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

Abstract

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.

External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh

“…Specifically, we sought to compare estimates of gestational age derived from our algorithms, through the analysis of newborn blood spots, against estimates of gestational age determined by first-trimester ultrasound. A version of the protocol for this study has been published ( Murphy et al, 2017 ). Due to logistical challenges in initiating the study, fewer samples were collected than initially anticipated in our protocol and low numbers of infants with gestational age below 34 weeks.…”

“…In the process of applying newborn screening procedures for the analysis of samples, results of ‘screen negative’ and ‘screen positive’ were generated for conditions screened for by the NSO program. Management of incidental clinical findings (screen-positive cases) has been reported elsewhere ( Murphy et al, 2017 ).…”

Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers

“…Analytes were included as candidate predictors in GA estimation models based on their routine measurement as part of Ontario’s expanded newborn screening program, including hemoglobin profiles, amino acids, acylcarnitines, hormone and endocrine markers, enzymes and co-enzymes (Table 2). Management of incidental clinical findings (screen-positive cases) for conditions screened for by the NSO program identified in the course of analyzing samples have been reported elsewhere 21,22 .…”

Metabolic gestational age assessment in low resource settings: a validation protocol

“…Study personnel will collect, handle and transport heel-prick and cord dried blood spot samples from participants according to study-specific standard operating procedures ( Figure 1 ). Sample collection procedures have been published previously 12 . In brief, cord blood will be collected within 30 minutes of delivery of the placenta into an uncoated, sterile syringe.…”

“…Reporting procedures from the NSO to the collection site include provision of reports on sample quality, on newborns’ risk of congenital hypothyroidism (CH), medium-chain acyl-CoA dehydrogenase deficiency (MCADD) and haemoglobinopathies (HGBs) and quarterly reports summarizing study progress. Adapted from 12 under a CC BY 4.0 license .…”