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

学习用新型连续脑动脉状态指数预测迟发性脑缺血

• 批准号: 10406378
• 负责人: Xiao Hu
• 金额: $ 62.06万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10406378
• 关键词: Acute; Adopted; Algorithms; Aneurysmal Subarachnoid Hemorrhages; Angioplasty; Appearance; Area; Blood Flow Velocity; Cephalic; Cerebral Ischemia; Cerebral perfusion pressure; Cerebrovascular Circulation; Cerebrum; Characteristics; Chronic; Clinical; Clinical Decision Support Systems; Clinical Trials; Complication; Data; Data Analyses; Data Reporting; Development; Diagnosis; Dilatation - action; Distal; Electronic Health Record; Evaluation; Event; Future; Goals; Hydrocephalus; Incidence; Individual; Infusion procedures; Injury; Institution; Intervention; Intracranial Pressure; Learning; Machine Learning; Maps; Medical; Modeling; Monitor; Morphology; Nature; Neurologic; Neurological status; Patient Monitoring; Patient-Focused Outcomes; Patients; Pattern; Pattern Recognition; Performance; Pharmaceutical Preparations; Phase; Physiologic Monitoring; Physiological; Procedures; Process; Prospective Studies; Pulse Pressure; Recurrence; Reproducibility; Research; Risk; Shapes; Signal Transduction; Source; Symptoms; System; Techniques; Testing; Time; Training; Transcranial Doppler Ultrasonography; Validation; Vasodilator Agents; clinical practice; constriction; data streams; diagnostic accuracy; efficacy testing; electronic health data; hemodynamics; improved; indexing; machine learning algorithm; neural; novel; prediction algorithm; predictive modeling; prevent; prospective; recurrent neural network; temporal measurement; timeline; vector

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.

项目概要 迟发性脑缺血（DCI）是继动脉瘤后最具破坏性的并发症 蛛网膜下腔出血（aSAH）的发生率为30%。目前的实践依赖于 间歇性评估神经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。仅在之后 医疗管理失败，是否进行血管内治疗（EVT），包括动脉内血管扩张剂输注 和/或开始颅内血管成形术。这种反应性做法并没有考虑到早期的预测因素 DCI 可能会错过出现 DCI 症状或症状之前的早期 DCI 发展的最佳 EVT 窗口 严重偏离正常血流动力学。该项目的目标是开发算法 在 DCI 和相关目标发展的早期阶段进行预测。 DCI 的准确预测 将能够采取更积极主动的策略来预防和治疗 DCI 的根本原因。 为了实现该项目的目标，将追求以下三个目标： 1) 开发特定于 aSAH 的 基于脉搏的颅内压（ICP）脑动脉状态指数； 2) 开发并验证预测 aSAH后迟发性脑缺血相关靶点模型； 3）开展前瞻性机构—— 对已开发模型的具体调整和验证。 我们的 DCI 预测算法只需要当前临床实践中可用的数据，因此它们可以 很容易被采用。如果经过验证，这些算法将使临床医生能够监测 DCI 风险 持续并主动提供适当的治疗。拟议的前瞻性研究 算法的实施和适应将为未来的临床试验做好准备，以测试其功效 基于算法的干预。