PRECISE pregnancy cohort: challenges and strategies in setting up a biorepository in sub-Saharan Africa

Background and objective: PRECISE is a population-based, prospective pregnancy cohort study designed for deep phenotyping of pregnancies in women with placenta-related disorders, and in healthy controls. The PRECISE Network is recruiting ~ 10,000 pregnant women in three countries (The Gambia, Kenya, and Mozambique) representing sub-Saharan Africa. The principal aim is to improve our understanding of pre-eclampsia, fetal growth restriction and stillbirth. This involves the creation of a highly curated biorepository for state of the art discovery science and a rich database of antenatal variables and maternal and neonatal outcomes. Our overarching aim is to provide large sample numbers with adequate power to address key scientific questions. Here we describe our experience of establishing a biorepository in the PRECISE Network and review the issues and challenges surrounding set-up, management and scientific use. Methods: The feasibility of collecting and processing each sample type was assessed in each setting and plans made for establishing the necessary infrastructure. Quality control (QC) protocols were established to ensure that biological samples are \u27fit-for-purpose\u27. The management structures required for standardised sample collection and processing were developed. This included the need for transport of samples between participating countries and to external academic/commercial institutions. Results: Numerous practical challenges were encountered in setting up the infrastructure including facilities, staffing, training, cultural barriers, procurement, shipping and sample storage. Whilst delaying the project, these were overcome by establishing good communication with the sites, training workshops and constant engagement with the necessary commercial suppliers. A Project Executive Committee and Biology Working Group together defined the biospecimens required to answer the research questions paying particular attention to harmonisation of protocols with other cohorts so as to enable cross-biorepository collaboration. Governance structures implemented include a Data and Sample Committee to ensure biospecimens and data will be used according to consent, and prioritisation by scientific excellence. A coordinated sample and data transfer agreement will prevent delay in sample sharing. Discussion: With adequate training and infrastructure, it is possible to establish high quality sample collections to facilitate research programmes such as the PRECISE Network in sub-Saharan Africa. These preparations are pre-requisites for effective execution of a biomarker-based approach to better understand the complexities of placental disease in these settings, and others

The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network’s first protocol: deep phenotyping in three sub-Saharan African countries

Background: The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network is a new and broadly-based group of research scientists and health advocates based in the UK, Africa and North America. Methods: This paper describes the protocol that underpins the clinical research activity of the Network, so that the investigators, and broader global health community, can have access to ‘deep phenotyping’ (social determinants of health, demographic and clinical parameters, placental biology and agnostic discovery biology) of women as they advance through pregnancy to the end of the puerperium, whether those pregnancies have normal outcomes or are complicated by one/more of the placental disorders of pregnancy (pregnancy hypertension, fetal growth restriction and stillbirth). Our clinical sites are in The Gambia (Farafenni), Kenya (Kilifi County), and Mozambique (Maputo Province). In each country, 50 non-pregnant women of reproductive age will be recruited each month for 1 year, to provide a final national sample size of 600; these women will provide culturally-, ethnically-, seasonallyand spatially-relevant control data with which to compare women with normal and complicated pregnancies. Between the three countries we will recruit ≈10,000 unselected pregnant women over 2 years. An estimated 1500 women will experience one/more placental complications over the same epoch. Importantly, as we will have accurate gestational age dating using the TraCer device, we will be able to discriminate between fetal growth restriction and preterm birth. Recruitment and follow-up will be primarily facility-based and will include women booking for antenatal care, subsequent visits in the third trimester, at time-of-disease, when relevant, during/ immediately after birth and 6 weeks after birth. Conclusions: To accelerate progress towards the women’s and children’s health-relevant Sustainable Development Goals, we need to understand how a variety of social, chronic disease, biomarker and pregnancy-specific determinants health interact to result in either a resilient or a compromised pregnancy for either mother or fetus/ newborn, or both. This protocol has been designed to create such a depth of understanding. We are seeking funding to maintain the cohort to better understand the implications of pregnancy complications for both maternal and child health

Pregnancy outcomes and vaccine effectiveness during the period of omicron as the variant of concern, INTERCOVID-2022: a multinational, observational study

Pregnancy outcomes; Vaccine effectiveness; OmicronResultados del embarazo; Efectividad de la vacuna; OmicronResultats de l'embaràs; Eficàcia de la vacuna; OmicronBackground In 2021, we showed an increased risk associated with COVID-19 in pregnancy. Since then, the SARS-CoV-2 virus has undergone genetic mutations. We aimed to examine the effects on maternal and perinatal outcomes of COVID-19 during pregnancy, and evaluate vaccine effectiveness, when omicron (B.1.1.529) was the variant of concern. Methods INTERCOVID-2022 is a large, prospective, observational study, involving 41 hospitals across 18 countries. Each woman with real-time PCR or rapid test, laboratory-confirmed COVID-19 in pregnancy was compared with two unmatched women without a COVID-19 diagnosis who were recruited concomitantly and consecutively in pregnancy or at delivery. Mother and neonate dyads were followed until hospital discharge. Primary outcomes were maternal morbidity and mortality index (MMMI), severe neonatal morbidity index (SNMI), and severe perinatal morbidity and mortality index (SPMMI). Vaccine effectiveness was estimated, adjusted by maternal risk profile. Findings We enrolled 4618 pregnant women from Nov 27, 2021 (the day after WHO declared omicron a variant of concern), to June 30, 2022: 1545 (33%) women had a COVID-19 diagnosis (median gestation 36·7 weeks [IQR 29·0–38·9]) and 3073 (67%) women, with similar demographic characteristics, did not have a COVID-19 diagnosis. Overall, women with a diagnosis had an increased risk for MMMI (relative risk [RR] 1·16 [95% CI 1·03–1·31]) and SPMMI (RR 1·21 [95% CI 1·00–1·46]). Women with a diagnosis, compared with those without a diagnosis, also had increased risks of SNMI (RR 1·23 [95% CI 0·88–1·71]), although the lower bounds of the 95% CI crossed unity. Unvaccinated women with a COVID-19 diagnosis had a greater risk of MMMI (RR 1·36 [95% CI 1·12–1·65]). Severe COVID-19 symptoms in the total sample increased the risk of severe maternal complications (RR 2·51 [95% CI 1·84–3·43]), perinatal complications (RR 1·84 [95% CI 1·02–3·34]), and referral, intensive care unit (ICU) admission, or death (RR 11·83 [95% CI 6·67–20·97]). Severe COVID-19 symptoms in unvaccinated women increased the risk of MMMI (RR 2·88 [95% CI 2·02–4·12]) and referral, ICU admission, or death (RR 20·82 [95% CI 10·44–41·54]). 2886 (63%) of 4618 total participants had at least a single dose of any vaccine, and 2476 (54%) of 4618 had either complete or booster doses. Vaccine effectiveness (all vaccines combined) for severe complications of COVID-19 for all women with a complete regimen was 48% (95% CI 22–65) and 76% (47–89) after a booster dose. For women with a COVID-19 diagnosis, vaccine effectiveness of all vaccines combined for women with a complete regimen was 74% (95% CI 48–87) and 91% (65–98) after a booster dose. Interpretation COVID-19 in pregnancy, during the first 6 months of omicron as the variant of concern, was associated with increased risk of severe maternal morbidity and mortality, especially among symptomatic and unvaccinated women. Women with complete or boosted vaccine doses had reduced risk for severe symptoms, complications, and death. Vaccination coverage among pregnant women remains a priority

The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) database: open-access data collection in maternal and newborn health

In less-resourced settings, adverse pregnancy outcome rates are unacceptably high. To effect improvement, we need accurate epidemiological data about rates of death and morbidity, as well as social determinants of health and processes of care, and from each country (or region) to contextualise strategies. The PRECISE database is a unique core infrastructure of a generic, unified data collection platform. It is built on previous work in data harmonisation, outcome and data field standardisation, open-access software (District Health Information System 2 and the Baobab Laboratory Information Management System), and clinical research networks. The database contains globally-recommended indicators included in Health Management Information System recording and reporting forms. It comprises key outcomes (maternal and perinatal death), life-saving interventions (Human Immunodeficiency Virus testing, blood pressure measurement, iron therapy, uterotonic use after delivery, postpartum maternal assessment within 48 h of birth, and newborn resuscitation, immediate skin-to-skin contact, and immediate drying), and an additional 17 core administrative variables for the mother and babies. In addition, the database has a suite of additional modules for ‘deep phenotyping’ based on established tools. These include social determinants of health (including socioeconomic status, nutrition and the environment), maternal comorbidities, mental health, violence against women and health systems. The database has the potential to enable future high-quality epidemiological research integrated with clinical care and discovery bioscience

Pregnancy cohorts and biobanking in sub-Saharan Africa: a systematic review

Background: Technological advances and high throughput biological assays can facilitate discovery science in biobanks from population cohorts, including pregnant women. Biological pathways associated with health outcomes differ depending on geography, and high-income country data may not generalise to low-resource settings. We conducted a systematic review to identify prospective pregnancy cohorts in sub-Saharan Africa (SSA) that include biobanked samples with potential to enhance discovery science opportunity. Methods: Inclusion criteria were prospective data collection during pregnancy, with associated biobanking in SSA. Data sources included: scientific databases (with comprehensive search terms), grey literature, hand searching applicable reference lists and expert input. Results were screened in a three-stage process based on title, abstract and full text by two independent reviewers. The review is registered on PROSPERO (CRD42019147483). Results: Fourteen SSA studies met the inclusion criteria from database searches (n=8), reference list searches (n=2) and expert input (n=4). Three studies have ongoing data collection. The most represented countries were South Africa and Mozambique (Southern Africa) (n=3), Benin (Western Africa) (n=4) and Tanzania (Eastern Africa) (n=4); including an estimated 31 763 women. Samples commonly collected were blood, cord blood and placenta. Seven studies collected neonatal samples. Common clinical outcomes included maternal and perinatal mortality, malaria and preterm birth. Conclusions: Increasingly numerous pregnancy cohorts in SSA that include biobanking are generating a uniquely valuable resource for collaborative discovery science, and improved understanding of the high regional risks of maternal, fetal and neonatal morbidity and mortality. Future studies should align protocols and consider their added value and distinct contributions

Association between preterm-birth phenotypes and differential morbidity, growth, and neurodevelopment at age 2 years: Results from the INTERBIO-21st newborn study

Importance: The etiologic complexities of preterm birth remain inadequately understood, which may impede the development of better preventative and treatment measures.Objective: To examine the association between specific preterm-birth phenotypes and clinical, growth, and neurodevelopmental differences among preterm newborns compared with term newborns up to age 2 years.Design, setting, and participants: The INTERBIO-21st study included a cohort of preterm and term newborn singletons enrolled between March 2012 and June 2018 from maternity hospitals in 6 countries worldwide who were followed up from birth to age 2 years. All pregnancies were dated by ultrasonography. Data were analyzed from November 2019 to October 2020.Exposures/interventions: Preterm-birth phenotypes.Main outcomes and measures: Infant size, health, nutrition, and World Health Organization motor development milestones assessed at ages 1 and 2 years; neurodevelopment evaluated at age 2 years using the INTERGROWTH-21st Neurodevelopment Assessment (INTER-NDA) tool.Results: A total of 6529 infants (3312 boys [50.7%]) were included in the analysis. Of those, 1381 were preterm births (mean [SD] gestational age at birth, 34.4 [0.1] weeks; 5148 were term births (mean [SD] gestational age at birth, 39.4 [0] weeks). Among 1381 preterm newborns, 8 phenotypes were identified: no main maternal, fetal, or placental condition detected (485 infants [35.1%]); infections (289 infants [20.9%]); preeclampsia (162 infants [11.7%]); fetal distress (131 infants [9.5%]); intrauterine growth restriction (110 infants [8.0%]); severe maternal disease (85 infants [6.2%]); bleeding (71 infants [5.1%]); and congenital anomaly (48 infants [3.5%]). For all phenotypes, a previous preterm birth was a risk factor for recurrence. Each phenotype displayed differences in neonatal morbidity and infant outcomes. For example, infants with the no main condition detected phenotype had low neonatal morbidity but increased morbidity and hospitalization incidence at age 1 year (odds ratio [OR], 2.2; 95% CI, 1.8-2.7). Compared with term newborns, the highest risk of scoring lower than the 10th centile of INTER-NDA normative values was observed in the fine motor development domain among newborns with the fetal distress (OR, 10.6; 95% CI, 5.1-22.2) phenotype.Conclusions and relevance: Results of this study suggest that phenotypic classification may provide a better understanding of the etiologic factors and mechanisms associated with preterm birth than continuing to consider it an exclusively time-based entity

Acceptability and Feasibility of a Low-Cost Device for Gestational Age Assessment in a Low-Resource Setting: Qualitative Study

Background: Ultrasound for gestational age (GA) assessment is not routinely available in resource-constrained settings, particularly in rural and remote locations. The TraCer device combines a handheld wireless ultrasound probe and a tablet with artificial intelligence (AI)-enabled software that obtains GA from videos of the fetal head by automated measurements of the fetal transcerebellar diameter and head circumference. Objective: The aim of this study was to assess the perceptions of pregnant women, their families, and health care workers regarding the feasibility and acceptability of the TraCer device in an appropriate setting. Methods: A descriptive study using qualitative methods was conducted in two public health facilities in Kilifi county in coastal Kenya prior to introduction of the new technology. Study participants were shown a video role-play of the use of TraCer at a typical antenatal clinic visit. Data were collected through 6 focus group discussions (N=52) and 18 in-depth interviews. Results: Overall, TraCer was found to be highly acceptable to women, their families, and health care workers, and its implementation at health care facilities was considered to be feasible. Its introduction was predicted to reduce anxiety regarding fetal well-being, increase antenatal care attendance, increase confidence by women in their care providers, as well as save time and cost by reducing unnecessary referrals. TraCer was felt to increase the self-image of health care workers and reduce time spent providing antenatal care. Some participants expressed hesitancy toward the new technology, indicating the need to test its performance over time before full acceptance by some users. The preferred cadre of health care professionals to use the device were antenatal clinic nurses. Important implementation considerations included adequate staff training and the need to ensure sustainability and consistency of the service. Misconceptions were common, with a tendency to overestimate the diagnostic capability, and expectations that it would provide complete reassurance of fetal and maternal well-being and not primarily the GA. Conclusions: This study shows a positive attitude toward TraCer and highlights the potential role of this innovation that uses AI-enabled automation to assess GA. Clarity of messaging about the tool and its role in pregnancy is essential to address misconceptions and prevent misuse. Further research on clinical validation and related usability and safety evaluations are recommended

Machine learning for accurate estimation of fetal gestational age based on ultrasound images

Accurate estimation of gestational age is an essential component of good obstetric care and informs clinical decision-making throughout pregnancy. As the date of the last menstrual period is often unknown or uncertain, ultrasound measurement of fetal size is currently the best method for estimating gestational age. The calculation assumes an average fetal size at each gestational age. The method is accurate in the first trimester, but less so in the second and third trimesters as growth deviates from the average and variation in fetal size increases. Consequently, fetal ultrasound late in pregnancy has a wide margin of error of at least ±2 weeks’ gestation. Here, we utilise state-of-the-art machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information. The machine learning model is based on ultrasound images from two independent datasets: one for training and internal validation, and another for external validation. During validation, the model was blinded to the ground truth of gestational age (based on a reliable last menstrual period date and confirmatory first-trimester fetal crown rump length). We show that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction. Our best machine-learning based model estimates gestational age with a mean absolute error of 3.0 (95% CI, 2.9–3.2) and 4.3 (95% CI, 4.1–4.5) days in the second and third trimesters, respectively, which outperforms current ultrasound-based clinical biometry at these gestational ages. Our method for dating the pregnancy in the second and third trimesters is, therefore, more accurate than published methods

Harnessing the PRECISE network as a platform to strengthen global capacity for maternal and child health research in sub-Saharan Africa

It is widely acknowledged across the global health sector that research programmes need to be designed and implemented in a way that maximise opportunities for strengthening local capacity. This paper examines how the United Kingdom Research and Innovation (UKRI) Grand Challenges Research Fund (GCRF) funded PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network has been established as a platform to strengthen global capacity for research focused on the improvement of maternal, fetal and newborn health in subSaharan Africa. Best practice principles outlined in an ESSENCE on Health Research report have been considered in relation to the PRECISE Network capacity-building activities described in this paper. These activities are described at the individual, programmatic and institutional levels, and successes, challenges and recommendations for future work are outlined. The paper concludes that the PRECISE leadership have an opportunity to review and refresh activity plans for capacity building at this stage in the project to build on achievements to date

Interactions between the Physical and Social Environments with Adverse Pregnancy Events Related to Placental Disorders—A Scoping Review

Background: Due to different social and physical environments across Africa, understanding how these environments differ in interacting with placental disorders will play an important role in developing effective interventions. Methods: A scoping review was conducted, to identify current knowledge on interactions between the physical and social environment and the incidence of placental disease in Africa. Results: Heavy metals were said to be harmful when environmental concentrations are beyond critical limits. Education level, maternal age, attendance of antenatal care and parity were the most investigated social determinants. Conclusions: More evidence is needed to determine the relationships between the environment and placental function in Africa. The results show that understanding the nature of the relationship between social determinants of health (SDH) and placental health outcomes plays a pivotal role in understanding the risk in the heterogenous communities in Africa