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.