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

动脉瘤性蛛网膜下腔出血（aSAH）发生于相对年轻的个体， 死亡率和严重残疾率。虽然社会、临床和遗传因素各自独立地被 尽管这些结果与残疾有关，但仍有很大一部分无法解释的变异性， 非裔美国人患者与白人患者的结局差异。因此， 在以下方面的知识：1）准确预测长期残疾后果风险最高的人，2） 这些多变量因素对观察到的非洲人结局差异的相对贡献 美国人这些差距目前是实现发展个性化医疗目标的关键障碍。 干预以减少aSAH后残疾并提高生活质量。本建议的目的是 通过准确识别风险最大的个人， 造成这些人群种族差异的因素。我们的核心假设是 包括选定的社会、临床和遗传因素的多变量模型将提供一个敏感的， 对白人和非裔美国人人群12个月残疾结局的具体预测。指导 我们强大的试点数据和利用两个现有数据库的力量，这一假设将由两个 具体目标：1）利用社会，临床和遗传数据，我们建议开发一个残疾预测模型 高加索人群aSAH后12个月; 2）使用社会、临床和遗传数据，我们建议 在非裔美国人队列中开发aSAH后12个月残疾的预测模型。验证后 和交叉验证，将比较两个模型的一致性，以了解驱动 这些群体之间的结果差异。该项目是创新的多元预测模型， 包括收集和添加遗传数据，以及在比较时看到的种族多样性， 这两个独特的纵向aSAH数据集。这个项目是重要的，因为它将告知准确的目标， 旨在减少aSAH后结果的残疾和差异的干预措施，这将使更好的质量 生命对于这些病人来说。

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.