External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh.

Malia Sq Murphy,Anisur Rahman,Steven Hawken,Beth K Potter,Matthew Henderson,Courtney Gravett,Mark Walker,Julian Little,Eve Lackritz,Wei Cheng,Jesmin Pervin,Monica Lamoureux,Azad Chowdhury,Pranesh Chakraborty,Kumanan Wilson,Lindsay A Wilson

doi:10.7554/elife.42627

Abstract

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 (93.2% of heel prick and 89.9% of cord blood) were collected from term infants. When applied to heel prick data, algorithms correctly estimated gestational age to within an average deviation of 1 week overall (root mean square error = 1.07 weeks). Metabolic gestational age estimation provides accurate population-level estimates of gestational age in this data set. Models were effective on data obtained from both heel prick and cord blood, the latter being a more feasible option in low-resource settings.

Highlights

Complications related to preterm birth are the leading cause of death among children under 5 years of age (March of Dimes, 2012)
We evaluated the discrimination of gestational age across a dichotomous preterm birth threshold (37 weeks vs
We demonstrate that algorithms developed using newborn screening data from Ontario, Canada are effective in deriving estimates of gestational age in infants born in Matlab, Bangladesh that are accurate to within approximately 1 to 2 weeks of ultrasound-validated gestational age

Summary

Introduction

Complications related to preterm birth are the leading cause of death among children under 5 years of age (March of Dimes, 2012). Estimating the burden of preterm birth in low-resource settings is challenging due to the absence of ultrasound technology and the unreliability of recall of last menstrual period. Strengthened data surveillance systems to more accurately assess and track changes in preterm birth across jurisdictions are urgently required (March of Dimes, 2012). Algorithms based on newborn metabolic profiles in combination with clinical covariates such as sex and birthweight have demonstrated the potential to accurately categorize infants across preterm birth categories in high-resource settings

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Conclusion