Performance of a postnatal metabolic gestational age algorithm: a retrospective validation study among ethnic subgroups in Canada

Abstract

ObjectivesBiological modelling of routinely collected newborn screening data has emerged as a novel method for deriving postnatal gestational age estimates. Validation of published models has previously been limited to cohorts largely consisting of infants of white Caucasian ethnicity. In this study, we sought to determine the validity of a published gestational age estimation algorithm among recent immigrants to Canada, where maternal landed immigrant status was used as a surrogate measure of infant ethnicity.DesignWe conducted a retrospective validation study in infants born in Ontario between April 2009 and September 2011.SettingProvincial data from Ontario, Canada were obtained from the Institute for Clinical Evaluative Sciences.ParticipantsThe dataset included 230 034 infants born to non-landed immigrants and 70 098 infants born to immigrant mothers. The five most common countries of maternal origin were India (n=10 038), China (n=7468), Pakistan (n=5824), The Philippines (n=5441) and Vietnam (n=1408). Maternal country of origin was obtained from Citizenship and Immigration Canada’s Landed Immigrant Database.Primary and secondary outcome measuresPerformance of a postnatal gestational age algorithm was evaluated across non-immigrant and immigrant populations.ResultsRoot mean squared error (RMSE) of 1.05 weeks was observed for infants born to non-immigrant mothers, whereas RMSE ranged from 0.98 to 1.15 weeks among infants born to immigrant mothers. Area under the receiver operating characteristic curve for distinguishing term versus preterm infants (≥37 vs <37 weeks gestational age or >34 vs ≤34 weeks gestational age) was 0.958 and 0.986, respectively, in the non-immigrant subgroup and ranged from 0.927 to 0.964 and 0.966 to 0.99 in the immigrant subgroups.ConclusionsAlgorithms for postnatal determination of gestational age may be further refined by development and validation of region or ethnicity-specific models. However, our results provide reassurance that an algorithm developed from Ontario-born infant cohorts performs well across a range of ethnicities and maternal countries of origin without modification.

Postnatal gestational age estimation via newborn screening analysis: application and potential

“…In this way, we sought to determine whether one global algorithm would be adequate for estimating GA across ethnic subgroups, or whether local model calibration would likely be required in each new setting. Our results indicated that tailored algorithms may help to improve the precision of GA estimation, but our model performed well among infants from a variety of ethnic backgrounds [24], although there was some variation in accuracy of GA estimation. Among non-immigrant mothers, the model estimated GA to within an average of 1.05 weeks of true GA, while among immigrant mothers, estimates ranged from 0.98 to 1.15 weeks of true GA [24].…”

“…Our results indicated that tailored algorithms may help to improve the precision of GA estimation, but our model performed well among infants from a variety of ethnic backgrounds [24], although there was some variation in accuracy of GA estimation. Among non-immigrant mothers, the model estimated GA to within an average of 1.05 weeks of true GA, while among immigrant mothers, estimates ranged from 0.98 to 1.15 weeks of true GA [24]. This suggested that our global model could perform well across a wide variety of settings but might be further improved through local calibration in new settings.…”

Metabolic profiles derived from residual blood spot samples: A longitudinal analysis

“…Unique to this study is our evaluation of the impact of alterations in metabolic profiles over time on the performance of gestational age estimation models developed by our group. We have previously demonstrated the accuracy of such algorithms to estimate gestational age to within one week when applied to infants born in Ontario, Canada 3 7 9 . Gestational age algorithms such as those described here have the potential to provide reliable population-level estimates of preterm birth for jurisdictions where such data are currently lacking 20 .…”

“…Validation of metabolic gestational age estimation models. Our group has previously developed and validated gestational age estimation algorithms derived from newborn screening profiles and other clinical covariates 3 7 9 . Linear regression models were developed to estimate continuous gestational age, and logistic models were fit to classify infants as term (≥ 37 completed gestational age weeks) or preterm (<37 completed gestational age weeks).…”

“…Linear regression models were developed to estimate continuous gestational age, and logistic models were fit to classify infants as term (≥ 37 completed gestational age weeks) or preterm (<37 completed gestational age weeks). Published gestational age estimation models were developed and validated using metabolic profiles generated within the standard newborn screening timeframe 3 7 9 .…”