In Gates open research
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.
Adu-Amankwah Amanda, Bellad Mrutunjaya B, Benson Aimee M, Beyuo Titus K, Bhandankar Manisha, Charanthimath Umesh, Chisembele Maureen, Cole Stephen R, Dhaded Sangappa M, Enweronu-Laryea Christabel, Freeman Bethany L, Freeman Nikki L B, Goudar Shivaprasad S, Jiang Xiaotong, Kasaro Margaret P, Kosorok Michael R, Luckett Daniel, Mbewe Felistas M, Misra Sujata, Mutesu Kunda, Nuamah Mercy A, Oppong Samuel A, Patterson Jackie K, Peterson Marc, Pokaprakarn Teeranan, Price Joan T, Pujar Yeshita V, Rouse Dwight J, Sebastião Yuri V, Spelke M Bridget, Sperger John, Stringer Jeffrey S A, Tuuli Methodius G, Valancius Michael, Vwalika Bellington
2022
Labor, adverse birth outcomes, delivery, intrapartum, machine learning, postpartum
