Abstract
Background:Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emergedas a novel method of acquiring population-level preterm birth estimates in low resource settings.To date, model development and validation have been carried out in North American settings.Validation outside of these settings is warranted. Methods:This was a retrospective database studyusing data from newborn screening programs in Canada, the Philippines and China.ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent group of infants, and externally validated in cohorts of infants from the Philippines and China. Results:Cohortsincluded 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China. For the full model including sex, birth weight and metabolomic markers, GA estimates were within 5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within 6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions:Accuracy of metabolic GA algorithms was attenuated when applied in external settings. Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. Asinnovatorslookto take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility.
Highlights
Global- and population-level surveillance of preterm birth is challenging
In this study, we demonstrated that the performance of gestational dating algorithms developed in a cohort of infants from Ontario, Canada including newborn screening metabolomic markers from dried blood-spot samples was attenuated when the models were applied to data derived from external laboratories and populations
When these Canadian-based models were tailored to the analytes available from newborn screening programs in Shanghai, China and Manila, Philippines, the models were less accurate in estimating absolute gestational age (GA) in infant cohorts from these locations than when the same models were applied to an Ontario infant cohort
Summary
Global- and population-level surveillance of preterm birth is challenging. Inconsistent use of international standards to define preterm birth and gestational age (GA) categories, the range of methods and timing used for GA assessment, and inadequate jurisdictional or national health data systems all hamper reliable population estimates of preterm birth[1]. The second criteria led to the disproportionate exclusion of preterm infants who more often had delayed sample collection, despite this not being recommended practice. This exclusion biased the rate of preterm gestation observed in our Ontario study cohort downward, but it was unlikely to have had any important impact on GA model development, as we still had a large sample size across the full spectrum of gestational ages at birth to allow robust model development and performance evaluation. Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility
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