Abstract 4349985: Whole Patient Targeting Highly Predicts Future Stroke Events: A Risk Stratification Model for Timely Intervention in Senior Populations

Abstract

Background: Hypertension is a leading contributor to stroke related events, yet most health systems lack predictive infrastructure to identify at-risk individuals early enough for preventive action. In collaboration with Emory Healthcare’s informatics division, Guidehealth developed a risk stratification model to identify patients with hypertension most likely to experience adverse cerebrovascular events and benefit from targeted interventions. Objective: To evaluate the predictive performance and clinical utility of a novel risk stratification algorithm: (1) to identify hypertensive patients at high risk for a cerebrovascular event (2) to estimate likelihood of successful intervention based on clinical and social context. Methods: Guidehealth built machine learning models using longitudinal data from 197,967 Medicare-eligible hypertensive seniors to a feed-forward neural network with long short-term memory predict adverse cerebrovascular events. Time series were subsampled with a 24-month lookback and prediction interval over the following 6-12 months. Additive temporal encoding preserved chronicity and exposure. Features included comorbidities, medication adherence, labs/imaging, and utilization trends—capturing both static and time-varying variables from claims data. Outcomes were 6–12-month stroke admissions. Outputs prioritized outreach and modifiable drivers of risk. Results: Among flagged patients in the historical validation set, >98% a cerebrovascular event within the timeframe. Model specificity (98%) was prioritized over sensitivity (30%) due to the cost and resource allocation. A patient prioritization dashboard enabled targeted outreach and prospective monitoring. Conclusion: Whole Patient Targeting represents a powerful advance in stroke prevention and represents a shift toward anticipatory health. By identifying high-risk and high-impactability patients, the model offers a scalable method to reduce avoidable cerebrovascular events and enable more effective, person-centered care for aging populations.