra-utérin pour les grossesses à risque ou atteintes.

POPULATION CIBLE

Toutes les patientes enceintes menant une grossesse monofœtale. BéNéFICES, RISQUES ET COûTS: La mise en application des recommandations de la présente directive devrait améliorer la compétence des cliniciens quant à la détection du retard de croissance intra-utérin et à la réalisation des interventions indiquées. DONNéES PROBANTES: La littérature publiée a été colligée par des recherches effectuées jusqu'en septembre 2022 dans les bases de données PubMed, Medline, CINAHL et Cochrane Library en utilisant un vocabulaire contrôlé au moyen de termes MeSH pertinents (fetal growth retardation and small for gestational age) et de mots-clés (fetal growth, restriction, growth retardation, IUGR, FGR, low birth weight, small for gestational age, Doppler, placenta, pathology). Seuls les résultats de revues systématiques, d'essais cliniques randomisés ou comparatifs et d'études observationnelles ont été retenus. La littérature grise a été obtenue par des recherches menées dans des sites Web d'organismes s'intéressant à l'évaluation des technologies dans le domaine de la santé et d'organismes connexes, dans des collections de directives cliniques, des registres d'essais cliniques et des sites Web de sociétés de spécialité médicale nationales et internationales. MéTHODES DE VALIDATION: Les auteurs ont évalué la qualité des données probantes et la force des recommandations en utilisant le cadre méthodologique GRADE (Grading of Recommendations Assessment, Development and Evaluation). Voir l'annexe A en ligne (tableau A1 pour les définitions et tableau A2 pour l'interprétation des recommandations fortes et conditionnelles [faibles]). PROFESSIONNELS CONCERNéS: Obstétriciens, médecins de famille, infirmières, sages-femmes, spécialistes en médecine fœto-maternelle, radiologistes et autres professionnels de la santé qui prodiguent des soins aux patientes enceintes. RéSUMé POUR TWITTER: Mise à jour de la directive sur le dépistage, le diagnostic et la prise en charge du retard de croissance intra-utérin pour les grossesses à risque ou atteintes. DÉCLARATIONS SOMMAIRES: RECOMMANDATIONS: Prédiction du retard de croissance intra-utérin Prévention du retard de croissance intra-utérin Détection du retard de croissance intra-utérin Examens en cas de retard de croissance intra-utérin soupçonné Prise en charge du retard de croissance intra-utérin précoce Prise en charge du retard de croissance intra-utérin tardif Prise en charge du post-partum et consultations préconception.

Guideline No. 442: Fetal Growth Restriction: Screening, Diagnosis, and Management in Singleton Pregnancies

OBJECTIVE

Fetal growth restriction is a common obstetrical complication that affects up to 10% of pregnancies in the general population and is most commonly due to underlying placental diseases. The purpose of this guideline is to provide summary statements and recommendations to support a clinical framework for effective screening, diagnosis, and management of pregnancies that are either at risk of or affected by fetal growth restriction.

TARGET POPULATION

All pregnant patients with a singleton pregnancy.

BENEFITS, HARMS, AND COSTS

Implementation of the recommendations in this guideline should increase clinician competency to detect fetal growth restriction and provide appropriate interventions.

EVIDENCE

Published literature in English was retrieved through searches of PubMed or MEDLINE, CINAHL, and The Cochrane Library through to September 2022 using appropriate controlled vocabulary via MeSH terms (fetal growth retardation and small for gestational age) and key words (fetal growth, restriction, growth retardation, IUGR, FGR, low birth weight, small for gestational age, Doppler, placenta, pathology). Results were restricted to systematic reviews, randomized controlled trials/controlled clinical trials, and observational studies. Grey 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.

VALIDATION METHODS

The authors rated the quality of evidence and strength of recommendations using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. See online Appendix A (Table A1 for definitions and Table A2 for interpretations of strong and conditional [weak] recommendations).

INTENDED AUDIENCE

Obstetricians, family physicians, nurses, midwives, maternal-fetal medicine specialists, radiologists, and other health care providers who care for pregnant patients.

TWEETABLE ABSTRACT

Updated guidelines on screening, diagnosis, and management of pregnancies at risk of or affected by FGR.

SUMMARY STATEMENTS

RECOMMENDATIONS: Prediction of FGR Prevention of FGR Detection of FGR Investigations in Pregnancies with Suspected Fetal Growth Restriction Management of Early-Onset Fetal Growth Restriction Management of Late-Onset FGR Postpartum management and preconception counselling.

Late preterm birth and growth trajectories during childhood: a linked retrospective cohort study

BACKGROUND

Evidence suggests that accelerated postnatal growth in children is detrimental for adult cardiovascular health. It is unclear whether children born late preterm (34-36 weeks) compared to full term (≥ 39 weeks), have different growth trajectories. Our objective was to evaluate the association between gestational age groups and growth trajectories of children born between 2006-2014 and followed to 2021 in Ontario, Canada.

METHODS

We conducted a retrospective cohort study of children from singleton births in TARGet Kids! primary care network with repeated measures of weight and height/length from birth to 14 years, who were linked to health administrative databases. Piecewise linear mixed models were used to model weight (kg/month) and height (cm/month) trajectories with knots at 3, 12, and 84 months. Analyses were conducted based on chronological age.

RESULTS

There were 4423 children included with a mean of 11 weight and height measures per child. The mean age at the last visit was 5.9 years (Standard Deviation: 3.1). Generally, the more preterm, the lower the mean value of weight and height until early adolescence. Differences in mean weight and height for very/moderate preterm and late preterm compared to full term were evident until 12 months of age. Weight trajectories were similar between children born late preterm and full term with small differences from 84-168 months (mean difference (MD) -0.04 kg/month, 95% CI -0.06, -0.03). Children born late preterm had faster height gain from 0-3 months (MD 0.70 cm/month, 95% CI 0.42, 0.97) and 3-12 months (MD 0.17 cm/month, 95% CI 0.11, 0.22).

CONCLUSIONS

Compared to full term, children born late preterm had lower average weight and height from birth to 14 years, had a slightly slower rate of weight gain after 84 months and a faster rate of height gain from 0-12 months. Follow-up is needed to determine if growth differences are associated with long-term disease risk.

Ultrasound evaluation of normal rhesus macaque fetal biometry and uteroplacental hemodynamics

Nonhuman primates are important preclinical models for translational, reproductive, and developmental science. Clinical evaluation of human fetal development is performed using standard sonographic-derived fetal biometry, assessments of amniotic fluid, and uteroplacental hemodynamics. These noninvasive in utero measurements provide important information regarding fetal growth and pregnancy well-being. Abnormalities in fetal growth, amniotic fluid volume, or placental vascular function are associated with placental insufficiency and adverse perinatal outcomes including stillbirth. The fetal biometric parameters most commonly assessed are biparietal diameter, head circumference, abdominal circumference, and femur diaphysis length. Evaluation of amniotic fluid volume includes measuring the fluid in four quadrants of the uterus to generate an Amniotic Fluid Index. Measures of uteroplacental hemodynamics typically include doppler assessment of the umbilical artery and ductus venosus, but can also include interrogation of the uterine artery and umbilical vein. In this study, we compile prenatal ultrasound data of fetal biometry, amniotic fluid measurements, and uteroplacental hemodynamics obtained from pregnancy studies conducted at the Oregon National Primate Research Center. The data included are from control unperturbed pregnant animals who have not undergone in utero experimental manipulations. This is the first report of comprehensive sonographic measurements following standardized clinical obstetric protocols utilized in rhesus macaques. The outcome is a large, prenatal ultrasound resource to be used by laboratory animal researchers in future nonhuman primate pregnancy studies for antenatal assessment.

Vitamin D supplementation improved physical growth and neurologic development of Preterm Infants receiving Nesting Care in the neonatal Intensive Care Unit

OBJECTIVE

To study the effects of vitamin D supplementation on physical growth and neurologic development of very preterm infants receiving nesting intervention in the neonatal intensive care unit (NICU).

METHODS

A total of 196 preterm infants had been hospitalized in NICU with the gestational age (GA) between 28 and 32 weeks. Among them, 98 preterm infants received nesting intervention, and the other 98 cases received both nesting and vitamin D supplementation (400 IU). The interventions were continued until 36 weeks postmenstrual age (PMA). The 25(OH)D serum levels, anthropometric parameters, and Premie-Neuro (PN) scores were compared at 36 weeks PMA.

RESULTS

Higher median serum level of 25(OH)D was found in the nesting + vitamin D [38.40 ng/mL (IQR: 17.20 ~ 70.88) ng/mL] as compared to the nesting group [15.95 ng/mL (IQR: 10.80 ~ 24.30) ng/mL] at 36 weeks PMA. Besides, infants receiving combined nesting intervention and vitamin D supplementation had less proportion of vitamin D deficiency [VDD, 25(OH)D levels < 20 ng/mL] than those receiving nesting intervention alone. After intervention, the anthropometric parameters of infants, including weight, length, BMI and head circumference were improved in the nesting + vitamin D group as compared to the nesting group at 36 weeks PMA, with higher scores of neurological, movement and responsiveness.

CONCLUSIONS

Vitamin D supplementation effectively decreased the prevalence of VDD and led to higher concentrations of 25(OH)D at 36 weeks PMA. This was one more study that supported the necessity of vitamin D supplementation to improve physical growth and neurologic development of preterm-born newborns who received nesting intervention in the NICU.

Risk of stillbirth in singleton fetuses with advancing gestational age at term: A 10-year experience of late third trimester prenatal screenings of 50,000 deliveries in a referral center in northern Italy

BACKGROUND

The risk of intrauterine death (IUD) at term varies from less than one to up to three cases per 1,000 ongoing pregnancies. The cause of death is often largely undefined. Protocols and criteria to prevent and define the rates and causes of stillbirth are the subjects of important scientific and clinical debates. We examined the gestational age and rate of stillbirth at term in a 10-year period at our maternity hub to evaluate the possible favorable impact of a surveillance protocol on maternal and fetal well-being and growth.

METHODS AND FINDINGS

Our cohort included all women with singleton pregnancies resulting in early term to late term birth at our maternity hub between 2010 and 2020, with the exclusion of fetal anomalies. As per our protocol for monitoring term pregnancies, all women underwent near term to early term maternal and fetal well-being and growth surveillance. If risk factors were identified, outpatient monitoring was initiated and early- or full-term induction was indicated. Labor was induced at late term (41+0-41+4 weeks of gestation), if it did not occur spontaneously. We retrospectively collected, verified, and analyzed all cases of stillbirth at term. The incidence of stillbirth at each week of gestation, was calculated by dividing the number of stillbirths observed that week by the number of women with ongoing pregnancies in that same week. The overall rate of stillbirth per 1000 was also calculated for the entire cohort. Fetal and maternal variables were analyzed to assess the possible causes of death.

RESULTS

A total of 57,561 women were included in our study, of which 28 cases of stillbirth (overall rate, 0.48 per 1000 ongoing pregnancies; 95% CI: 0.30-0.70) were identified. The incidence of stillbirth in the ongoing pregnancies measured at 37, 38, 39, 40, and 41 weeks of gestation was 0.16, 0.30, 0.11, 0.29, and 0.0 per 1000, respectively. Only three cases occurred after 40+0 weeks of gestation. Six patients had an undetected small for gestational age fetus. The identified causes included placental conditions (n = 8), umbilical cord conditions (n = 7), and chorioamnionitis (n = 4). Furthermore, the cases of stillbirth included one undetected fetal abnormality (n = 1). The cause of fetal death remained unknown in eight cases.

CONCLUSIONS

In a referral center with an active universal screening protocol for maternal and fetal prenatal surveillance at near and early term, the rate of stillbirth was 0.48 per 1000 in singleton pregnancies at term in a large, unselected population. The highest incidence of stillbirth was observed at 38 weeks of gestation. The vast majority of stillbirth cases occurred before 39 weeks of gestation and 6 of 28 cases were SGA, and the median percentile of the remaining case was the 35th.

Can Early First Trimester Ultrasounds Correctly Determine Gestational Age Compared to Ultrasounds Performed Between 7 to 14 Weeks Gestational Age?

OBJECTIVES

The Society of Obstetricians and Gynaecologists of Canada (SOGC) recommends the use of an ultrasound performed between 7 and 14 weeks gestation to accurately predict gestational age (GA). This study aimed to assess the accuracy of earlier ultrasounds (5 to 6⁶ weeks gestation) by comparing the estimated delivery dates (EDD) in participants that had undergone both an earlier ultrasound and ultrasound completed during the standard of care timeframe.

METHODS

EDD based on crown-rump length were retrospectively reviewed for patients that had undergone an ultrasound between 5-6⁶ weeks GA versus the recommended 7-14 weeks GA at the Montfort Hospital during 2018 and 2019. The charts of 981 patients that had an ultrasound prior to 7 weeks GA and at 7-14 weeks GA were reviewed; 54 were included.

RESULTS

There was no significant difference (P = 0.307) between the EDD of the early (5-6⁶ weeks GA) and the second ultrasound (7-14 weeks GA). The first ultrasounds were then separated into very early (5-5⁶ weeks GA) and early (6-6⁶ weeks GA) and compared. No significant differences (P = 0.579) were found. Similarly, no difference was found between the EDD of the early (6-6⁶ weeks GA) and standard of care timing (P = 0.324).

CONCLUSION

These results show no significant difference in accurately determining the EDD between ultrasounds completed at the early and standard of care time points. This could result in cost-saving benefits by foregoing a repeat ultrasound; however, further research is required prior to applying these findings in clinical settings.

Guideline No. 432a: Cervical Ripening and Induction of Labour - General Information

OBJECTIVE

This guideline presents evidence and recommendations for cervical ripening and induction of labour. It aims to provide information to birth attendants and pregnant individuals on optimal perinatal care while avoiding unnecessary obstetrical intervention.

TARGET POPULATION

All pregnant patients.

BENEFITS, HARMS, AND COSTS

Consistent interprofessional use of the guideline, appropriate equipment, and trained professional staff enhance safe intrapartum care. Pregnant individuals and their support person(s) should be informed of the benefits and risks of induction of labour.

EVIDENCE

Literature published to March 2022 was reviewed. PubMed, CINAHL, and the Cochrane Library were used to search for systematic reviews, randomized controlled trials, and observational studies on cervical ripening and induction of labour. Grey (unpublished) literature was identified by 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.

VALIDATION METHODS

The authors rated the quality of evidence and strength of recommendations using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. See online Appendix A (Tables A1 for definitions and A2 for interpretations of strong and conditional [weak] recommendations).

INTENDED AUDIENCE

All providers of obstetrical care.

RECOMMANDATIONS

Directive clinique no 432a : Maturation cervicale et déclenchement artificiel du travail - Information générale

OBJECTIF

Présenter des données probantes et des recommandations sur la maturation cervicale et le déclenchement artificiel du travail. Fournir de l'information aux professionnels accoucheurs et aux personnes enceintes sur les soins périnataux optimaux et la prévention des interventions obstétricales inutiles.

POPULATION CIBLE

Toutes les patientes enceintes. BéNéFICES, RISQUES ET COûTS: La mise en application interprofessionnelle et cohérente de la présente directive, l'équipement adéquat et le personnel compétent améliorent la sécurité des soins per partum. Les personnes enceintes et leurs personnes de soutien doivent être informées des risques et bénéfices du déclenchement artificiel du travail. DONNéES PROBANTES: La littérature publiée jusqu'en mars 2022 a été passée en revue. Une recherche a été effectuée dans les bases de données PubMed, CINAHL et Cochrane Library pour répertorier des revues systématiques, des essais cliniques randomisés et des études observationnelles sur la maturation cervicale et le déclenchement artificiel du travail. La littérature grise (non publiée) a été obtenue à l'aide de recherches menées dans des sites Web d'organismes s'intéressant à l'évaluation des technologies dans le domaine de la santé et d'organismes connexes, dans des collections de directives cliniques, des registres d'essais cliniques et des sites Web de sociétés de spécialité médicale nationales et internationales. MéTHODES DE VALIDATION: Les auteurs ont évalué la qualité des données probantes et la force des recommandations en utilisant le cadre méthodologique GRADE (Grading of Recommendations Assessment, Development and Evaluation). Voir l'annexe A en ligne (tableau A1 pour les définitions et tableau A2 pour l'interprétation des recommandations fortes et conditionnelles [faibles]). PROFESSIONNELS CONCERNéS: Tous les fournisseurs de soins obstétricaux.

RECOMMANDATIONS

Midwife-Led Ultrasound Scanning to Date Pregnancy in Malawi: Development of a Novel Training Program

The use of ultrasound to determine gestational age is fundamental to the optimum management of pregnancy and is recommended for all women by the World Health Organization. However, this modality remains unavailable to many women in low-income countries where trained practitioners are scarce. Although previous initiatives have demonstrated efficacy in training midwives and technicians to perform antenatal ultrasound, these programs have often been too long and too complex to be realistic within the specific constraints of this context, highlighting the need for a novel and pragmatic approach. We describe the development and piloting of a bespoke course to teach midwives 3 fundamental components of early antenatal ultrasound scanning: (1) to identify the number of fetuses, (2) to confirm fetal viability, and (3) to determine gestational age. Having established that 5 days is insufficient, we propose that the minimum duration required to train ultrasound-naive midwives to competency is 10 days. Our completed program therefore consists of one and one-half days of didactic teaching, followed by 8 and one-half days of supervised hands-on practical training in which trainees are assessed on their skills. This package has subsequently been successfully implemented across 6 sites in Malawi, where 28 midwives have achieved competency. By describing the processes involved in our cross-continental collaboration, we explain how unexpected challenges helped shape and improve our program, demonstrating the value of preimplementation piloting and a pragmatic and adaptive approach.

Second and third trimester estimation of gestational age using ultrasound or maternal symphysis-fundal height measurements: A systematic review

Many vulnerable women seek antenatal care late in pregnancy. How should gestational age be determined? We examine all available studies estimating GA >20 weeks. Ultrasound is much better than fundal height, and using cerebellar measurement appears to be the most accurate.

Linked article: This article is commented on by Philip J. Steer, pp. 1459 in this issue. To view this minicommentary visit https://doi.org/10.1111/1471‐0528.17127 .

Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa

Background

Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature.

Methods

A subset of 330 pairs of heel prick dried blood spot samples were shipped on dry ice and in ambient temperature from field sites in Tanzania, Bangladesh and Pakistan to laboratory in Iowa (USA). We evaluated impact on recovery of analytes of shipment temperature, developed and evaluated models for predicting gestational age using a limited set of metabolic screening analytes after excluding 17 analytes that were impacted by shipment conditions of a total of 44 analytes.

Results

With the machine learning model using all the analytes, samples shipped in dry ice yielded a Root Mean Square Error (RMSE) of 1.19 weeks compared to 1.58 weeks for samples shipped in ambient temperature. Out of the 44 screening analytes, recovery of 17 analytes was significantly different between the two shipment methods and these were excluded from further machine learning model development. The final model, restricted to stable analytes provided a RMSE of 1.24 (95% confidence interval (CI) = 1.10-1.37) weeks for samples shipped on dry ice and RMSE of 1.28 (95% CI = 1.15-1.39) for samples shipped at ambient temperature. Analysis for discriminating preterm births (gestational age <37 weeks), yielded an area under curve (AUC) of 0.76 (95% CI = 0.71-0.81) for samples shipped on dry ice and AUC of 0.73 (95% CI = 0.67-0.78) for samples shipped in ambient temperature.

Conclusions

In this study, we demonstrate that machine learning algorithms developed using a sub-set of newborn screening analytes which are not sensitive to shipment at ambient temperature, can accurately provide estimates of gestational age comparable to those from published regression models from North America using all analytes. If validated in larger samples especially with more newborns <34 weeks, this technology could substantially facilitate implementation in LMICs.

Every Mother and Every Fetus Matters: A Positive Pregnant Test = Multiple Offerings of Reproductive Risk Screening for personal, family, and specific obstetrical-fetal conditions

Structured OBJECTIVE: The requirement and need for a focused 'pregnant person -centered' antenatal care process with time for informed consent and shared decision making are important for optimal antenatal care. This commentary focuses on the evidenced -based screening test options and timing as part of the overall 'pregnant person-centered' preconception and antenatal care journey.

METHODS

A structured quality improvement (QI) review (Squire 2.0) was undertaken to examine the appropriate reproductive screening process in the periods of preconception and during pregnancy.

RESULTS

First, evaluated the broader antenatal care structure which, second, enabled the directed reproductive risk screening processes to be offered within an informed consent process. Four international pre-conception and antenatal evidenced-based consensus would routinely offer specific gestational age reproductive risk screening elements: totaling 21 screening elements (preconception 3; 1st trimester 9; 2nd trimester 3; 3rd trimester 4; intrapartum 1; postpartum 1).

CONCLUSION

The best evidenced-based opportunity for comprehensive and collaborative antenatal care with appropriate screening elements requires: single national access healthcare system; expert evidenced-based guideline creation; collaborative maternity care providers based for risk assessment, triage, and management; pregnant person (women) centered care model of maternity care; clearly identified evidenced-based gestational age directed screening elements; international pre-conception and antenatal guideline consensus.

Maternal weight gain and pregnancy outcomes in twin gestations

BACKGROUND

Data on the optimal gestational weight gain in twin pregnancies are limited. As a result, the Institute of Medicine currently provides only provisional recommendations on gestational weight gain in this population.

OBJECTIVE

This study aimed to identify the optimal range of gestational weight gain in twin pregnancies and to estimate the association between inappropriate gestational weight gain and adverse pregnancy outcomes.

STUDY DESIGN

This was a retrospective cohort study of all women with twin pregnancies that were followed up in a single, tertiary center between 2000 and 2014. We used 2 approaches to identify the optimal range of gestational weight gain: a statistical approach (the interquartile range of gestational weight gain in low-risk pregnancies with normal outcomes) and an outcome-based approach (by identifying thresholds of gestational weight gain below or above which the rate of adverse outcomes increases). The primary outcome was preterm birth. Associations of gestational weight gain below or above the normal range with the study outcomes were estimated using logistic regression analysis and were expressed as adjusted odds ratio with 95% confidence intervals. These associations were stratified by prepregnancy body mass index group.

RESULTS

A total of 1274 women with twin pregnancies met the study criteria: 43 were classified as underweight, 777 were normal weight, 278 were overweight, and 176 were obese. Our estimates of the optimal gestational weight gain range were similar to those recommended by the Institute of Medicine except for the obese category, in which our optimal gestational weight gain range at 37 weeks (9.3-16.3 kg) was lower than in the provisional Institute of Medicine recommendations (11.3-19.1 kg). Nearly half of our cohort experienced inappropriate gestational weight gain: 30% (n=381) gained weight below and 17% (n=216) gained weight above current Institute of Medicine recommendations. In the normal weight group, gestational weight gain below recommendations was associated with an increased risk of preterm birth and birthweight at the <10th centile and with a reduction in the risk of hypertensive disorders, whereas gestational weight gain above recommendations was associated with an increased risk of hypertensive disorders and a reduction in the risk of birthweight at the <10th centile. Associations were less consistent in the overweight and obese groups.

CONCLUSION

These findings identify gestational weight gain as a potentially modifiable risk factor for preterm birth and other pregnancy complications in twin gestations. Further prospective studies are needed to determine whether interventions aimed at optimizing gestational weight gain can improve the outcomes of these high-risk pregnancies.

Ultrasound Diagnosis of the Small and Large Fetus

Antenatal imaging is crucial in the management of high-risk pregnancies. Accurate dating relies on acquisition of reliable and reproducible ultrasound images and measurements. Quality image acquisition is necessary for assessing fetal growth and performing Doppler measurements to help diagnose pregnancy complications, stratify risk, and guide management. Further research is needed to ascertain whether current methods for estimating fetal weight can be improved with 3-dimensional ultrasound or magnetic resonance imaging; optimize dating with late initiation of prenatal care; minimize under-diagnosis of fetal growth restriction; and identify the best strategies to make ultrasound more available in low-income and middle-income countries.

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.

Guideline No. 421: Point of Care Ultrasound in Obstetrics and Gynaecology

OBJECTIVE

To provide an opinion regarding the usefulness of point of care ultrasound in obstetrics and gynaecology.

TARGET POPULATION

Women with pregnancy-related complications or issues who could benefit from an urgent bedside sonographic evaluation.

OPTIONS

Point of care ultrasound is a readily accessible option, requiring few resources.

BENEFITS, HARMS, AND COSTS

This low-cost imaging option can expedite appropriate patient management, enhance provider confidence, and allay the patient's anxiety in a timely fashion. However, there is potential for error in imaging or interpretation, resulting in incorrect and potentially harmful patient management.

EVIDENCE

MEDLINE, PubMed, Embase, and the Cochrane Library weres earched from 2009 to 2019. Medical Subject Headings (MeSH) and keywords were related to pregnancy, PoCUS, point of care ultrasound, and bedside ultrasound. This document represents an abstraction of the evidence rather than a methodological review.

VALIDATION METHODS

The authors rated the quality of evidence and strength of recommendations using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. See online Appendix A (Tables A1 for definitions and A2 for interpretations of strong and weak recommendations).

INTENDED AUDIENCE

Providers of urgent care for pregnant women (obstetricians and gynaecologists, family physicians, emergency room physicians, midwives, nurse practitioners, nurses).

SUMMARY STATEMENT (GRADE RATINGS IN PARENTHESES)

RECOMMENDATIONS (GRADE RATINGS IN PARENTHESES).

Comparison of Expanded New Ballard, Eregie and Parkin Scores in Predicting Gestational Age in Newborns

BACKGROUND

Postnatal gestational age (GA) estimation is crucial in neonates with absent early trimester ultrasonography (USG) reports to evaluate the risk of morbidity and mortality. Widely used Expanded New Ballard (ENBS) is complex and needs an expert medical professional to assess. Eregie (ES) and Parkin (PS) scores are easy to perform with good inter-observer reliability. We aimed to compare these scores for their ability in predicting GA with early trimester USG-GA as the gold standard.

METHODS

This cross-sectional study was conducted among 1114 intramural neonates who qualified according to the selection criteria. All neonates were evaluated using the three scores by residents (blinded to USG-GA) with a pre-defined inter-observer difference of <10%. Method applicability was compared in terms of accuracy, precision and validity.

RESULTS

GA was systematically overestimated by 0.65, 0.68 and 0.32 weeks by ENBS, PS and ES, respectively. For GA subgroups, ES had better overall accuracy with mean difference of 0.32 ± 0.88 weeks. Validity was highest for ES (79.6%).

INTERPRETATION

We conclude that all three scores were found to be of acceptable accuracy and validity. ES had better overall accuracy and validity among the subjects and for subgroups. In limited resource settings, ES or PS can substitute for ENBS.

Committee Opinion No. 418: The Complete 11-14 Week Prenatal Sonographic Examination

OBJECTIVE

Sonography during the first trimester provides an opportunity to assess a pregnancy in its early stage. This document provides an opinion about the implementation and content of prenatal sonographic examinations at 11-14 weeks gestation in Canada.

TARGET POPULATION

Pregnant women at 11-14 weeks gestation.

BENEFITS, HARMS, AND COSTS

The 11-14 week prenatal sonographic examination can provide important information that may contribute to pregnancy management. It can be used to confirm viability, establish gestational age, determine the number of fetuses, assess the adnexa/ovaries, and, in a multiple pregnancy, assess chorionicity and amnionicity. Scanning also offers an opportunity to detect fetal abnormalities and perform aneuploidy screening by measuring the nuchal translucency thickness. It may be valuable in screening for preeclampsia and other obstetrical disorders (by combining uterine artery Doppler scanning with other bio-clinical markers) and for invasive placentation. There are no physical harms to mother or fetus from offering a routine 11-14 week prenatal sonographic examination, and there are no extra costs for patients.

EVIDENCE

Articles related to routine 11-14 week prenatal sonography were identified in a search of EMBASE and MEDLINE using the search terms first trimester ultrasound, nuchal translucency, and 11-14 week ultrasound. The search included all articles published on the topic until May 2019. Abstracts were reviewed by one author, and articles deemed relevant were then reviewed in full to determine whether to include them in the study. Articles that were not in English and articles that did not pertain to 11-14 week prenatal sonography were excluded.

INTENDED AUDIENCE

This document is intended for sonographers, midwives, family physicians, obstetricians, and maternal-fetal medicine specialists.

Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation

BACKGROUND

Globally, 15 million infants are born preterm and another 23.2 million infants are born small for gestational age (SGA). Determining burden of preterm and SGA births, is essential for effective planning, modification of health policies and targeting interventions for reducing these outcomes for which accurate estimation of gestational age (GA) is crucial. Early pregnancy ultrasound measurements, last menstrual period and post-natal neonatal examinations have proven to be not feasible or inaccurate. Proposed algorithms for GA estimation in western populations, based on routine new-born screening, though promising, lack validation in developing country settings. We evaluated the hypothesis that models developed in USA, also predicted GA in cohorts of South Asia (575) and Sub-Saharan Africa (736) with same precision.

METHODS

Dried heel prick blood spots collected 24-72 hours after birth from 1311 new-borns, were analysed for standard metabolic screen. Regression algorithm based, GA estimates were computed from metabolic data and compared to first trimester ultrasound validated, GA estimates (gold standard).

RESULTS

Overall Algorithm (metabolites + birthweight) estimated GA to within an average deviation of 1.5 weeks. The estimated GA was within the gold standard estimate by 1 and 2 weeks for 70.5% and 90.1% new-borns respectively. Inclusion of birthweight in the metabolites model improved discriminatory ability of this method, and showed promise in identifying preterm births. Receiver operating characteristic (ROC) curve analysis estimated an area under curve of 0.86 (conservative bootstrap 95% confidence interval (CI) = 0.83 to 0.89); P < 0.001) and Youden Index of 0.58 (95% CI = 0.51 to 0.64) with a corresponding sensitivity of 80.7% and specificity of 77.6%.

CONCLUSION

Metabolic gestational age dating offers a novel means for accurate population-level gestational age estimates in LMIC settings and help preterm birth surveillance initiatives. Further research should focus on use of machine learning and newer analytic methods broader than conventional metabolic screen analytes, enabling incorporation of region-specific analytes and cord blood metabolic profiles models predicting gestational age accurately.

Comparison of first trimester dating methods for gestational age estimation and their implication on preterm birth classification in a North Indian cohort

Background

Different formulae have been developed globally to estimate gestational age (GA) by ultrasonography in the first trimester of pregnancy. In this study, we develop an Indian population-specific dating formula and compare its performance with published formulae. Finally, we evaluate the implications of the choice of dating method on preterm birth (PTB) rate. This study’s data was from GARBH-Ini, an ongoing pregnancy cohort of North Indian women to study PTB.

Methods

Comparisons between ultrasonography-Hadlock and last menstrual period (LMP) based dating methods were made by studying the distribution of their differences by Bland-Altman analysis. Using data-driven approaches, we removed data outliers more efficiently than by applying clinical parameters. We applied advanced machine learning algorithms to identify relevant features for GA estimation and developed an Indian population-specific formula (Garbhini-GA1) for the first trimester. PTB rates of Garbhini-GA1 and other formulae were compared by estimating sensitivity and accuracy.

Results

Performance of Garbhini-GA1 formula, a non-linear function of crown-rump length (CRL), was equivalent to published formulae for estimation of first trimester GA (LoA, − 0.46,0.96 weeks). We found that CRL was the most crucial parameter in estimating GA and no other clinical or socioeconomic covariates contributed to GA estimation. The estimated PTB rate across all the formulae including LMP ranged 11.27–16.50% with Garbhini-GA1 estimating the least rate with highest sensitivity and accuracy. While the LMP-based method overestimated GA by 3 days compared to USG-Hadlock formula; at an individual level, these methods had less than 50% agreement in the classification of PTB.

Conclusions

An accurate estimation of GA is crucial for the management of PTB. Garbhini-GA1, the first such formula developed in an Indian setting, estimates PTB rates with higher accuracy, especially when compared to commonly used Hadlock formula. Our results reinforce the need to develop population-specific gestational age formulae.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-021-03807-4.

FIGO (international Federation of Gynecology and obstetrics) initiative on fetal growth: best practice advice for screening, diagnosis, and management of fetal growth restriction

Fetal growth restriction (FGR) is defined as the failure of the fetus to meet its growth potential due to a pathological factor, most commonly placental dysfunction. Worldwide, FGR is a leading cause of stillbirth, neonatal mortality, and short- and long-term morbidity. Ongoing advances in clinical care, especially in definitions, diagnosis, and management of FGR, require efforts to effectively translate these changes to the wide range of obstetric care providers. This article highlights agreements based on current research in the diagnosis and management of FGR, and the areas that need more research to provide further clarification of recommendations. The purpose of this article is to provide a comprehensive summary of available evidence along with practical recommendations concerning the care of pregnancies at risk of or complicated by FGR, with the overall goal to decrease the risk of stillbirth and neonatal mortality and morbidity associated with this condition. To achieve these goals, FIGO (the International Federation of Gynecology and Obstetrics) brought together international experts to review and summarize current knowledge of FGR. This summary is directed at multiple stakeholders, including healthcare providers, healthcare delivery organizations and providers, FIGO member societies, and professional organizations. Recognizing the variation in the resources and expertise available for the management of FGR in different countries or regions, this article attempts to take into consideration the unique aspects of antenatal care in low-resource settings (labelled “LRS” in the recommendations). This was achieved by collaboration with authors and FIGO member societies from low-resource settings such as India, Sub-Saharan Africa, the Middle East, and Latin America.

Quantification of 1.5 T T1 and T2 * Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies

BACKGROUND

Despite its many advantages, experience with fetal magnetic resonance imaging (MRI) is limited, as is knowledge of how fetal tissue relaxation times change with gestational age (GA). Quantification of fetal tissue relaxation times as a function of GA provides insight into tissue changes during fetal development and facilitates comparison of images across time and subjects. This, therefore, can allow the determination of biophysical tissue parameters that may have clinical utility.

PURPOSE

To demonstrate the feasibility of quantifying previously unknown T₁ and T₂ ^* relaxation times of fetal tissues in uncomplicated pregnancies as a function of GA at 1.5 T.

STUDY TYPE

Pilot.

POPULATION

Nine women with singleton, uncomplicated pregnancies (28-38 weeks GA).

FIELD STRENGTH/SEQUENCE

All participants underwent two iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) acquisitions at different flip angles (6° and 20°) at 1.5 T.

ASSESSMENT

Segmentations of the lungs, liver, spleen, kidneys, muscle, and adipose tissue (AT) were conducted using water-only images and proton density fat fraction maps. Driven equilibrium single pulse observation of T₁ (DESPOT₁ ) was used to quantify the mean water T₁ of the lungs, intraabdominal organs, and muscle, and the mean water and lipid T₁ of AT. IDEAL T₂ ^* maps were used to quantify the T₂ ^* values of the lungs, intraabdominal organs, and muscle.

STATISTICAL TESTS

F-tests were performed to assess the T₁ and T₂ ^* changes of each analyzed tissue as a function of GA.

RESULTS

No tissue demonstrated a significant change in T₁ as a function of GA (lungs [P = 0.89]; liver [P = 0.14]; spleen [P = 0.59]; kidneys [P = 0.97]; muscle [P = 0.22]; AT: water [P = 0.36] and lipid [P = 0.14]). Only the spleen and muscle T₂ ^* showed a significant decrease as a function of GA (lungs [P = 0.67); liver [P = 0.05]; spleen [P < 0.05]; kidneys [P = 0.70]; muscle [P < 0.05]).

DATA CONCLUSION

These preliminary data suggest that the T₁ of the investigated tissues is relatively stable over 28-38 weeks GA, while the T₂ ^* change in spleen and muscle decreases significantly in that period.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

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.