Postnatal Prediction of Gestational Age Using Newborn Fetal Hemoglobin Levels

Wilson, Kumanan; Hawken, Steven; Murphy, Malia S. Q.; Atkinson, Katherine; Potter, Beth K.; Sprague, Ann E.; Walker, Mark; Chakraborty, Pranesh; Little, Julian

doi:10.1016/j.ebiom.2016.11.032

Cited by 30 publications

(51 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Because of the known relationship between the ratio of fetal-to-adult hemoglobin (Hb) levels and GA [25], and the relative ease of measuring Hb levels compared to other newborn screening analytes (measured by HPLC versus MS/MS), we investigated the ratio of fetal-to-adult Hb as a potential new predictor of GA in our original Ontario sample. Though insufficient to predict GA on its own, the ratio of fetal-to-adult Hb in combination with clinical factors such as sex and birthweight estimated GA better than clinical covariates alone and improved upon the performance of our original Ontario-based algorithm described above [26]. Notably, Hb levels are relatively consistent and stable between blood spot samples taken via cord blood and heel-prick [27].…”

Section: Model Refinement and Ethnic Validationmentioning

confidence: 80%

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

Wilson

Murphy

Ducharme

et al. 2019

Expert Review of Proteomics

Self Cite

View full text Add to dashboard Cite

Introduction: Preterm birth is a major global health concern, contributing to 35% of all neonatal deaths in 2016. Given the importance of accurately ascertaining estimates of preterm birth and in light of current limitations in postnatal gestational age (GA) estimation, novel methods of estimating GA postnatally in the absence of prenatal ultrasound are needed. Previous work has demonstrated the potential for metabolomics to estimate GA by analyzing data captured through routine newborn screening. Areas covered: Circulating analytes found in newborn blood samples vary by GA. Leveraging newborn screening and demographic data, our group developed an algorithm capable of estimating GA postnatally to within approximately 1 week of ultrasound-validated GA. Since then, we have built on the model by including additional analytes and validating the model's performance through internal and external validation studies, and through implementation of the model internationally. Expert opinion: Currently, using metabolomics to estimate GA postnatally holds considerable promise but is limited by issues of cost-effectiveness and resource access in low-income settings. Future work will focus on enhancing the precision of this approach while prioritizing point-of-care testing that is both accessible and acceptable to individuals in low-resource settings.

show abstract

Section: Model Refinement and Ethnic Validationmentioning

confidence: 80%

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

Wilson

Murphy

Ducharme

et al. 2019

Expert Review of Proteomics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although limited sample size prevented evaluation of the model among small-for gestational age or low birthweight infants, recent work from our group has demonstrated comparable accuracy among this infant subpopulation. 14 This is particularly important when considering the potential to implement metabolic dating tools in lowand middle income countries given the prevalence of low birthweight infants in low-resource communities. In addition to continuous estimates, provision of gestational age estimates across dichotomous thresholds (eg, 37 weeks gestational age) may be useful for the purpose of population surveillance.…”

Section: Discussionmentioning

confidence: 99%

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

Hawken¹,

Ducharme

Murphy

et al. 2017

BMJ Open

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Specifically, we propose to test four models using methods we have previously described. 18 While our original published approach included model development based on the full panel of newborn screening analytes, utility of such an algorithm in low-resource settings is likely to be hampered by limited access to advanced laboratory technology, including tandem mass spectrometry (MS/MS). For this reason, we have developed simplified models of gestational age prediction that rely on non-MS/ MS-derived analytes and have published the comparative performance of these models in a Canadian cohort.…”

Section: Potential For Interventionmentioning

confidence: 99%

“…For this reason, we have developed simplified models of gestational age prediction that rely on non-MS/ MS-derived analytes and have published the comparative performance of these models in a Canadian cohort. 18 In brief, we propose to assess the performance of each of the following models: 1) birth weight alone; 2) combination of birth weight and fetal/adult haemoglobin levels; 3) combination of birth weight, haemoglobin levels, thyroid-stimulating hormone and 17-OHP (all non-mass-spectrometry-derived analytes); and 4) birth weight and the full panel of newborn screening analytes. Sex and multiple birth (yes, no) will be included in all models.…”

Section: Potential For Interventionmentioning

confidence: 99%

“…Fetal (F, F1) and adult (A) haemoglobin levels will be modelled as (F+F1)/(A+F+F1) as previously described. 18 A weighting scheme will be used such that newborns with lower gestational ages will be weighted more heavily for model development, to ensure that term and preterm newborn infants both drive model fitting to a similar degree and to prevent parameter estimates from being overwhelmingly driven by term newborns.…”

Section: Predictive Modellingmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2017

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Postnatal Prediction of Gestational Age Using Newborn Fetal Hemoglobin Levels

Cited by 30 publications

References 22 publications

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

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

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

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

Contact Info

Product

Resources

About