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)发病年龄较小，发病率较高。 死亡率和严重伤残率。虽然社会、临床和遗传因素各自独立地 被证明与残疾有关，仍然有很大一部分原因不明的变异性以及巨大的 与高加索人患者相比，非裔美国人患者的预后差异。因此，就有了差距 关于以下方面的知识：1)准确预测长期残疾后果的最高风险人群；2) 这些多变量因素对观察到的非洲结果差异的相对贡献 美国人。这些差距目前对开发个性化的 ASAH后减少残疾和提高生活质量的干预措施。目前这项提议的目标是 通过准确识别最有风险的个人和确定 造成这些人口出现种族差异的因素。我们的中心假设是 包含选定的社会、临床和遗传因素的多变量模型将提供敏感和 对高加索人和非裔美国人人口12个月残疾结局的具体预测。指导原则 我们强大的试点数据，并利用两个现有数据库的力量，这一假设将通过两个 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；和2)使用社会、临床和基因数据，我们建议 在一个非裔美国人队列中开发一种预测ASAH后12个月残疾的模型。验证后 和交叉验证，将比较两个模型的一致性，以深入了解推动 这些组之间的结果存在差异。该项目的创新之处在于其多变量预测模型 结合了遗传数据的收集和添加，以及在比较时看到的种族多样性 这两个唯一的纵向ASAH数据集。这个项目意义重大，因为它将准确地告知目标 旨在减少ASAH后残疾和预后差异的干预措施，这将使质量更好 这些病人的生活。

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.