Metabolic gestational age assessment in low resource settings: a validation protocol

Gates Open Research

article

A. Brianne Bota¹  Victoria Ward²  Stephen Hawken¹  Lindsay A. Wilson¹  Monica Lamoureux³  Robin Ducharme¹  Malia S. Q. Murphy¹  Kathryn M. Denize³  Matthew Henderson³  Samir K. Saha⁴  Salma Akther⁴  Nancy A. Otieno⁵  Stephen Munga⁵  Raphael O. Atito⁵  Jeffrey S. A. Stringer⁶  Humphrey Mwape⁷  Joan T. Price⁶  Hilda Angela Mujuru⁸  Gwendoline Chimhini⁸  Thulani Magwali⁹  Louisa Mudawarima⁸  Pranesh Chakraborty³  Gary L. Darmstadt²  Kumanan Wilson¹ 

[1] Clinical Epidemiology Program, Ottawa Health Research Institute;Department of Pediatrics, Stanford University School of Medicine;Newborn Screening Ontario, Children's Hospital of Eastern Ontario;Child Health Research Foundation;Kenya Medical Research Institute ,(KEMRI), Center for Global Health Research;Department of Obstetrics and Gynecology, UNC School of Medicine;UNC Global Projects Zambia;Department of Paediatrics and Child Health, University of Zimbabwe;Department of Obstetrics and Gynaecology, University of Zimbabwe;Department of Medicine, University of Ottawa;Bruyère Research Institute

关键词: gestational age;    newborn screening;    preterm birth;    machine learning;    prediction modeling;   

DOI  :  10.12688/gatesopenres.13155.2

学科分类：电子与电气工程

来源: American Journal Of Pharmtech Research

【 摘 要 】

Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children’s Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario’s newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.

【 授权许可】

CC BY