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Precision Medicine Data Integration Core

精准医疗数据集成核心

基本信息

批准号：
10281311
负责人：
CLIFTON O BINGHAM
金额：
$ 20.44万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-09 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10281311
关键词：
Address Area Big Data Biologic Characteristic COVID-19 Characteristics Clinical Clinical Research Collaborations Complex Consent Data Data Aggregation Data Analyses Data Collection Data Science Data Security Data Sources Databases Development Disease Documentation Ensure Environment Event Faculty Fostering Foundations Healthcare Heterogeneity Image Imaging Device Infrastructure Institution Institutional Review Boards Kinetics Laboratory Procedures Learning Lupus Malignant Neoplasms Measurement Medical Device Medicine Methods Myositis Outcome Measure Participant Pathogenesis Pathway interactions Patient Care Patient Outcomes Assessments Patients Pattern Phenotype Precision Medicine Initiative Protocols documentation Psoriatic Arthritis Quality of life Registries Regulation Research Research Ethics Research Personnel Resources Rheumatism Rheumatoid Arthritis Rheumatology Risk Scleroderma Secure Sjogren&apos s Syndrome Speed Standardization System Techniques Technology Telemedicine Therapeutic Time Universities Vasculitis Work analytical tool base biobank clinically relevant cohort common rule data acquisition data harmonization data integration data modeling data pipeline data streams diagnostic platform digital disorder subtype electronic consent experience immune-related adverse events improved insight multiple data sources novel patient subsets phenotypic data point of care precision medicine programs quantitative imaging recruit research study response risk stratification screening tool wearable device

项目摘要

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.

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。