The NHLBI has invested extensively in the Pediatric Cardiac Genomics Consortium (PCGC), recognizing that translating genomic discoveries into optimized management and therapeutic strategies for congenital heart disease (CHD) can only be achieved in the context of multi-center, collaborative research. Currently, the PCGC is lacking two fundamental capabilities that hinder its ability to define the genomic basis for CHD outcomes: (1) a robust mechanism for extracting pertinent, machine-readable clinical data from Electronic Health Records (EHRs) across multiple institutions; and (2) a robust Artificial Intelligence (AI) platform that is capable of teasing apart the complex interplay between maternal factors, phenotypes, genotypes, gene functions and clinical outcomes. Here, we propose innovative solutions to these challenges, by assembling teams of content experts to leverage existing infrastructure to extract relevant outcomes directly from the EHR of participating PCGC Centers and by designing best-practice AI tools for outcomes research. Our principal goal is provide the vision, infrastructure and expertise to collaboratively empower CHD outcomes research, foster knowledge exchange, and train the next generation of genomic scientists. We propose to leverage existing data infrastructure to obtain Electronic Health Records (EHR) and other clinical variables at scale by partnering with other research networks to create a PCGC Data Resource. Using this resource, we will create and deploy a platform of Artificial Intelligence (AI)-based predictors for CHD outcomes research, with the goal of translating genomic discoveries into improved management and therapeutic strategies for CHD.
The overall goal of this project is to apply genomic discoveries toward prediction of clinical outcomes in congenital heart disease. The proposal encompasses innovative concepts and methodologies that will advance the field of congenital heart disease in a very practical manner.
