Accurate prediction of gestational age using newborn screening analyte data

Wilson, Kumanan; Hawken, Steven; Potter, Beth K.; Chakraborty, Pranesh; Walker, Mark; Ducharme, Robin; Little, Julian

doi:10.1016/j.ajog.2015.10.017

Cited by 42 publications

(61 citation statements)

References 37 publications

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“…The full prediction model had an overall RMSE of 1·04 weeks, and was capable of providing accurate estimations of gestational age to ± 1 or ± 2 weeks of true gestational age in 68·7%, and 95·3% of the cohort, respectively. Consistent with our previous findings (Wilson et al, 2016), performance of all linear regression models was diminished in SGA10 infants. Comparison of linear regression model performance characteristics by gestational age is provided in Table 3.…”

Section: Resultssupporting

confidence: 92%

“…We used the same data partitioning strategy described previously (Wilson et al, 2016). In brief, the newborn cohort was divided into three dataset subsamples; one for model development, one to independently validate the choice of terms included in the final model, and one to independently test performance of the final model.…”

Section: Methodsmentioning

confidence: 99%

“…We and others have recently developed prediction models based on routinely collected newborn metabolic screening profiles that provide accurate estimates of gestational age (Jelliffe-Pawlowski et al, 2015, Ryckman et al, 2015, Wilson et al, 2016). Many newborn screening analytes used to identify rare metabolic conditions may only be reliably ascertained using tandem mass spectrometry – technology requiring significant financial resources and technical expertise.…”

Section: Introductionmentioning

confidence: 99%

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Postnatal Prediction of Gestational Age Using Newborn Fetal Hemoglobin Levels

et al. 2017

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IntroductionIn many parts of the developing world procurement of antenatal gestational age estimates is not possible, challenging provision of appropriate perinatal care. This study aimed to develop a model for postnatal gestational age estimation utilizing measures of the newborn hemoglobin levels and other metabolic analyte data derived from newborn blood spot samples.MethodsWe conducted a retrospective cohort analysis of 159,215 infants born January 2012–December 2014 in Ontario, Canada. Multivariable linear and logistic regression analyses were used to evaluate the precision of developed models.ResultsModels derived from a combination of hemoglobin ratios and birthweight were more precise at predicting gestational age (RMSE1·23 weeks) than models limited to birthweight (RMSE1·34). Models including birthweight, hemoglobin, TSH and 17-OHP levels were able to accurately estimate gestational age to ± 2 weeks in 95·3% of the cohort and discriminate ≤ 34 versus > 34 (c-statistic, 0·98). This model also performed well in small for gestational age infants (c-statistic, 0·998).DiscussionThe development of a point-of-care mechanism to allow widespread implementation of postnatal gestational age prediction tools that make use of hemoglobin or non-mass spectromietry-derived metabolites could serve areas where antenatal gestational age dating is not routinely available.

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Section: Resultssupporting

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Postnatal Prediction of Gestational Age Using Newborn Fetal Hemoglobin Levels

et al. 2017

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“…It is now well established that NBS metabolites including amino acids and acylcarnitines vary according to gestational age and birth-weight. (6-10) Gestational age and newborn weight as measures of developmental immaturity have long been used to aid in the determination of risk for acquired diseases of prematurity like NEC. As an alternative, NBS panels may be used to identify a predisposing metabolic phenotype that is associated with an acquired disease of prematurity such as NEC.…”

Section: Introductionmentioning

confidence: 99%

Acylcarnitine Profiles Reflect Metabolic Vulnerability for Necrotizing Enterocolitis in Newborns Born Premature

Sylvester

Kastenberg

Moss

et al. 2017

The Journal of Pediatrics

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Objectives Given the known risk factors for NEC, we hypothesized that metabolic dysfunction reflected in routinely collected newborn screening data would be associated with NEC in an at risk population. Study Design We conducted a retrospective cohort study using discharge records for all preterm neonatal intensive care unit admissions in California from 2005 to 2009. Infants with linked state newborn screening results were included. A model-development cohort of 94,110 preterm births from 2005 to 2008 was used to develop a risk-stratification model that was then applied to a validation cohort of 22,992 births from 2009. Results Fourteen acylcarnitines and acylcarnitine ratios were associated with increased risk of developing NEC. Each log unit increase in C5 and FC/(C16+18:1) was associated with a 78% and a 76% increased risk for developing NEC, respectively (OR 1.78, 95% CI 1.53 – 2.02, and OR 1.76, 95% CI 1.51 – 2.06). Six acylcarnitines, along with birth weight and total parenteral nutrition, were able to identify 89.8% of newborns with NEC in the model-development cohort (AUC=0.898, 95% confidence interval (CI) 0.889 – 0.907) and 90.8% of the newborns with NEC in the validation cohort (AUC=0.908, 95% CI 0.901 – 0.930). Conclusions These findings demonstrate that abnormal fatty acid metabolism is associated with prematurity and the development of NEC. Metabolic profiling through newborn screening may serve as an objective biologic surrogate of risk for the development of disease and thus facilitate disease prevention strategies.

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“…Our findings demonstrated that this reference model was capable of discriminating between categories of term and preterm birth with robust predictive ability and could also accurately classify infants across a dichotomous preterm birth threshold of 34-weeks gestational age. 13 The utility of newborn metabolic profiles for postnatal gestational dating has also been confirmed by others. 14 15 Our work suggests potential value in using dried blood spot-derived analytes for the postnatal assessment of gestational age.…”

Section: Key Questionsmentioning

confidence: 69%

Postnatal gestational age estimation using newborn screening blood spots: a proposed validation protocol

et al. 2017

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BackgroundKnowledge of gestational age (GA) is critical for guiding neonatal care and quantifying regional burdens of preterm birth. In settings where access to ultrasound dating is limited, postnatal estimates are frequently used despite the issues of accuracy associated with postnatal approaches. Newborn metabolic profiles are known to vary by severity of preterm birth. Recent work by our group and others has highlighted the accuracy of postnatal GA estimation algorithms derived from routinely collected newborn screening profiles. This protocol outlines the validation of a GA model originally developed in a North American cohort among international newborn cohorts.MethodsOur primary objective is to use blood spot samples collected from infants born in Zambia and Bangladesh to evaluate our algorithm’s capacity to correctly classify GA within 1, 2, 3 and 4 weeks. Secondary objectives are to 1) determine the algorithm's accuracy in small-for-gestational-age and large-for-gestational-age infants, 2) determine its ability to correctly discriminate GA of newborns across dichotomous thresholds of preterm birth (≤34 weeks, <37 weeks GA) and 3) compare the relative performance of algorithms derived from newborn screening panels including all available analytes and those restricted to analyte subsets. The study population will consist of infants born to mothers already enrolled in one of two preterm birth cohorts in Lusaka, Zambia, and Matlab, Bangladesh. Dried blood spot samples will be collected and sent for analysis in Ontario, Canada, for model validation.DiscussionThis study will determine the validity of a GA estimation algorithm across ethnically diverse infant populations and assess population specific variations in newborn metabolic profiles.

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Accurate prediction of gestational age using newborn screening analyte data

Abstract: Metabolic gestational dating is accurate for the prediction of gestational age and could have value in low resource settings.

Cited by 42 publications

References 37 publications

Postnatal Prediction of Gestational Age Using Newborn Fetal Hemoglobin Levels

Postnatal Prediction of Gestational Age Using Newborn Fetal Hemoglobin Levels

Acylcarnitine Profiles Reflect Metabolic Vulnerability for Necrotizing Enterocolitis in Newborns Born Premature

Postnatal gestational age estimation using newborn screening blood spots: a proposed validation protocol

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