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

基本信息

批准号：
10599717
负责人：
Xiao Hu
金额：
$ 58.03万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
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/10599717
关键词：
Acute Adopted Algorithms Aneurysmal Subarachnoid Hemorrhages Angioplasty Appearance Area Blood Flow Velocity 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 Ensure 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 TimeLine Training Transcranial Doppler Ultrasonography Validation Vasodilator Agents base clinical practice constriction data streams diagnostic accuracy efficacy testing electronic data hemodynamics improved indexing machine learning algorithm novel prediction algorithm predictive modeling prevent prospective recurrent neural network relating to nervous system temporal measurement vector

Acute Adopted Algorithms Aneurysmal Subarachnoid Hemorrhages Angioplasty Appearance Area Blood Flow Velocity 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 Ensure 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 TimeLine Training Transcranial Doppler Ultrasonography Validation Vasodilator Agents base clinical practice constriction data streams diagnostic accuracy efficacy testing electronic data hemodynamics improved indexing machine learning algorithm novel prediction algorithm predictive modeling prevent prospective recurrent neural network relating to nervous system temporal measurement vector

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项目摘要

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.

项目摘要延迟的脑缺血（DCI）是动脉瘤后最具破坏性的并发症亚蛛网膜下腔出血（ASAH），发病率为30％。当前的实践依赖通过间歇性评估神经系统状况和每日脑血流速度（CBFV）经颅多普勒超声（TCD）指导医疗管理以防止DCI。只有之后医疗管理失败，是血管内治疗（EVT），包括内部血管扩张器输注和/或颅内血管成形术。这种反应性实践不能解释 DCI和可能会在症状或与正常血液动力学的严重偏差。该项目的目的是开发算法在开发的早期阶段预测DCI和相关目标。 DCI的准确预测将实现一种更加主动的策略，以防止和处理DCI的根本原因。以下三个目标将实现该项目的目标：1）开发特定于ASAH 颅内压（ICP）基于脉冲的脑动脉态指数； 2）发展和验证预测与近视后延迟脑缺血有关的靶标模型； 3）进行潜在的机构 - 开发模型的特定适应和验证。我们的DCI预测算法只需要当前临床实践中可用的数据，因此可以容易采用。如果得到验证，这些算法将使临床医生能够监视DCI的风险连续并主动提供适当的治疗。拟议的前瞻性研究算法实施和适应将为以后的临床试验做好准备，以测试算法信息干预措施。