Dataset Information

The assessment of gestational age: a comparison of different methods from a malaria pregnancy cohort in sub-Saharan Africa.

ABSTRACT: BACKGROUND:Determining gestational age in resource-poor settings is challenging because of limited availability of ultrasound technology and late first presentation to antenatal clinic. Last menstrual period (LMP), symphysio-pubis fundal height (SFH) and Ballard Score (BS) at delivery are therefore often used. We assessed the accuracy of LMP, SFH, and BS to estimate gestational age at delivery and preterm birth compared to ultrasound (US) using a large dataset derived from a randomized controlled trial in pregnant malaria patients in four African countries. METHODS:Mean and median gestational age for US, LMP, SFH and BS were calculated for the entire study population and stratified by country. Correlation coefficients were calculated using Pearson's rho, and Bland Altman plots were used to calculate mean differences in findings with 95% limit of agreements. Sensitivity, specificity, positive predictive value and negative predictive value were calculated considering US as reference method to identify term and preterm babies. RESULTS:A total of 1630 women with P. falciparum infection and a gestational age?>?24?weeks determined by ultrasound at enrolment were included in the analysis. The mean gestational age at delivery using US was 38.7?weeks (95%CI: 38.6-38.8), by LMP, 38.4?weeks (95%CI: 38.0-38.9), by SFH, 38.3?weeks (95%CI: 38.2-38.5), and by BS 38.0?weeks (95%CI: 37.9-38.1) (p

SUBMITTER: Unger H

PROVIDER: S-EPMC6323786 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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The assessment of gestational age: a comparison of different methods from a malaria pregnancy cohort in sub-Saharan Africa.

Unger Holger H Thriemer Kamala K Ley Benedikt B Tinto Halidou H Traoré Maminata M Valea Innocent I Tagbor Harry H Antwi Gifty G Gbekor Prosper P Nambozi Michael M Kabuya Jean-Bertin Bukasa JB Mulenga Modest M Mwapasa Victor V Chapotera Gertrude G Madanitsa Mwayiwawo M Rulisa Stephen S de Crop Maaike M Claeys Yves Y Ravinetto Raffaella R D'Alessandro Umberto U

BMC pregnancy and childbirth 20190108 1

<h4>Background</h4>Determining gestational age in resource-poor settings is challenging because of limited availability of ultrasound technology and late first presentation to antenatal clinic. Last menstrual period (LMP), symphysio-pubis fundal height (SFH) and Ballard Score (BS) at delivery are therefore often used. We assessed the accuracy of LMP, SFH, and BS to estimate gestational age at delivery and preterm birth compared to ultrasound (US) using a large dataset derived from a randomized c ...[more]

PMID: 30621604

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Project description:Malaria during pregnancy is associated with an increased risk for low birth weight (<2500 grams). Distinguishing infants that are born premature (< 37 weeks) from those that are growth-restricted (less than the 10th percentile at birth) requires accurate assessment of gestational age. Where ultrasound is accessible, sonographic confirmation of gestational age is more accurate than menstrual dating. The goal was to pilot the feasibility and utility of adding ultrasound to an observational pregnancy malaria cohort.In July 2009, research staff (three mid-level clinical providers, one nurse) from The Blantyre Malaria Project underwent an intensive one-week ultrasound training to perform foetal biometry. Following an additional four months of practice and remote image review, subjects from an ongoing cohort were recruited for ultrasound to determine gestational age. Gestational age at delivery established by ultrasound was compared with postnatal gestational age assessment (Ballard examination).One hundred and seventy-eight women were enrolled. The majority of images were of good quality (94.3%, 509/540) although a learning curve was apparent with 17.5% (24/135) images of unacceptable quality in the first 25% of scans. Ultrasound was used to date 13% of the pregnancies when menstrual dates were unknown and changed the estimated gestational age for an additional 25%. There was poor agreement between the gestational age at delivery as established by the ultrasound protocol compared to that determined by the Ballard examination (bias 0.8 weeks, limits of agreement -3.5 weeks to 5.1 weeks). The distribution of gestational ages by Ballard suggested a clustering of gestational age around the mean with 87% of the values falling between 39 and 41 weeks. The distribution of gestational age by ultrasound confirmed menstrual dates was more typical. Using ultrasound confirmed dates as the gold standard, 78.5% of preterm infants were misclassified as term and 26.8% of small-for gestational age infants misclassified as appropriately grown by Ballard.Ultrasound should be strongly considered in prospective malaria studies with obstetric endpoints to confirm gestational age and avoid misclassification of infants as premature or growth-restricted. The use of ultrasound does require a significant investment of time to maintain quality image acquisition.

Project description:BackgroundBabies 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.MethodsThis 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.ResultsOverall 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).ConclusionsMachine 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.

Project description:BackgroundPreterm birth is the leading cause of death among children <5 years of age. Accurate determination of prematurity is necessary to provide appropriate neonatal care and guide preventive measures. To estimate the most accurate method to identify infants at risk for adverse outcomes, we assessed the validity of two widely available methods-last menstrual period (LMP) and the New Ballard (NB) neonatal assessment-against ultrasound in determining gestational age and preterm birth in highland Guatemala.MethodsPregnant women (n = 188) were recruited with a gestational age <20 weeks and followed until delivery. Ultrasound was performed by trained physicians and LMP was collected during recruitment. NB was performed on infants within 96 hours of birth by trained study nurses. LMP and NB accuracy at determining gestational age and identifying prematurity was assessed by comparing them to ultrasound.ResultsBy ultrasound, infant mean gestational age at birth was 38.3 weeks (SD = 1.6) with 16% born at less than 37 gestation. LMP was more accurate than NB (mean difference of +0.13 weeks for LMP and +0.61 weeks for NB). However, LMP and NB estimates had low agreement with ultrasound-determined gestational age (Lin's concordance<0.48 for both methods) and preterm birth (?<0.29 for both methods). By LMP, 18% were judged premature compared with 6% by NB. LMP underestimated gestational age among women presenting later to prenatal care (0.18 weeks for each additional week). Gestational age for preterm infants was overestimated by nearly one week using LMP and nearly two weeks using NB. New Ballard neuromuscular measurements were more predictive of preterm birth than those measuring physical criteria.ConclusionIn an indigenous population in highland Guatemala, LMP overestimated prematurity by 2% and NB underestimated prematurity by 10% compared with ultrasound estimates. New, simple and accurate methods are needed to identify preterm birth in resource-limited settings worldwide.

Dataset Information

The assessment of gestational age: a comparison of different methods from a malaria pregnancy cohort in sub-Saharan Africa.

Publications

The assessment of gestational age: a comparison of different methods from a malaria pregnancy cohort in sub-Saharan Africa.

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