Data Harmonization

数据协调

• 批准号: 10267752
• 负责人: Ricardo Henao Giraldo
• 金额: $ 66.56万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10267752
• 关键词: Address; Age; Alabama; Algorithms; American Heart Association; American Stroke Association; Assessment tool; Award; Big Data to Knowledge; Categories; Cause of Death; Cohort Studies; Complex Analysis; Data; Development; Ethnic Origin; Ethnic group; Etiology; Event; Funding; Geography; Guidelines; Hemorrhage; Individual; Longitudinal cohort study; Machine Learning; Methods; Modeling; National Heart, Lung, and Blood Institute; National Institute of Neurological Disorders and Stroke; Outcome; Patient Self-Report; Patients; Performance; Prevention Guidelines; Prevention strategy; Primary Prevention; Race; Research; Risk Assessment; Risk Factors; Stroke; Stroke prevention; Techniques; Time; United States National Institutes of Health; Universities; Update; Validation; base; cohort; computing resources; data exchange; data harmonization; database of Genotypes and Phenotypes; deep learning; design; experience; genetic information; offspring; precision medicine; predictive modeling; repository; risk prediction model; risk stratification; sex; stroke model; stroke risk; tool; web services

PROJECT SUMMARY Stroke is the fifth most prevalent cause of death in the U.S. afflicting nearly 800,000 per year. About three quarters of strokes are first events, underscoring the importance of primary prevention. Designing optimal preventive strategies requires identification of risk factors and estimation of the risk of stroke. The most recent American Heart Association (AHA)/American Stroke Association Guidelines for the Primary Prevention of Stroke conclude that “an ideal stroke risk assessment tool that is simple, is widely applicable and accepted, and takes into account the effects of multiple risk factors does not exist.” One of the most commonly recommended predictive models is the Framingham Stroke Profile, developed and updated more than 25 years ago. Newer models have been proposed (including the Self-Reported Stroke Risk Stratification tool from the REGARDS study) but have not been thoroughly validated. Consequently, the Primary Prevention of Stroke guidelines call for more research “to validate risk assessment tools across age, sex, and race/ethnic groups” and “to evaluate whether any of the more recently identified risk factors add to the predictive accuracy of existing scales”. We propose to address these gaps by aggregating and harmonizing existing patient-level data collected as part of longitudinal cohort studies supported by the NINDS and NHLBI. The data will be obtained through a partnership with the coordinating center for the REGARDS Study at the University of Alabama at Birmingham and by a request submitted to the NIH dbGap repository to obtain data from the Framingham Offspring, ARIC and MESA cohorts. At the same time, we will expand the advanced machine learning techniques developed as part of our currently funded NIH BD2K award to Duke. We will apply these models to the harmonized data to facilitate development and validation of prediction tool for primary strokes. These complex analyses will require advanced computational resources that will utilize the AHA's Precision Medicine Platform (PMP), built based on Amazon Web Services.

项目概要 中风是美国第五大最常见的死亡原因，每年有近 80 万人受到中风的影响。 四分之一的中风是首发事件，强调了设计最佳预防的重要性。 预防策略需要识别危险因素并估计最新的中风风险。 美国心脏协会 (AHA)/美国中风协会初级预防指南 中风的结论是“一种理想的中风风险评估工具，简单、广泛适用和接受， 并考虑到多种风险因素的影响，但最常见的风险因素之一并不存在。” 推荐的预测模型是 Framingham 中风概况，已开发和更新超过 25 个 几年前就提出了新的模型（包括自我报告的中风风险分层工具）。 REGARDS 研究）但尚未经过彻底验证，中风的一级预防。 指南呼吁进行更多研究“验证跨年龄、性别和种族/族裔群体的风险评估工具” 以及“评估最近发现的任何风险因素是否会增加预测的准确性 现有规模”。 我们建议通过汇总和协调现有的患者级数据来解决这些差距 NINDS 和 NHLBI 支持的纵向队列研究的一部分数据将通过 与阿拉巴马大学伯明翰分校 REGARDS 研究协调中心合作 并通过向 NIH dbGap 存储库请求从 Framingham Offspring、ARIC 获取提交的数据 与此同时，我们将扩展开发的先进机器学习技术。 作为我们目前资助的 NIH BD2K 奖的一部分，我们将把这些模型应用到统一数据中。 促进原发性中风预测工具的开发和验证。 需要先进的计算资源，利用 AHA 的精准医学平台 (PMP) 基于亚马逊网络服务。