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Estimating onset time from longitudinal data in the presence of measurement error with application to estimating gestational age from maternal anthropometry during pregnancy.

ABSTRACT: Accurate assessment of gestational age at birth is necessary for optimal pediatric care. In high resource countries, several methods using ultrasound have been proposed to assess gestational age at birth; however, these methods are not easily accessible for low-resource populations. We develop a shared random parameter model for estimating gestational age at birth from longitudinal maternal anthropometry that incorporates additional maternal information from the last menstrual period, a measure of gestational age collected with sizable measurement error. The proposed methodology is evaluated using simulation studies under a training-test set paradigm. In addition, we propose methodology to validate prediction when some measurements of the gold standard are collected with measurement error. We illustrate the proposed methodologies with data from the NICHD Fetal Growth Studies.

SUBMITTER: Ortega-Villa AM

PROVIDER: S-EPMC6497444 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Estimating onset time from longitudinal data in the presence of measurement error with application to estimating gestational age from maternal anthropometry during pregnancy.

Ortega-Villa Ana M AM Albert Paul S PS

Statistics in medicine 20180917 30

Accurate assessment of gestational age at birth is necessary for optimal pediatric care. In high resource countries, several methods using ultrasound have been proposed to assess gestational age at birth; however, these methods are not easily accessible for low-resource populations. We develop a shared random parameter model for estimating gestational age at birth from longitudinal maternal anthropometry that incorporates additional maternal information from the last menstrual period, a measure ...[more]

PMID: 30295951

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Project description:BackgroundThird trimester fetal anthropometric parameters are known to predict neonatal complications. A better understanding of predictors of adverse fetal parameters might help to personalize the use and frequency of fetal ultrasound. The objectives of this study were: (a) to evaluate the utility of maternal sociodemographic, anthropometric and metabolic predictors to predict 3rd trimester fetal anthropometric parameters in women with gestational diabetes mellitus (GDM), (b) to assess whether the impact of these maternal predictors is fetal sex-dependent, and (c) to provide a risk stratification for markers of fetal overgrowth (fetal weight centile (FWC) and fetal abdominal circumference centile (FACC) depending on prepregnancy BMI and gestational weight gain (GWG) until the 1st GDM visit.MethodsThis prospective study included 189 women with GDM. Maternal predictors were age, ethnicity, prepregnancy BMI, GWG and excessive weight gain until the 1st GDM visit, fasting, 1-hour and 2-hour blood glucose oral glucose tolerance test values, HbA1c at the 1st visit and medical treatment requirement. Fetal outcomes included FWC, FWC >90% and <10%, FACC, FACC >90% and <10%, at 29 0/7 to 35 6/7 weeks of gestational age. We performed univariate and multivariate regression analyses and probability analyses.ResultsIn multivariate analyses, prepregnancy BMI was associated with FWC, FWC > 90% and FACC. GWG until the 1st GDM visit was associated with FWC, FACC and FACC > 90% (all p ≤ 0.045). Other maternal parameters were not significantly associated with fetal anthropometry in multivariate analyses (all p ≥ 0.054). In female fetuses, only GWG was associated with FACC (p= 0.044). However, in male fetuses, prepregnancy BMI was associated with FWC, FWC > 90% and FACC and GWG with FWC in multivariate analyses (all p ≤ 0.030). In women with a prepregnancy BMI of ≥ 25 kg/m2 and a GWG until the 1st GDM visit ≥ 10.3 kg (mean GWG), the risk for FWC > 90% and FACC > 90% was 5.3 and 4 times higher than in their counterparts.ConclusionsA personalized fetal ultrasound surveillance guided by fetal sex, prepregnancy BMI and GWG may be beneficial in reducing adverse fetal and neonatal outcomes.

Project description:ObjectivePregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy.Study designA longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation.ResultsThe following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%).ConclusionThe plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.

Dataset Information

Estimating onset time from longitudinal data in the presence of measurement error with application to estimating gestational age from maternal anthropometry during pregnancy.

Publications

Estimating onset time from longitudinal data in the presence of measurement error with application to estimating gestational age from maternal anthropometry during pregnancy.

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