Journal article
Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation
Journal of global health, Vol.11, pp.1-11
07/17/2021
DOI: 10.7189/jogh.11.04044
PMCID: PMC8285766
PMID: 34326994
Abstract
Background Globally, 15 million infants are born preterm and another 23.2 million infants are born small for gestational age (SGA). Determining burden of preterm and SGA births, is essential for effective planning, modification of health policies and targeting interventions for reducing these outcomes for which accurate estimation of gestational age (GA) is crucial. Early pregnancy ultrasound measurements, last menstrual period and post-natal neonatal examinations have proven to be not feasible or inaccurate. Proposed algorithms for GA estimation in western populations, based on routine new-born screening, though promising, lack validation in developing country settings. We evaluated the hypothesis that models developed in USA, also predicted GA in cohorts of South Asia (575) and Sub-Saharan Africa (736) with same precision. Methods Dried heel prick blood spots collected 24-72 hours after birth from 1311 new-borns, were analysed for standard metabolic screen. Regression algorithm based, GA estimates were computed from metabolic data and compared to first trimester ultrasound validated, GA estimates (gold standard). Results Overall Algorithm (metabolites + birthweight) estimated GA to within an average deviation of 1.5 weeks. The estimated GA was within the gold standard estimate by 1 and 2 weeks for 70.5% and 90.1% new-borns respectively. Inclusion of birthweight in the metabolites model improved discriminatory ability of this method, and showed promise in identifying preterm births. Receiver operating characteristic (ROC) curve analysis estimated an area under curve of 0.86 (conservative bootstrap 95% confidence interval (CI) = 0.83 to 0.89); P < 0.001) and Youden Index of 0.58 (95% CI = 0.51 to 0.64) with a corresponding sensitivity of 80.7% and specificity of 77.6%. Conclusion Metabolic gestational age dating offers a novel means for accurate population-level gestational age estimates in LMIC settings and help preterm birth surveillance initiatives. Further research should focus on use of machine learning and newer analytic methods broader than conventional metabolic screen analytes, enabling incorporation of region-specific analytes and cord blood metabolic profiles models predicting gestational age accurately
Details
- Title: Subtitle
- Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation
- Creators
- Sunil Sazawal - Center for Public Health Kinetics, Global Division, New Delhi, India, Public Health Laboratory-IDC, Chake Chake, Pemba,TanzaniaKelli K Ryckman - University of Iowa, College of Public Health, Department of Epidemiology, Iowa City, Iowa, USAHarshita Mittal - Center for Public Health Kinetics, Global Division, New Delhi, IndiaRasheda Khanam - Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USAImran Nisar - Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, PakistanElizabeth Jasper - University of Iowa, College of Public Health, Department of Epidemiology, Iowa City, Iowa, USASayedur Rahman - Projahnmo Research Foundation, Dhaka, BangladeshUsma Mehmood - Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, PakistanSayan Das - Center for Public Health Kinetics, Global Division, New Delhi, IndiaBruce Bedell - University of Iowa, College of Public Health, Department of Epidemiology, Iowa City, Iowa, USANabidul Haque Chowdhury - Projahnmo Research Foundation, Dhaka, BangladeshAmina Barkat - Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, PakistanArup Dutta - Center for Public Health Kinetics, Global Division, New Delhi, IndiaSaikat Deb - Center for Public Health Kinetics, Global Division, New Delhi, India, Public Health Laboratory-IDC, Chake Chake, Pemba,TanzaniaSalahuddin Ahmed - Projahnmo Research Foundation, Dhaka, BangladeshFarah Khalid - Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, PakistanRubhana Raqib - International Center for Diarrheal Disease Research, Bangladesh, Mohakhali, Dhaka, BangladeshMuhammad Ilyas - Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, PakistanAmbreen Nizar - Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, PakistanSaid Mohammed Ali - Public Health Laboratory-IDC, Chake Chake, Pemba,TanzaniaAlexander Manu - World Health Organization (MCA/MRD), Geneva, SwitzerlandSachiyo Yoshida - World Health Organization (MCA/MRD), Geneva, SwitzerlandAbdullah H Baqui - Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USAFyezah Jehan - Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, PakistanUsha Dhingra - Center for Public Health Kinetics, Global Division, New Delhi, IndiaRajiv Bahl - World Health Organization (MCA/MRD), Geneva, Switzerland
- Resource Type
- Journal article
- Publication Details
- Journal of global health, Vol.11, pp.1-11
- DOI
- 10.7189/jogh.11.04044
- PMID
- 34326994
- PMCID
- PMC8285766
- NLM abbreviation
- J Glob Health
- ISSN
- 2047-2978
- eISSN
- 2047-2986
- Language
- English
- Date published
- 07/17/2021
- Academic Unit
- Stead Family Department of Pediatrics; Epidemiology
- Record Identifier
- 9984214793802771
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