External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh.
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ABSTRACT:
SUBMITTER: Murphy MS
PROVIDER: S-EPMC6424558 | biostudies-literature | 2019 Mar
REPOSITORIES: biostudies-literature
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External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh.
Murphy Malia Sq MS Hawken Steven S Cheng Wei W Wilson Lindsay A LA Lamoureux Monica M Henderson Matthew M Pervin Jesmin J Chowdhury Azad A Gravett Courtney C Lackritz Eve E Potter Beth K BK Walker Mark M Little Julian J Rahman Anisur A Chakraborty Pranesh P Wilson Kumanan K
eLife 20190319
This study sought to evaluate the performance of metabolic gestational age estimation models developed in Ontario, Canada in infants born in Bangladesh. Cord and heel prick blood spots were collected in Bangladesh and analyzed at a newborn screening facility in Ottawa, Canada. Algorithm-derived estimates of gestational age and preterm birth were compared to ultrasound-validated estimates. 1036 cord blood and 487 heel prick samples were collected from 1069 unique newborns. The majority of samples ...[more]
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Project description:BackgroundGestational age (GA) is frequently unknown or inaccurate in pregnancies in low-income countries. Early identification of preterm infants may help link them to potentially life-saving interventions.MethodsWe conducted a validation study in a community-based birth cohort in rural Bangladesh. GA was determined by pregnancy ultrasound (<20 weeks). Community health workers conducted home visits (<72 hours) to assess physical/neuromuscular signs and measure anthropometrics. The distribution, agreement, and diagnostic accuracy of different clinical methods of GA assessment were determined compared with early ultrasound dating.ResultsIn the live-born cohort (n = 1066), the mean ultrasound GA was 39.1 weeks (SD 2.0) and prevalence of preterm birth (<37 weeks) was 11.4%. Among assessed newborns (n = 710), the mean ultrasound GA was 39.3 weeks (SD 1.6) (8.3% preterm) and by Ballard scoring the mean GA was 38.9 weeks (SD 1.7) (12.9% preterm). The average bias of the Ballard was -0.4 weeks; however, 95% limits of agreement were wide (-4.7 to 4.0 weeks) and the accuracy for identifying preterm infants was low (sensitivity 16%, specificity 87%). Simplified methods for GA assessment had poor diagnostic accuracy for identifying preterm births (community health worker prematurity scorecard [sensitivity/specificity: 70%/27%]; Capurro [5%/96%]; Eregie [75%/58%]; Bhagwat [18%/87%], foot length <75 mm [64%/35%]; birth weight <2500 g [54%/82%]). Neonatal anthropometrics had poor to fair performance for classifying preterm infants (areas under the receiver operating curve 0.52-0.80).ConclusionsNewborn clinical assessment of GA is challenging at the community level in low-resource settings. Anthropometrics are also inaccurate surrogate markers for GA in settings with high rates of fetal growth restriction.
| S-EPMC4925072 | biostudies-literature
Project description:Low gestational weight gain (GWG) is a known predictor of fetal growth restriction in higher income countries, but there is little information on this association in lower income countries. Our objective is to describe the association between GWG and birth outcomes among pregnant women in rural Bangladesh. Pregnant women were identified in a community-based programme and enrolled into the study at an average of 13 weeks' gestation (n = 4,011). Maternal weight and height were measured at enrolment, maternal weight was measured at 36 weeks' gestation, and newborns were measured after birth. Rate of GWG (g/weeks) was calculated, and women were categorized as having adequate or inadequate GWG (Institute of Medicine recommendations). Newborn anthropometric outcomes included weight-for-age z score (WAZ), length-for-age z score (LAZ), head-circumference-for-age z score (HCZ), body mass index (BMI)-for-age z score (BMIZ), low birthweight (LBW < 2,500 g), WAZ < -2, LAZ < -2, HCZ < -2, BMIZ < -2, and small for gestational age (SGA: <10th percentile). Multivariate models were adjusted for confounders. Only 26% of the 2,562 women in these analyses had adequate GWG. Compared with newborns of women with inadequate GWG, infants of women with adequate GWG had a lower risk of adverse anthropometric outcomes (relative risk [95% confidence interval]: LBW = 0.68 [0.59, 0.80], LAZ < -2 = 0.64 [0.51, 0.80], HCZ < -2 = 0.75 [0.60, 0.93], BMIZ < -2 = 0.70 [0.59, 0.83], and SGA = 0.80 [0.73, 0.86]), but there was no significant difference in mean (SE) duration of gestation, 39.7 (0.08) versus 39.7 (0.05) weeks. In this population, GWG rate is a strong predictor of newborn anthropometric outcomes, but not duration of gestation.
| S-EPMC6859981 | biostudies-literature
Project description:Gestational age is associated with greater school achievement and variation in newborn metabolic markers. Whether metabolic markers are related to gestational age differences in achievement is unknown. This study examines whether newborn metabolic markers are associated with gestational age differences in performance on standardized school tests. This retrospective cohort study linked birth certificates of children born in Iowa between 2002 and 2010 to newborn screening records and school tests between 2009 and 2018. The analytical sample includes up to 229,679 children and 973,247 child-grade observations. Regression models estimate the associations between gestational age and 37 newborn metabolic markers with national percentile ranking (NPR) scores on math, reading comprehension, and science tests. An additional gestational week is associated with 0.6 (95% CI: 0.6, 0.7), 0.5 (95% CI: 0.4, 0.5), and 0.4 (95% CI: 0.4, 0.5) higher NPRs on math, reading, and science, respectively. Compared to full term children (37-44 weeks), preterm children (32-36 weeks) have 2.2 (95% CI: -2.6, -1.8), 1.5 (95% CI: -1.9, -1.1), and 1.0 (95% CI: -1.4, -0.7) lower NPRs on math, reading comprehension, and science. Very preterm children (20-31 weeks) have 8.3 (95% CI: -9.4, -7.2), 5.2 (95% CI: -6.2, -4.0), and 4.7 (95% CI: -5.6, -3.8) lower NPRs than full term children on math, reading, and science. Metabolic markers are associated with 27%, 36%, and 45% of gestational age differences in math, reading, and science scores, respectively, and over half of the difference in test scores between preterm or very preterm and full term children. Newborn metabolic markers are strongly related to gestational age differences in school test scores, suggesting that early metabolic differences are important markers of long-term child development.
| S-EPMC8834716 | biostudies-literature
Project description:ObjectivesBiological modelling of routinely collected newborn screening data has emerged as a novel method for deriving postnatal gestational age estimates. Validation of published models has previously been limited to cohorts largely consisting of infants of white Caucasian ethnicity. In this study, we sought to determine the validity of a published gestational age estimation algorithm among recent immigrants to Canada, where maternal landed immigrant status was used as a surrogate measure of infant ethnicity.DesignWe conducted a retrospective validation study in infants born in Ontario between April 2009 and September 2011.SettingProvincial data from Ontario, Canada were obtained from the Institute for Clinical Evaluative Sciences.ParticipantsThe dataset included 230?034 infants born to non-landed immigrants and 70?098 infants born to immigrant mothers. The five most common countries of maternal origin were India (n=10?038), China (n=7468), Pakistan (n=5824), The Philippines (n=5441) and Vietnam (n=1408). Maternal country of origin was obtained from Citizenship and Immigration Canada's Landed Immigrant Database.Primary and secondary outcome measuresPerformance of a postnatal gestational age algorithm was evaluated across non-immigrant and immigrant populations.ResultsRoot mean squared error (RMSE) of 1.05 weeks was observed for infants born to non-immigrant mothers, whereas RMSE ranged from 0.98 to 1.15 weeks among infants born to immigrant mothers. Area under the receiver operating characteristic curve for distinguishing term versus preterm infants (?37?vs?<37 weeks gestational age or >34?vs ?34 weeks gestational age) was 0.958 and 0.986, respectively, in the non-immigrant subgroup and ranged from 0.927 to 0.964 and 0.966 to 0.99 in the immigrant subgroups.ConclusionsAlgorithms for postnatal determination of gestational age may be further refined by development and validation of region or ethnicity-specific models. However, our results provide reassurance that an algorithm developed from Ontario-born infant cohorts performs well across a range of ethnicities and maternal countries of origin without modification.
| S-EPMC5589017 | biostudies-literature
Project description:ObjectiveAccurate gestational-age (GA) estimation, preferably by ultrasound measurement of fetal crown-rump length before 14 weeks' gestation, is an important component of high-quality antenatal care. The objective of this study was to determine how GA can best be estimated by fetal ultrasound for women who present for the first time late in pregnancy with uncertain or unknown menstrual dates.MethodsINTERGROWTH-21st was a large, prospective, multicenter, population-based project performed in eight geographically defined urban populations. One of its principal components, the Fetal Growth Longitudinal Study, aimed to develop international fetal growth standards. Each participant had their certain menstrual dates confirmed by first-trimester ultrasound examination. Fetal head circumference (HC), biparietal diameter (BPD), occipitofrontal diameter (OFD), abdominal circumference (AC) and femur length (FL) were measured every 5 weeks from 14 weeks' gestation until delivery. For each participant, a single, randomly selected ultrasound examination was used to explore all candidate biometric variables and permutations to build models to predict GA. Regression equations were ranked based upon minimization of the mean prediction error, goodness of fit and model complexity. An automated machine learning algorithm, the Genetic Algorithm, was adapted to evaluate > 64 000 potential polynomial equations as predictors.ResultsOf the 4607 eligible women, 4321 (94%) had a pregnancy without major complications and delivered a live singleton without congenital malformations. After other exclusions (missing measurements in GA window and outliers), the final sample comprised 4229 women. Two skeletal measures, HC and FL, produced the best GA prediction, given by the equation loge (GA) = 0.03243 × (loge (HC))2 + 0.001644 × FL × loge (HC) + 3.813. When FL was not available, the best equation based on HC alone was loge (GA) = 0.05970 × (loge (HC))2 + 0.000000006409 × (HC)3 + 3.3258. The estimated uncertainty of GA prediction (half width 95% interval) was 6-7 days at 14 weeks' gestation, 12-14 days at 26 weeks' gestation and > 14 days in the third trimester. The addition of FL to the HC model led to improved prediction intervals compared with using HC alone, but no further improvement in prediction was afforded by adding AC, BPD or OFD. Equations that included other measurements (BPD, OFD and AC) did not perform better.ConclusionsAmong women initiating antenatal care late in pregnancy, a single set of ultrasound measurements combining HC and FL in the second trimester can be used to estimate GA with reasonable accuracy. We recommend this tool for underserved populations but considerable efforts should be implemented to improve early initiation of antenatal care worldwide. © 2016 Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
| S-EPMC6680349 | biostudies-literature
Project description:ObjectiveOur study sought to determine whether metabolites from a retrospective collection of banked cord blood specimens could accurately estimate gestational age and to validate these findings in cord blood samples from Busia, Uganda.Study designForty-seven metabolites were measured by tandem mass spectrometry or enzymatic assays from 942 banked cord blood samples. Multiple linear regression was performed, and the best model was used to predict gestational age, in weeks, for 150 newborns from Busia, Uganda.ResultsThe model including metabolites and birthweight, predicted the gestational ages within 2 weeks for 76.7% of the Ugandan cohort. Importantly, this model estimated the prevalence of preterm birth <34 weeks closer to the actual prevalence (4.67% and 4.00%, respectively) than a model with only birthweight which overestimates the prevalence by 283%.ConclusionModels that include cord blood metabolites and birth weight appear to offer improvement in gestational age estimation over birth weight alone.
| S-EPMC8830770 | biostudies-literature
Project description:Prematurity predisposes to cardiovascular disease; however the underlying mechanisms remain elusive. Disturbance of the endothelial glycocalyx (EG), an important regulator of vessel function, is thought to contribute to vascular pathology. Here, we studied the EG with respect to gestational and postnatal age in preterm and term neonates. The Perfused Boundary Region (PBR), an inverse measure of glycocalyx thickness, was measured postnatally in 85 term and 39 preterm neonates. Preterm neonates were further analyzed in two subgroups i.e., neonates born < 30 weeks gestational age (group A) and neonates born ≥ 30 weeks (group B). In preterm neonates, weekly follow-up measurements were performed if possible. PBR differed significantly between preterm and term neonates with lowest values representing largest EG dimension in extremely premature infants possibly reflecting its importance in fetal vascular development. Linear regression revealed a dependence of PBR on both, gestational age and postnatal age. Furthermore, hematocrit predicted longitudinal PBR changes. PBR measured in group A at a corrected age of > 30 weeks was significantly higher than in group B at birth, pointing towards an alteration of intrinsic maturational effects by extrinsic factors. These changes might contribute to the increased cardiovascular risk associated with extreme prematurity.
| S-EPMC7862677 | biostudies-literature
Project description:BackgroundThe best method of gestational age assessment is by ultrasound in the first trimester; however, this method is impractical in large field trials in rural areas. Our objective was to assess the validity of gestational age estimated from prospectively collected date of last menstrual period (LMP) using crown-rump length (CRL) measured in early pregnancy by ultrasound.MethodsAs part of a large, cluster-randomized, controlled trial in rural Bangladesh, we collected dates of LMP by recall and as marked on a calendar every 5 weeks in women likely to become pregnant. Among those with a urine-test confirmed pregnancy, a subset with gestational age of <15 weeks (n = 353) were enrolled for ultrasound follow-up to measure CRL. We compared interview-assessed LMP with CRL gestational age estimates and classification of preterm, term, and post-term births.ResultsLMP-based gestational age was higher than CRL by a mean (SD) of 2.8 (10.7) days; differences varied by maternal education and preterm birth (P < 0.05). Lin's concordance correlation coefficient was good at ultrasound [0.63 (95 % CI 0.56, 0.69)] and at birth [0.77 (95 % CI 0.73, 0.81)]. Validity of classifying preterm birth was high but post-term was lower, with specificity of 96 and 89 % and sensitivity of 86 and 67 %, respectively. Results were similar by parity.ConclusionsProspectively collected LMP provided a valid estimate of gestational age and preterm birth in a rural, low-income setting and may be a suitable alternative to ultrasound in programmatic settings and large field trials.Trial registrationClinicalTrials.gov NCT00860470.
| S-EPMC5073953 | biostudies-literature
Project description:BACKGROUND:Newborn screening programs are essential preventative public health initiatives but are not widely available in low-resource settings. The objective of this study was to describe the frequency and nature of screen positive determinations as made by a Canadian newborn screening program in a cohort of infants born in Matlab, Bangladesh. Dried newborn cord and heel-prick blood spot samples collected as part of a validation study nested within a preterm birth research cohort were collected between January 2017 and July 2018 and analyzed in a Canadian newborn screening laboratory where the laboratory's disease panel and screening thresholds were applied. RESULTS:A total of 1661 newborn samples (520 heel-prick and 1141 cord blood samples) were available for analysis. Based on the applied screening thresholds, 61 samples (22 by heel-prick and 39 by cord blood) were screen positive for conditions included in the Canadian disease panel. Congenital hypothyroidism was the most common determination for heel-prick (n = 17) and cord blood (n = 12) samples. Carriers of hemoglobinopathy variants were identified in 6.9% of both tested heel-prick and cord blood samples. CONCLUSIONS:This study provides insight into the nature and frequency of treatable congenital conditions in a rural Bangladesh community where such data were previously unavailable. As comment to the feasibility of newborn screening in the region we confirm that screening based on cord blood sampling continues to be the most acceptable modality to parents in such settings. Acknowledged barriers include early infant discharge, which may affect the reliability of initial screening thresholds to determine disease risk. We further highlight the importance of continuing efforts in the country to identify infants with congenital hypothyroidism.
| S-EPMC6354381 | biostudies-literature
Project description:ObjectiveTo assess the external validity of all published first-trimester prediction models based on routinely collected maternal predictors for the risk of small- and large-for-gestational-age (SGA and LGA) infants. Furthermore, the clinical potential of the best-performing models was evaluated.DesignMulticentre prospective cohort.SettingThirty-six midwifery practices and six hospitals (in the Netherlands).PopulationPregnant women were recruited at <16 weeks of gestation between 1 July 2013 and 31 December 2015.MethodsPrediction models were systematically selected from the literature. Information on predictors was obtained by a web-based questionnaire. Birthweight centiles were corrected for gestational age, parity, fetal sex, and ethnicity.Main outcome measuresPredictive performance was assessed by means of discrimination (C-statistic) and calibration.ResultsThe validation cohort consisted of 2582 pregnant women. The outcomes of SGA <10th percentile and LGA >90th percentile occurred in 203 and 224 women, respectively. The C-statistics of the included models ranged from 0.52 to 0.64 for SGA (n = 6), and from 0.60 to 0.69 for LGA (n = 6). All models yielded higher C-statistics for more severe cases of SGA (<5th percentile) and LGA (>95th percentile). Initial calibration showed poor-to-moderate agreement between the predicted probabilities and the observed outcomes, but this improved substantially after recalibration.ConclusionThe clinical relevance of the models is limited because of their moderate predictive performance, and because the definitions of SGA and LGA do not exclude constitutionally small or large infants. As most clinically relevant fetal growth deviations are related to 'vascular' or 'metabolic' factors, models predicting hypertensive disorders and gestational diabetes are likely to be more specific.Tweetable abstractThe clinical relevance of prediction models for the risk of small- and large-for-gestational-age is limited.
| S-EPMC6590121 | biostudies-literature