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

> **NIH NIH R01** · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · 2020 · $280,374

## Abstract

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.

## Key facts

- **NIH application ID:** 9982447
- **Project number:** 5R01NR017407-03
- **Recipient organization:** UNIVERSITY OF TENNESSEE HEALTH SCI CTR
- **Principal Investigator:** Ansley Grimes Stanfill
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $280,374
- **Award type:** 5
- **Project period:** 2018-08-08 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9982447

## Citation

> US National Institutes of Health, RePORTER application 9982447, A multivariate predictive model for long-term disability post subarachnoid hemorrhage in Caucasian and African American populations (5R01NR017407-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9982447. Licensed CC0.

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