Virtual Intervention of Intracranial Aneurysms

颅内动脉瘤的虚拟干预

基本信息

批准号：
9026656
负责人：
HUI MENG
金额：
$ 33.84万
依托单位：
STATE UNIVERSITY OF NEW YORK AT BUFFALO
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-04-01 至 2020-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9026656
关键词：
Accounting Adopted Affect Aftercare Algorithms American Aneurysm Area Blood Vessels Blood flow Characteristics Classification Clinical Complex Computational algorithm Computer Simulation Data Development Devices Discipline Engineering Failure Fill-It Future Geometry Goals Grant Healed Health Image Implant In Vitro Institutes Intervention Intracranial Aneurysm Link Liquid substance Location Logistic Regressions Measures Methods Modality Modeling Modification Nature Neck Neurosurgeon Operative Surgical Procedures Outcome Output Parents Patient-Focused Outcomes Patients Performance Platinum Postoperative Period Procedures Receiver Operating Characteristics Recurrence Research Personnel Residual state Retreatment Risk Rupture Statistical Data Interpretation Statistical Models Stents Techniques Testing Thrombosis Thrombus Time Translating Treatment Failure Treatment outcome Velocimetries Virtual Tool base cohort combat computerized tools cranium effective therapy experience follow-up healing hemodynamics implantable device implantation improved individual patient innovation minimally invasive novel outcome prediction particle post intervention power analysis predictive modeling prevent prototype reconstruction simulation success tool treatment planning treatment strategy virtual

项目摘要

DESCRIPTION (provided by applicant): Endovascular intervention is the predominant mode of for treating intracranial aneurysms (IAs). As a minimally invasive alternative to open-skull surgery, it obliterates an aneurysm by either filling it with platinum coils to decrease inflow and induce aneurysmal thrombosis, or diverting blood flow away using stent-like flow diverters (FDs) to induce gradual aneurysmal occlusion and parent vessel reconstruction. Despite its immense success, 30% of coiled IAs experience recanalization (recurrence), while 10% of FD-treated IAs fail to occlude. Patients experiencing such negative outcomes are subjected to increased risks for IA rupture and complications from treatment. This grant aims at developing a method to predict treatment outcome a priori. Our central hypothesis is that, with other factors, postprocedural hemodynamics predicts endovascular treatment outcome. This proposal aims to both develop clinically-practical computational tools to simulate endovascular treatment strategies and test the above hypothesis by creating predictive models that utilize hemodynamics from computational fluid dynamics (CFD) simulations on cases treated in silico. In Aim 1, we will develop and test rapid simulation tools for coil and FD implantation. Our methods are based on novel ball-winding (coil deployment) and ball-sweeping (FD deployment) algorithms. These methods improve upon existing ones by mimicking clinical deployment strategies with superior computational efficiency. To test if our modeling techniques recapitulate the effects of actual device deployment, we will compare CFD results from treated IAs in silico against hemodynamics experimentally measured by particle image velocimetry in treated patient- specific IA phantoms. In Aim 2, we will test the hypothesis that postprocedural hemodynamics, with other clinical factors, predicts patient angiographic outcome. To this end we will apply virtual intervention retrospectively to 700 treated IA cases at our institute, model post-treatment hemodynamics using CFD, and develop multivariate statistical models for treatment outcome based on patient data. We will use an innovative two-tiered statistical approach to extract models for treatment outcome prediction: discriminant function analysis to pre-screen a large number of candidate variables, followed by multivariate logistic regression for creation of parsimonious predictive models. In Aim 3, we will independently test the models prospectively on a new cohort of 300 treated IAs to determine if the models can correctly predict treatment outcome at 12 months. Successful completion of this project will establish-for the first time-a computational tool to predict IA treatment outcome a priori, thereby enabling neurosurgeons to assess different treatment strategies prior to device deployment. When implemented in the procedure room, this new ability will allow for optimization of treatment for individual patients and development of new strategies for those cases with higher failure rates. This project brings together experienced investigators from multiple disciplines and provides an unprecedented opportunity to translate engineering and computational advancements into clinical usage.

描述（由申请人提供）：血管内介入治疗是治疗颅内动脉瘤（IA）的主要方式，作为开颅手术的微创替代方案，它通过用铂弹簧圈填充动脉瘤以减少血流来消除动脉瘤。诱导动脉瘤血栓形成，或使用支架样分流器 (FD) 转移血流以诱导逐渐动脉瘤闭塞和载瘤血管重建，尽管取得了巨大成功，但 30% 的卷绕 IAs 会出现再通（复发），而 10% 的 FD- 会发生血管再通。 IAs 未能闭塞的患者接受治疗，以增加 IA 破裂和治疗并发症的风险。我们的中心假设是，与其他因素一起，术后血流动力学可以预测血管内治疗结果。该提案旨在开发临床实用的计算工具来模拟血管内治疗策略，并通过创建利用预测模型来测试上述假设。在计算机模拟中，我们将开发和测试用于线圈和 FD 植入的快速模拟工具。（线圈部署）和扫球（FD 部署）算法通过模仿具有卓越计算效率的临床部署策略来改进现有算法。为了测试我们的建模技术是否重现实际设备部署的效果，我们将比较 CFD 结果。在目标 2 中，我们将测试手术后血流动力学与其他临床因素的假设。为此，我们将回顾性地对我们研究所的 700 例 IA 病例进行虚拟干预，使用 CFD 建立治疗后血流动力学模型，并根据患者数据开发治疗结果的多变量统计模型。 - 分层统计方法提取治疗结果预测模型：判别函数分析预先筛选大量候选变量，然后使用多元逻辑回归创建简约预测模型。在目标 3 中，我们将独立进行。在 300 名治疗 IAs 的新队列中前瞻性地测试模型，以确定模型是否能够正确预测 12 个月时的治疗结果，该项目的成功完成将首次建立一种计算工具来先验预测 IA 治疗结果，当在手术室实施时，神经外科医生能够评估不同的治疗策略，从而优化个体患者的治疗，并为失败率较高的病例制定新策略。该项目汇集了经验丰富的研究人员。来自多个学科并提供了将工程和计算进步转化为临床应用的前所未有的机会。