Learning to Predict Delayed Cerebral Ischemia with Novel Continuous Cerebral Arterial State Index

项目摘要 迟发性脑缺血（DCI）是脑梗死后最具破坏性的并发症， 蛛网膜下腔出血（aSAH），发病率为30%。目前的做法依赖于 间歇性评估神经系统状态和每日脑血流速度（CBFV）， 经颅多普勒超声（TCD）指导医疗管理，以预防DCI。后才 医疗管理失败，是血管内治疗（EVT），包括动脉内血管扩张剂输注 和/或开始颅内血管成形术。这种反应性的做法并不能解释早期的预测因素， DCI，并且可能在症状出现之前的DCI发展早期错过最佳EVT窗口， 严重偏离正常血流动力学。这个项目的目标是开发算法， 在发展的早期阶段预测DCI和相关目标。DCI的准确预测 将使一个更积极的战略，以预防和治疗DCI的根本原因。 为实现本项目的目标，将实现以下三个目标：1）制定特定的SAH 颅内压（ICP）基于脉搏的脑动脉状态指数; 2）开发并验证预测性 aSAH后迟发性脑缺血相关靶点模型; 3）进行前瞻性研究- 具体调整和验证所开发的模型。 我们的DCI预测算法仅需要当前临床实践中可用的数据，因此它们可以 很容易被采纳。如果得到验证，这些算法将使临床医生能够监测DCI的风险 积极主动地进行适当的治疗。拟议的前瞻性研究 算法的实现和适应将为未来的临床试验做好准备，以测试 基于算法的干预

Project Summary Delayed cerebral ischemia (DCI) is the most devastating complication after aneurysmal subarachnoid hemorrhage (aSAH) and has an incidence rate of 30%. Current practice relies on intermittent assessment of neurological status and daily cerebral blood flow velocity (CBFV) by Transcranial Doppler ultrasound (TCD) to guide medical management to prevent DCI. Only after medical management fails, is endovascular treatment (EVT) including intraarterial vasodilator infusion and/or intracranial angioplasty initiated. This reactive practice does not account for early predictors of DCI and may miss the optimal EVT window at an early stage of DCI development before symptoms or severe deviations from normal hemodynamics. The goal of this project is to develop algorithms to predict DCI and related targets at an early stage in their development. An accurate prediction of DCI will enable a more proactive strategy to prevent and treat the underlying cause of DCI. The following three aims will be pursued towards the goal of the project: 1) Develop aSAH-specific intracranial pressure (ICP) pulse-based cerebral arterial state index; 2) Develop and validate predictive models of targets related to delayed cerebral ischemia after aSAH; 3) Conduct a prospective institution- specific adaption and validation of the developed models. Our DCI predictive algorithms only need data available in current clinical practice hence they can be readily adopted. If validated, these algorithms will enable clinicians to monitor risk of DCI continuously and to proactively deliver appropriate treatment. The proposed prospective study of algorithm implementation and adaptation will well prepare future clinical trials to test the efficacy of algorithm-informed interventions.