Clinical and epidemiological

Leads: Jodi Edwards & Peter Liu

Determining who in our population are at risk for brain-heart diseases is an important first step in managing and preventing these conditions. This can be done by taking advantage of population-based datasets that are already available, such as the Concussion database and Pediatric Emergency Research Consortium (PERC) database, as these contain long-term follow-up data. Through the use of modeling and artificial intelligence networks, the clinical phenotypes of those at high risk for brain-heart vulnerabilities will be established. In addition, mental health, and social determinants such as education and economic stability on those at risk will be assessed. This will allow for the creation of novel algorithms to inform risk prediction which will be further developed as new data is added. The goal is to create, and continuously refine, risk prediction tools and personalized care strategies across different life-stages for use at the bedside, by patients and other knowledge users.

Deliverables: Creating a big data collaboration platform featuring the latest linked epidemiological data on brain-hear comorbidities. Creating and validating clinical risk prediction tools for heath system incorporation or commercialization. Identifying novel at risk subgroups and community cohorts to jointly develop knowledge tools to prevent disease and disability.