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

数据驱动模型预测蛛网膜下腔出血后迟发性脑缺血

• 批准号: 10288178
• 负责人: Jason Davies
• 金额: $ 25.16万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10288178
• 关键词: Affect; Angiography; Biological Markers; Blinded; Blood Vessels; Blood flow; Brain hemorrhage; Brain region; Caring; Cerebral Aneurysm; Cerebral Ischemia; Cerebrum; Cessation of life; Characteristics; Clinical; Clinical Data; Cognitive; Data; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Disease; Event; Goals; Hemorrhage; Heterogeneity; Homeostasis; Hospitalization; Image; Impaired cognition; Impairment; Infarction; Intracranial Aneurysm; Investigation; Link; Machine Learning; Maps; Methods; Modeling; Monitor; Morbidity - disease rate; Outcome; Outcome Measure; Patient risk; Patient-Focused Outcomes; Patients; Perfusion; Population; Predictive Analytics; Probability; Prophylactic treatment; Protocols documentation; Research; Resource Allocation; Resources; Risk; Rupture; Ruptured Aneurysm; Source; Spasm; Specificity; Standardization; Structure; Subarachnoid Hemorrhage; Testing; Thinness; Time; Tissues; Vasospasm; Work; cerebrovascular; experience; functional outcomes; hemodynamics; imaging biomarker; improved outcome; indexing; mortality; neurovascular; non-invasive imaging; parametric imaging; predictive modeling; prospective; quantitative imaging; tool; treatment planning

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.

颅内动脉瘤（IA）的特征是局部词典和变薄 血管，尽管它们仅影响6％的人口，但出血的说法 大约25％的脑血管死亡。颅内动脉瘤（IAS）的破裂导致一种 出血性中风的最致命类型，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，这主要是由于 延迟缺血并发症。尽管有症状的高达40％，但由于其严重 后果，因为我们无法确定谁会发展痉挛，所以所有患者都是受试者 为了广泛的监视协议，带来巨大的资源和额外的风险 监测和治疗。 该建议旨在开发预测分析，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 通过提供实时特定于患者的指导，亚蛛网膜下腔出血。我们的中心 假设是血管造影参数成像（API）血液动力学生物标志物相关 血管痉挛和脑自动调节受损，这两者都与较差有关 延迟脑缺血（DCI）的结果。 API提供了一组图像生物标志物 可以将其与特定于患者的临床信息结合在一起，以稳健地预测结果不佳 由于DCI。该提案的目标是开发，标准化和验证诊断 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的管道 发展DCI以及功能和认知结果的风险。 我们的应用很重要，因为目前尚无可靠的方法来预测DCI 在患者的课程中，可靠的预测可能有助于指导治疗和资源 分配。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 DCI。在目标两个方面，我们将开发将预测扩展到功能和认知的工具 结果。如果成功，这将是第一个生产的机器学习应用程序之一 综合预测工具，允许临床医生实时修改治疗计划以减少 患者风险和资源利用。