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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：
10487446
负责人：
CLIFTON O BINGHAM
金额：
$ 21.28万
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/10487446
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 treatment response 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。