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In Gates open research

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.

Bota A Brianne, Ward Victoria, Hawken Stephen, Wilson Lindsay A, Lamoureux Monica, Ducharme Robin, Murphy Malia S Q, Denize Kathryn M, Henderson Matthew, Saha Samir K, Akther Salma, Otieno Nancy A, Munga Stephen, Atito Raphael O, Stringer Jeffrey S A, Mwape Humphrey, Price Joan T, Mujuru Hilda Angela, Chimhini Gwendoline, Magwali Thulani, Mudawarima Louisa, Chakraborty Pranesh, Darmstadt Gary L, Wilson Kumanan

2020

gestational age, machine learning, newborn screening, prediction modeling, preterm birth