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Development and validation of a simplified algorithm for neonatal gestational age assessment – protocol for the Alliance for Maternal Newborn Health Improvement (AMANHI) prospective cohort study
OBJECTIVE: The objective of the Alliance for Maternal and Newborn Health Improvement (AMANHI) gestational age study is to develop and validate a programmatically feasible and simple approach to accurately assess gestational age of babies after they are born. The study will provide accurate, populati...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Edinburgh University Global Health Society
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5665676/ https://www.ncbi.nlm.nih.gov/pubmed/29163937 http://dx.doi.org/10.7189/jogh.07.021201 |
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author | Baqui, Abdullah Ahmed, Parvez Dasgupta, Sushil Kanta Begum, Nazma Rahman, Mahmoodur Islam, Nasreen Quaiyum, Mohammad Kirkwood, Betty Edmond, Karen Shannon, Caitlin Newton, Samuel Hurt, Lisa Jehan, Fyezah Nisar, Imran Hussain, Atiya Nadeem, Naila Ilyas, Muhammad Zaidi, Anita Sazawal, Sunil Deb, Saikat Dutta, Arup Dhingra, Usha Ali, Said Moh’d Hamer, Davidson H. Semrau, Katherine EA Straszak–Suri, Marina Grogan, Caroline Bemba, Godfrey Lee, Anne CC Wylie, Blair J Manu, Alexander Yoshida, Sachiyo Bahl, Rajiv |
author_facet | Baqui, Abdullah Ahmed, Parvez Dasgupta, Sushil Kanta Begum, Nazma Rahman, Mahmoodur Islam, Nasreen Quaiyum, Mohammad Kirkwood, Betty Edmond, Karen Shannon, Caitlin Newton, Samuel Hurt, Lisa Jehan, Fyezah Nisar, Imran Hussain, Atiya Nadeem, Naila Ilyas, Muhammad Zaidi, Anita Sazawal, Sunil Deb, Saikat Dutta, Arup Dhingra, Usha Ali, Said Moh’d Hamer, Davidson H. Semrau, Katherine EA Straszak–Suri, Marina Grogan, Caroline Bemba, Godfrey Lee, Anne CC Wylie, Blair J Manu, Alexander Yoshida, Sachiyo Bahl, Rajiv |
collection | PubMed |
description | OBJECTIVE: The objective of the Alliance for Maternal and Newborn Health Improvement (AMANHI) gestational age study is to develop and validate a programmatically feasible and simple approach to accurately assess gestational age of babies after they are born. The study will provide accurate, population–based rates of preterm birth in different settings and quantify the risks of neonatal mortality and morbidity by gestational age and birth weight in five South Asian and sub–Saharan African sites. METHODS: This study used on–going population–based cohort studies to recruit pregnant women early in pregnancy (<20 weeks) for a dating ultrasound scan. Implementation is harmonised across sites in Ghana, Tanzania, Zambia, Bangladesh and Pakistan with uniform protocols and standard operating procedures. Women whose pregnancies are confirmed to be between 8 to 19 completed weeks of gestation are enrolled into the study. These women are followed up to collect socio–demographic and morbidity data during the pregnancy. When they deliver, trained research assistants visit women within 72 hours to assess the baby for gestational maturity. They assess for neuromuscular and physical characteristics selected from the Ballard and Dubowitz maturation assessment scales. They also measure newborn anthropometry and assess feeding maturity of the babies. Computer machine learning techniques will be used to identify the most parsimonious group of signs that correctly predict gestational age compared to the early ultrasound date (the gold standard). This gestational age will be used to categorize babies into term, late preterm and early preterm groups. Further, the ultrasound–based gestational age will be used to calculate population–based rates of preterm birth. IMPORTANCE OF THE STUDY: The AMANHI gestational age study will make substantial contribution to improve identification of preterm babies by frontline health workers in low– and middle– income countries using simple evaluations. The study will provide accurate preterm birth estimates. This new information will be crucial to planning and delivery of interventions for improving preterm birth outcomes, particularly in South Asia and sub–Saharan Africa. |
format | Online Article Text |
id | pubmed-5665676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Edinburgh University Global Health Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-56656762017-11-21 Development and validation of a simplified algorithm for neonatal gestational age assessment – protocol for the Alliance for Maternal Newborn Health Improvement (AMANHI) prospective cohort study Baqui, Abdullah Ahmed, Parvez Dasgupta, Sushil Kanta Begum, Nazma Rahman, Mahmoodur Islam, Nasreen Quaiyum, Mohammad Kirkwood, Betty Edmond, Karen Shannon, Caitlin Newton, Samuel Hurt, Lisa Jehan, Fyezah Nisar, Imran Hussain, Atiya Nadeem, Naila Ilyas, Muhammad Zaidi, Anita Sazawal, Sunil Deb, Saikat Dutta, Arup Dhingra, Usha Ali, Said Moh’d Hamer, Davidson H. Semrau, Katherine EA Straszak–Suri, Marina Grogan, Caroline Bemba, Godfrey Lee, Anne CC Wylie, Blair J Manu, Alexander Yoshida, Sachiyo Bahl, Rajiv J Glob Health Research Theme 8: Alliance for Maternal and Newborn Health Improvement OBJECTIVE: The objective of the Alliance for Maternal and Newborn Health Improvement (AMANHI) gestational age study is to develop and validate a programmatically feasible and simple approach to accurately assess gestational age of babies after they are born. The study will provide accurate, population–based rates of preterm birth in different settings and quantify the risks of neonatal mortality and morbidity by gestational age and birth weight in five South Asian and sub–Saharan African sites. METHODS: This study used on–going population–based cohort studies to recruit pregnant women early in pregnancy (<20 weeks) for a dating ultrasound scan. Implementation is harmonised across sites in Ghana, Tanzania, Zambia, Bangladesh and Pakistan with uniform protocols and standard operating procedures. Women whose pregnancies are confirmed to be between 8 to 19 completed weeks of gestation are enrolled into the study. These women are followed up to collect socio–demographic and morbidity data during the pregnancy. When they deliver, trained research assistants visit women within 72 hours to assess the baby for gestational maturity. They assess for neuromuscular and physical characteristics selected from the Ballard and Dubowitz maturation assessment scales. They also measure newborn anthropometry and assess feeding maturity of the babies. Computer machine learning techniques will be used to identify the most parsimonious group of signs that correctly predict gestational age compared to the early ultrasound date (the gold standard). This gestational age will be used to categorize babies into term, late preterm and early preterm groups. Further, the ultrasound–based gestational age will be used to calculate population–based rates of preterm birth. IMPORTANCE OF THE STUDY: The AMANHI gestational age study will make substantial contribution to improve identification of preterm babies by frontline health workers in low– and middle– income countries using simple evaluations. The study will provide accurate preterm birth estimates. This new information will be crucial to planning and delivery of interventions for improving preterm birth outcomes, particularly in South Asia and sub–Saharan Africa. Edinburgh University Global Health Society 2017-12 2017-11-01 /pmc/articles/PMC5665676/ /pubmed/29163937 http://dx.doi.org/10.7189/jogh.07.021201 Text en Copyright © 2017 by the Journal of Global Health. All rights reserved. http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. |
spellingShingle | Research Theme 8: Alliance for Maternal and Newborn Health Improvement Baqui, Abdullah Ahmed, Parvez Dasgupta, Sushil Kanta Begum, Nazma Rahman, Mahmoodur Islam, Nasreen Quaiyum, Mohammad Kirkwood, Betty Edmond, Karen Shannon, Caitlin Newton, Samuel Hurt, Lisa Jehan, Fyezah Nisar, Imran Hussain, Atiya Nadeem, Naila Ilyas, Muhammad Zaidi, Anita Sazawal, Sunil Deb, Saikat Dutta, Arup Dhingra, Usha Ali, Said Moh’d Hamer, Davidson H. Semrau, Katherine EA Straszak–Suri, Marina Grogan, Caroline Bemba, Godfrey Lee, Anne CC Wylie, Blair J Manu, Alexander Yoshida, Sachiyo Bahl, Rajiv Development and validation of a simplified algorithm for neonatal gestational age assessment – protocol for the Alliance for Maternal Newborn Health Improvement (AMANHI) prospective cohort study |
title | Development and validation of a simplified algorithm for neonatal gestational age assessment – protocol for the Alliance for Maternal Newborn Health Improvement (AMANHI) prospective cohort study |
title_full | Development and validation of a simplified algorithm for neonatal gestational age assessment – protocol for the Alliance for Maternal Newborn Health Improvement (AMANHI) prospective cohort study |
title_fullStr | Development and validation of a simplified algorithm for neonatal gestational age assessment – protocol for the Alliance for Maternal Newborn Health Improvement (AMANHI) prospective cohort study |
title_full_unstemmed | Development and validation of a simplified algorithm for neonatal gestational age assessment – protocol for the Alliance for Maternal Newborn Health Improvement (AMANHI) prospective cohort study |
title_short | Development and validation of a simplified algorithm for neonatal gestational age assessment – protocol for the Alliance for Maternal Newborn Health Improvement (AMANHI) prospective cohort study |
title_sort | development and validation of a simplified algorithm for neonatal gestational age assessment – protocol for the alliance for maternal newborn health improvement (amanhi) prospective cohort study |
topic | Research Theme 8: Alliance for Maternal and Newborn Health Improvement |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5665676/ https://www.ncbi.nlm.nih.gov/pubmed/29163937 http://dx.doi.org/10.7189/jogh.07.021201 |
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