Innovative Technologies Core for the Stillbirth Research Consortium Data Coordinating Center

Research Triangle Institute, Durham NC

Investigators

Abstract

Innovative Technologies Core: Modified Project Abstract Despite impacting an estimated 1 out of every 160 pregnancies, the primary mechanisms driving stillbirth are largely unknown. Although various risk factors spanning pregnancy complications, chronic health conditions, genetics, environmental factors, and social determinants of health have been identified, they unfortunately explain only a limited proportion of the variation in stillbirth rates due to numerous barriers impeding stillbirth research. These barriers impacting stillbirth research include: the lack of standardization in data collection, diverse causes and risk factors, challenges in predictive screening, disparity in stillbirth rates across populations, and incomplete autopsy data. To address the barriers and foundational knowledge gaps in understanding the causes of stillbirth, and to inform future evidence-based preventive measures and interventions, we propose establishing an Innovative Technologies Core (ITC) for the Stillbirth Research Consortium Data Coordinating Center (DCC). This core will support the complex data harmonization and varied analytical needs of the Research Centers to advance their research objectives and maximize data comparability, enabling rigorous multi-center research that leverages strengths across institutions. Our team of expert data scientists, informaticists, statisticians, software engineers, social scientists, and domain experts, along with unparalleled DCC experience, will enable the Consortium to accelerate innovative and impactful stillbirth research through the following specific aims: (1) guide and coordinate the collection and processing of biospecimens and linking data in a centralized biorepository, (2) support the generation and standardization of high-quality, harmonized, stillbirth data, and (3) apply innovative technologies and advanced analytic methodologies, such as artificial intelligence and machine learning, to accelerate progress in stillbirth research. Our relevant domain experience coupled with our extensive expertise in informatics, data science, and innovative research technologies will allow us to provide the necessary rigor, analytical support, and technological resources to accelerate stillbirth research at an unprecedented scale. Our three-pronged aims of biospecimen collection (Aim 1), informatics (Aim 2), and data science (Aim 3) will provide comprehensive support for the Consortium to conduct innovative, collaborative research necessary to advance stillbirth knowledge and prevention.