DATA-DRIVEN MODELS TO PREDICT DELAYED CEREBRAL ISCHEMIA AFTER SUBARACHNOID HEMORRHAGE

颅内动脉瘤（IA）的特征是局部扩张和变薄， 血管，虽然他们只影响6%的人口，从他们的出血帐户 约25%的脑血管死亡。颅内动脉瘤（IA）破裂导致以下原因之一： 最致命的出血性中风，蛛网膜下腔出血-SAH。尽管 SAH管理的改善，死亡率和发病率仍然很高，主要是由于 迟发性缺血并发症虽然高达40%有症状，但由于其严重的 由于我们无法确定谁会发生痉挛，所有患者都可能发生痉挛。 广泛的监测协议，需要巨大的资源和额外的风险， 监测和治疗。 该提案旨在开发预测分析，整合定量血管造影， 非侵入性成像和临床数据，以改善患者的预后， 通过提供真实的时间患者特异性指导来治疗蛛网膜下腔出血。我们的中央 假设血管造影参数成像（API）血流动力学生物标记物与 血管痉挛和脑自动调节受损，两者都与不良 迟发性脑缺血（DCI）。API提供了一组图像-生物标志物图谱 可以与患者特定的临床信息相结合， 由于DCI。该提案的目标是开发、标准化和验证一种诊断方法， 使用基于图像的生物标志物和患者特征来预测患者特异性 发展DCI的风险，以及功能和认知结果。 我们的应用是重要的，因为目前没有可靠的方法来预测DCI早期 可靠的预测有助于指导治疗和资源 配置中为了实现这一目标，我们提出了两个目标。在第一个目标中，我们将扩展先前的工作 使用机器学习框架来预测哪些患者的发展风险最低 总督察在目标二中，我们将开发工具，将预测扩展到功能和认知 成果。如果成功的话，这将是第一个机器学习应用程序之一， 集成的预测工具，允许临床医生在真实的时间修改治疗计划，以减少 患者风险和资源利用。

Intracranial Aneurysm (IA) are characterized by a localized dilation and thinning of the blood vessel, and although they only affect 6% of the population, bleeding from them accounts for about 25% of cerebrovascular deaths. Rupture of intracranial aneurysms (IAs) causes one of the most lethal types of hemorrhagic stroke, subarachnoid hemorrhage-SAH. Despite improvements in SAH management, mortality and morbidity rates remain high, largely due to delayed ischemic complications. Although symptomatic in up to 40%, because of its severe consequences and because we cannot identify who will develop spasm, all patients are subject to extensive monitoring protocols, entailing enormous resources and additional risk for monitoring and treatment. This proposal seeks to develop predictive analytics, integrating quantitative angiography, non-invasive imaging, and clinical data, to improve outcomes for patients suffering subarachnoid hemorrhage by providing real time patient-specific guidance. Our central hypothesis is that angiographic parametric imaging (API) hemodynamic biomarkers correlate with vasospasm and impaired cerebral autoregulation, both of which are associated with poor outcomes in delayed cerebral ischemia (DCI). API provides a set of maps of image-biomarkers that may be combined with patient-specific clinical information to robustly predict poor outcomes due to DCI. The proposal’s objective is to develop, standardize, and validate a diagnostic pipeline that uses image-based biomarkers and patient characteristics to predict patient-specific risk of developing DCI, as well as functional and cognitive outcomes. Our application is significant since there is currently no reliable way to predict DCI early in a patient’s course, and reliable predictions could help to guide therapy and resource allocation. To achieve this, we propose two aims. In the first aim, we will expand on prior work using a machine learning framework to predict which patients are at lowest risk of developing DCI. In aim two we will develop tools to extend predictions to functional and cognitive outcomes. If successful, this will be one of the first machine learning applications to produce an integrated prediction tool that allows clinicians to modify treatment plans in real time to reduce patient risk and resource utilization.