A multivariate predictive model for long-term disability post subarachnoid hemorrhage in Caucasian and African American populations

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Aneurysmal subarachnoid hemorrhage (aSAH) strikes relatively young individuals and carries high rates of mortality and severe disability. While social, clinical, and genetic factors have each independently been shown to be associated with disability, there remains a large portion of unexplained variability as well as great disparities in outcome for African American patients as compared to Caucasian patients. Thus, there is a gap in knowledge relating to: 1) accurate prediction of those most at risk for long-term disability outcomes and 2) the relative contributions of these multivariate factors for the observed disparities in outcome seen for African Americans. These gaps currently present a critical barrier toward the goal of developing an individualized intervention to reduce disability and increase quality of life after aSAH. The objective of this current proposal is to lay the foundation for such an intervention by accurately identifying individuals most at risk and identifying the factors contributing to the racial disparities seen for these populations. Our central hypothesis is that multivariate models encompassing selected social, clinical, and genetic factors will provide a sensitive and specific prediction of 12-month disability outcomes for Caucasian and African American populations. Guided by our strong pilot data and leveraging the power of two existing databases, this hypothesis will be tested by two specific aims: 1) Using social, clinical, and genetic data, we propose to develop a predictive model for disability 12 months post aSAH in a Caucasian cohort; and 2) Using social, clinical, and genetic data, we propose to develop a predictive model for disability 12 months post aSAH in an African American cohort. After validation and cross-validation, the uniformity of the two models will be compared for insights into factors driving the disparities in outcome between these groups. This project is innovative for its multivariate predictive model that incorporates the collection and addition of genetic data and also for the racial diversity seen when comparing these two unique longitudinal aSAH datasets. This project is significant, as it will inform precisely targeted interventions aimed at reducing disability and disparity in outcomes post aSAH, which will allow a better quality of life for these patients.