1. Limiting adverse birth outcomes in resource-limited settings (LABOR): protocol of a prospective intrapartum cohort study.
- Author
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Adu-Amankwah A, Bellad MB, Benson AM, Beyuo TK, Bhandankar M, Charanthimath U, Chisembele M, Cole SR, Dhaded SM, Enweronu-Laryea C, Freeman BL, Freeman NLB, Goudar SS, Jiang X, Kasaro MP, Kosorok MR, Luckett D, Mbewe FM, Misra S, Mutesu K, Nuamah MA, Oppong SA, Patterson JK, Peterson M, Pokaprakarn T, Price JT, Pujar YV, Rouse DJ, Sebastião YV, Spelke MB, Sperger J, Stringer JSA, Tuuli MG, Valancius M, and Vwalika B
- Abstract
Background: Each year, nearly 300,000 women and 5 million fetuses or neonates die during childbirth or shortly thereafter, a burden concentrated disproportionately in low- and middle-income countries. Identifying women and their fetuses at risk for intrapartum-related morbidity and death could facilitate early intervention. Methods: The Limiting Adverse Birth Outcomes in Resource-Limited Settings (LABOR) Study is a multi-country, prospective, observational cohort designed to exhaustively document the course and outcomes of labor, delivery, and the immediate postpartum period in settings where adverse outcomes are frequent. The study is conducted at four hospitals across three countries in Ghana, India, and Zambia. We will enroll approximately 12,000 women at presentation to the hospital for delivery and follow them and their fetuses/newborns throughout their labor and delivery course, postpartum hospitalization, and up to 42 days thereafter. The co-primary outcomes are composites of maternal (death, hemorrhage, hypertensive disorders, infection) and fetal/neonatal adverse events (death, encephalopathy, sepsis) that may be attributed to the intrapartum period. The study collects extensive physiologic data through the use of physiologic sensors and employs medical scribes to document examination findings, diagnoses, medications, and other interventions in real time. Discussion: The goal of this research is to produce a large, sharable dataset that can be used to build statistical algorithms to prospectively stratify parturients according to their risk of adverse outcomes. We anticipate this research will inform the development of new tools to reduce peripartum morbidity and mortality in low-resource settings., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Adu-Amankwah A et al.)
- Published
- 2022
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