External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh.
epidemiology
gestational age
global health
human
newborn screening
prediction modeling
preterm birth
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
19 03 2019
19 03 2019
Historique:
received:
05
10
2018
accepted:
08
02
2019
entrez:
20
3
2019
pubmed:
20
3
2019
medline:
31
3
2020
Statut:
epublish
Résumé
This study sought to evaluate the performance of metabolic gestational age estimation models developed in Ontario, Canada in infants born in Bangladesh. Cord and heel prick blood spots were collected in Bangladesh and analyzed at a newborn screening facility in Ottawa, Canada. Algorithm-derived estimates of gestational age and preterm birth were compared to ultrasound-validated estimates. 1036 cord blood and 487 heel prick samples were collected from 1069 unique newborns. The majority of samples (93.2% of heel prick and 89.9% of cord blood) were collected from term infants. When applied to heel prick data, algorithms correctly estimated gestational age to within an average deviation of 1 week overall (root mean square error = 1.07 weeks). Metabolic gestational age estimation provides accurate population-level estimates of gestational age in this data set. Models were effective on data obtained from both heel prick and cord blood, the latter being a more feasible option in low-resource settings.
Identifiants
pubmed: 30887951
doi: 10.7554/eLife.42627
pii: 42627
pmc: PMC6424558
doi:
pii:
Types de publication
Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Bill and Melinda Gates Foundation
ID : OPP1141535
Pays : International
Informations de copyright
© 2019, Murphy et al.
Déclaration de conflit d'intérêts
MM, SH, WC, LW, ML, MH, JP, AC, CG, EL, BP, MW, JL, AR, PC, KW No competing interests declared
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