Bridging the Gap between Genomics and Clinical Outcomes in CHD

项目概要/摘要 NHLBI已经在儿科心脏基因组学联盟（PCGC）中进行了广泛的投资，认识到 将基因组发现转化为先天性心脏病的优化管理和治疗策略 冠心病（CHD）的治疗只能在多中心合作研究的背景下才能实现。目前，PCGC 缺乏两种基本能力，阻碍了其定义CHD结果基因组基础的能力：（1） 从电子健康记录中提取相关的机器可读临床数据的强大机制 （2）一个强大的人工智能（AI）平台，能够戏弄 除了母体因素、表型、基因型、基因功能和临床表现之间的复杂相互作用外， 结果。在这里，我们通过组建内容专家团队， 利用现有基础设施，直接从参与PCGC的EHR中提取相关结果 中心，并通过设计最佳实践AI工具进行成果研究。我们的主要目标是提供愿景， 基础设施和专业知识，以协同授权CHD结果研究，促进知识交流， 培养下一代基因组科学家我们建议利用现有的数据基础设施来获取 电子健康记录（EHR）和其他临床变量的规模通过与其他研究网络合作 创建PCGC数据资源。利用这一资源，我们将创建和部署一个人工平台， 用于CHD结局研究的基于智能（AI）的预测器，目标是转化基因组发现 改善冠心病的管理和治疗策略。

PROJECT SUMMARY/ABSTRACT 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.