A Data Fusion Method for Bedside Monitoring of Lumped Cerebral Arterial Radii

脑血管痉挛仍然是脑血管痉挛后发病率和死亡率的主要原因， 蛛网膜下腔出血（aSAH）。及时预测其发生以进行治疗干预， 在改善aSAH后的结果方面至关重要，但使用现有技术仍不令人满意。 本项目的总体目标是开发和验证一种新的数据融合算法，用于预测 aSAH后的血管痉挛，仅需要床边测量动脉血压（ABP）， 颅内压（ICP）和脑血流速度（CBFV）。 本项目的具体目标是：1）开发血管痉挛报警生成算法 基于从数据融合估计的集中的近端（n）和远端（r2）脑动脉半径 2）将数据融合方法与现有的基于经颅多普勒（TCD）的 血管痉挛诊断标准; 3）比较数据融合方法与模型无关方法， 血管痉挛预测 在数据融合过程中，采用约束非线性卡尔曼滤波器估计集总 近端（n）和远端（r2）脑动脉半径，其是颅内动脉的两个内部状态变量， 压力动态模型据推测，血管痉挛的发展将显示出一致的趋势， 估计的N和/或R2，并且该趋势可通过统计趋势检测算法检测。的 检测到这种趋势将发出即将发生血管痉挛的警告。 据估计，仅在美国，每年aSAH患者的数量约为30，000人，其中高达 70%的患者可发生血管造影性血管痉挛，其余患者可能发生脑小动脉血管痉挛。 例由于血管痉挛引起的脑缺血如果不及时治疗，可能会导致毁灭性的后果。 改善aSAH预后的一个有希望的方法是在血管造影证据出现之前就检测到它， 进行干预。如果得到验证，所提出的数据融合血管痉挛评估方法可以实现这一点 目标是以低成本和与当前临床实践兼容的方式，获得广泛的 临床接受新方法。

Cerebral vasospasm remains the leading cause of morbidity and mortality after aneurysmal subarachnoid hemorrhage (aSAH). Predicting its occurrence in a timely manner for therapy intervention is critically important in improving outcome after aSAH but remains unsatisfactory using existing techniques. The overall goal of this project is to develop and validate a novel data fusion algorithm for predicing vasospasm after aSAH that only requires bedside measurements of arterial blood pressure (ABP), intracranial pressure (ICP) and cerebral blood flow velocity (CBFV). The specific objectives of this project are 1) To develop a vasospasm warning generation algorithm based on the lumped proximal (n) and distal (r2) cerebral arterial radii estimated from the data fusion process; 2) To compare the data fusion approach with the existing Transcranial Doppler (TCD)-based vasospasm diagnostic criteria; 3) To compare the data fusion approach with model-independent methods for vasospasm prediction. A constrained nonlinear Kalman Filter is applied in the data fusion process to estimate the lumped proximal (n) and distal (r2) cerebral arterial radii that are two internal state variables of an intracranial pressure dynamic model. It is hypothesized that development of vasospasm will show a consistent trend in the estimated n and/or r2 and that this trend is detectable by a statistical trend detection algorithm. The detection of such a trend will then fire a warning of impending vasospasm. It was estimated that annual number of aSAH patients is about 30,000 in US alone, among which up to 70% can develop angiographic vasospasm with possible vasospasm in small cerebral arteries in remaining cases. Cerebral ischemia due to vasospasm not treated in a timely fashion can lead to devastating outcome. A promising way to improve outcome after aSAH is to detect it even before angiographic evidence and act with interventions. If validated, the proposed data fusion vasospasm assessment method could achieve this goal in a low cost and in a compatible way with the current clinical practice, a desirable feature to gain wide clinical acceptance of a new approach.