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Accurate prediction of gestational age using newborn screening analyte data


Background — Identification of preterm births and accurate estimates of gestational age (GA) for newborns is vital to guide care for the newborn. Unfortunately, in developing countries it can be challenging to obtain estimates of GA. Routinely collected newborn screening metabolic analytes vary by GA and may be useful to estimate GA.

Objectives — We sought to develop an algorithm that could estimate GA at birth based on the analytes obtained from newborn screening.

Study Design — We conducted a population based cross-sectional study of all live births in the province of Ontario including 249,700 infants born between April 2007 and March 2009 who underwent newborn screening. We used multivariable linear and logistic regression analyses to build a model to predict gestational age using newborn screening metabolite measurements and readily available physical characteristics data (birth weight and sex).

Results — The final model of our metabolic gestational dating algorithm had an average deviation between observed and expected GA of about 1 week suggesting excellent predictive ability (adjusted R-square of 0.65, and a root mean square error of 1.06 weeks). Two thirds of GAs predicted by our model were accurate within +/- 1 week of the actual GA. Our logistic regression model was able to discriminate extremely well between term and increasingly premature categories of infants (c-statistic>0.99).

Conclusions — Metabolic gestational dating is accurate for predicting gestational age and could have value in low resource settings.



Wilson K, Hawken S, Potter B, Chakraborty P, Walker M, Ducharme R, Little J. Am J Obstet Gynecol. 2016; 214(4):513.e1-9. Epub 2015 Oct 28.

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