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

点击翻译按钮获取中文摘要

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.