Precision Medicine Data Integration Core

核心B精准医疗数据集成 项目总结 风湿性疾病在表型、事件动力学和轨迹上是不同的，并且 对治疗的反应。定义相对同质的疾病亚组可以促进识别 有相同的潜在病理生理机制、并发症风险和 对不同的治疗策略的反应-从而为开发 精准医学接近风湿病。作为利用多个 亚组患者的临床和生物学特征可能会改善我们的预测 能力，关键是利用允许聚合和协调的工具和技术 多数据流数据。约翰·霍普金斯医学公司已经建立了一个大数据捕获系统， 聚合和协调。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，包括护理点更深的表型数据， 来自诊断系统的细粒度结果和来自医疗保健之外的测量 环境。在这项提议中，我们寻求将这些工具和技术引入我们的风湿病 狼疮、血管炎、类风湿性关节炎、银屑病、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发生 风湿病的研究。精准医学数据集成的具体目标 核心是：1)促进和加快研究性学习的实施，同时确保严格 研究道德和数据安全实践；以及2)将数据收集方法转变为利用 制度性的大数据，提高效率，实现远程学习进度。核心由以下人员领导 研究项目建设、研究伦理和数据安全实践以及数据方面的专家 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.