Gestational age dating using newborn metabolic screening: A validation study in Busia, Uganda

Scott P Oltman; Elizabeth A Jasper; Richard Kajubi; Teddy Ochieng; Abel Kakuru; Harriet Adrama; Martin Okitwi; Peter Olwoch; Moses Kamya; Bruce Bedell; Molly McCarthy; John Dagle; Prasanna Jagannathan; Tamara D Clark; Grant Dorsey; Larry Rand; Theodore Ruel; Elizabeth E Rogers; Kelli K Ryckman; Laura L Jelliffe-Pawlowski

doi:10.7189/jogh.11.04012

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Gestational age dating using newborn metabolic screening: A validation study in Busia, Uganda

Journal article

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Gestational age dating using newborn metabolic screening: A validation study in Busia, Uganda

Scott P Oltman, Elizabeth A Jasper, Richard Kajubi, Teddy Ochieng, Abel Kakuru, Harriet Adrama, Martin Okitwi, Peter Olwoch, Moses Kamya, Bruce Bedell, …

Journal of global health, Vol.11, pp.1-9

02/11/2021

DOI: 10.7189/jogh.11.04012

PMCID: PMC7916447

PMID: 33692896

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Abstract

Background Limited ultrasound capacity in low-resource settings makes correct gestational age (GA) dating difficult. Previous work demonstrated that newborn metabolic profiles can accurately determine gestational age, but this relationship has not been evaluated in low-income countries. The objective of this study was to validate and adapt a metabolic GA dating model developed using newborn blood spots for use in a low-resource setting in rural Uganda. Methods A cohort of pregnant women was followed prospectively and heel stick blood spots were collected from 666 newborns in Busia, Uganda at the time of delivery. They were dried, frozen, and shipped to the US where they were tested for 47 metabolites. Metabolic model performance was assessed using early ultrasound determined GA as the standard. Models tested included previously built multivariable models and models specifically adapted to the Busia population. Results The previously built model successfully dated 81.2% of newborns within two weeks of their ultrasound GA. Only 4.8% of GAs were off by greater than three weeks. In the model adapted to the local population, 89.2% of GAs matched their corresponding ultrasound to within two weeks. The model-derived preterm birth rate was 7.2% compared to 5.9% by ultrasound. Conclusions These results suggest that metabolic dating is a reliable method to determine GA in a low-income setting. Metabolic dating offers the potential to better elucidate preterm birth rates in low-resource settings, which is important for assessing population-level patterns, tailoring clinical care, and understanding the developmental trajectories of preterm infants.

Details

Title: Subtitle: Gestational age dating using newborn metabolic screening: A validation study in Busia, Uganda
Creators: Scott P Oltman - Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, California, USA, Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA
Elizabeth A Jasper - Department of Epidemiology, University of Iowa College of Public Health, Iowa City, Iowa, USA
Richard Kajubi - Infectious Diseases Research Collaboration, Kampala, Uganda
Teddy Ochieng - Infectious Diseases Research Collaboration, Kampala, Uganda
Abel Kakuru - Infectious Diseases Research Collaboration, Kampala, Uganda
Harriet Adrama - Infectious Diseases Research Collaboration, Kampala, Uganda
Martin Okitwi - Infectious Diseases Research Collaboration, Kampala, Uganda
Peter Olwoch - Infectious Diseases Research Collaboration, Kampala, Uganda
Moses Kamya - Infectious Diseases Research Collaboration, Kampala, Uganda, Department of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
Bruce Bedell - Department of Pediatrics, University of Iowa, Iowa City, Iowa, USA
Molly McCarthy - Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA
John Dagle - Department of Pediatrics, University of Iowa, Iowa City, Iowa, USA
Prasanna Jagannathan - Department of Medicine, Stanford University Medical Center, Stanford, California, USA
Tamara D Clark - Department of Medicine, University of California San Francisco School of Medicine, San Francisco, California, USA
Grant Dorsey - Department of Medicine, University of California San Francisco School of Medicine, San Francisco, California, USA
Larry Rand - Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA, Department of Obstetrics, Gynecology, & Reproductive Sciences, University of California San Francisco, San Francisco, California, USA
Theodore Ruel - Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
Elizabeth E Rogers - Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
Kelli K Ryckman - Department of Epidemiology, University of Iowa College of Public Health, Iowa City, Iowa, USA
Laura L Jelliffe-Pawlowski - Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, California, USA, Preterm Birth Initiative, University of California San Francisco, San Francisco, California, USA
Resource Type: Journal article
Publication Details: Journal of global health, Vol.11, pp.1-9
DOI: 10.7189/jogh.11.04012
PMID: 33692896
PMCID: PMC7916447
NLM abbreviation: J Glob Health
ISSN: 2047-2978
eISSN: 2047-2986
Language: English
Date published: 02/11/2021
Academic Unit: Stead Family Department of Pediatrics; Epidemiology; Biochemistry and Molecular Biology; Neonatology
Record Identifier: 9984214704002771

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