Precision Medicine Data Integration Core

核心B精准医学数据集成 项目摘要 风湿性疾病在表型、事件动力学和轨迹方面是异质性的， 对治疗的反应。定义相对同质的疾病亚组可以促进识别 共享潜在病理生理机制、并发症风险和 对不同治疗策略的反应-从而为开发 风湿性疾病的精准医疗。作为利用多个 亚组患者的临床和生物学特征可能会提高我们的预测 能力，关键是要利用工具和技术，使聚合和协调 多个数据流。约翰霍普金斯医学院已经建立了一个大数据采集系统， 汇总和协调。这个工具被称为精准医学分析平台， 捕获高质量的纵向数据，包括在护理点的更深的表型数据， 来自诊断系统和医疗保健外部测量的精细结果 环境在本提案中，我们寻求将这些工具和技术应用于我们的流变学 狼疮，血管炎，风湿性关节炎，银屑病关节炎，硬皮病， 肌炎、干燥综合征、莱姆病和癌症免疫相关不良事件加速 风湿性疾病的研究。精准医学数据集成的具体目标 核心是：1）促进和加速研究的实施，同时确保严格的 研究伦理和数据安全实践; 2）转变数据收集方法， 机构“大数据”，提高效率，使远程学习进步。核心由 研究项目建设、研究伦理和数据安全实践以及数据 捕获和协调方法。

Core B Precision Medicine Data Integration PROJECT SUMMARY Rheumatic diseases are heterogeneous in phenotypes, kinetics of events and trajectory, and response to therapy. Defining relatively homogenous disease subgroups can foster identification of patients who share underlying pathophysiologic mechanisms, risks of complications and response to distinct therapeutic strategies – thereby laying a critical foundation for developing precision medicine approaches to the rheumatic diseases. As approaches that utilize multiple clinical and biologic characteristics to subgroup patients will likely improve our predictive capability, it is critical to harness tools and technologies that allow aggregation and harmonization of multiple streams data. Johns Hopkins Medicine has built a system for big data capture, aggregation and harmonization. This tool, known as the Precision Medicine Analytics Platform, captures high quality longitudinal data including deeper phenotypic data at the point of care, granular results from diagnostic systems and measurements from outside the health care environment. In this proposal, we seek to bring these tools and technologies to our rheumatology subspecialty centers in Lupus, Vasculitis, Rheumatoid Arthritis, Psoriatic Arthritis, Scleroderma, Myositis, Sjogren’s syndrome, Lyme, and Cancer Immune-Related Adverse Events to accelerate research in the rheumatic diseases. The Specific Aims of the Precision Medicine Data Integration Core are to: 1) facilitate and accelerate research study implementation, while ensuring stringent research ethics and data security practices; and 2) transform data collection methods to harness institutional “big data,” improve efficiency and enable remote study progress. The Core is led by experts in research program building, research ethics and data security practices, and data capture and harmonization methods.