Data Harmonization

项目总结 中风是美国第五大最常见的死亡原因，每年有近80万人受到影响。大约三个人 四分之一的中风是第一事件，强调了初级预防的重要性。最优设计 预防策略需要确定危险因素和估计中风的风险。最新的 美国心脏协会(AHA)/美国中风协会一级预防指南 中风的结论是“一种简单、广泛适用和被接受的理想中风风险评估工具， 并考虑到多重风险因素的影响并不存在。最常见的一种 推荐的预测模型是Framingham Stroke Profile，开发和更新了超过25个 几年前。已经提出了较新的模型(包括来自 有关研究)，但还没有得到彻底的验证。因此，卒中的首要预防 指南呼吁进行更多的研究，“以验证不同年龄、性别和种族/民族的风险评估工具”。 评估最近发现的风险因素是否增加了预测的准确性 现有比例尺“。 我们建议通过聚合和协调收集的现有患者级别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。这些数据将通过 与阿拉巴马大学伯明翰分校的CORETS研究协调中心合作 并通过提交给NIH DBGaP储存库的请求从ARIC的Framingham子代获得数据 和梅萨族人。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于协调数据 促进初级卒中预测工具的开发和验证。这些复杂的分析将 需要先进的计算资源，这些资源将利用AHA的精密医学平台(PMP)， 基于亚马逊网络服务。

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.