| Autor: |
Bowen Banda, Caitlin Shannon, Said M. Ali, Nazma Begum, Usma Mehmood, Alexander Manu, Usha Dhingra, Lisa Hurt, Sachiyo Yoshida, Rajiv Bahl, Julie M. Herlihy, Arup Dutta, Atifa Mohammed Suleiman, Dipak Kumar Mitra, Sunil Sazawal, Muhammad Karim, Fyezah Jehan, Muhammad Sajid, Mahmoodur Rahman, Caroline Grogan, Karen Edmond, Monica Kapasa, Atiya Hussain, Fahad Aftab, Corneille Bashagaluke Akonkwa, Muhammad Imran Nisar, Jayson Wilbur, Anne Lee, Davidson H. Hamer, Rina Paul, Blair J. Wylie, Marina Straszak-Suri, Mohammad J. Uddin, Saikat Deb, Katherine Semrau, Betty R. Kirkwood, Farzana Kausar, Fern Mweene, Sayedur Rahman, Naila Nadeem, Parvez Ahmed, Salahuddin Ahmed, Muhammad Ilyas, Pratibha Dhingra, Mohammed K. Mohammed, Abdullah H Baqui |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
BMJ Global Health, Vol 6, Iss 9 (2021) |
| ISSN: |
2059-7908 |
| Popis: |
IntroductionPreterm birth is the leading cause of child mortality. This study aimed to develop and validate programmatically feasible and accurate approaches to estimate newborn gestational age (GA) in low resource settings.MethodsThe WHO Alliance for Maternal and Newborn Health Improvement (AMANHI) study recruited pregnant women from population-based cohorts in five countries (Bangladesh, Ghana, Pakistan, Tanzania and Zambia). Women Results7428 liveborn infants were included (n=536 preterm, ConclusionThe best machine-learning model (10 neonatal characteristics and LMP) estimated GA within ±15.7 days of early ultrasound dating. Simpler models performed reasonably well with marginal increases in prediction error. These models hold promise for newborn GA estimation when ultrasound dating is unavailable. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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