Unique value of real-time shear stress to enhance coronary disease management

我们无法研究基本的病理生物学过程，这阻碍了CAD的管理 这会导致个体冠状动脉斑块进展、不稳定和不良临床事件。一位批评者 导致斑块行为的机制是局部内皮剪应力(ESS)，即血液的摩擦力 流经血管内皮细胞，这是由动脉详细的局部几何形状决定的。低气压的震源区 ESS驱动一系列促动脉粥样硬化、促炎症和促血栓形成过程；因此，它不是 令人惊讶的是，低ESS现在被发现是未来冠状动脉事件最有力的预测因子。 然而，目前计算活体局部ESS的方法需要独特的专业知识，而且非常耗时和 资源密集型，包括一个漫长的、离线的过程，从单独的 OCT和血管造影图像，以及耗时数小时的计算流体动力学(CFD)模拟。 这些限制阻碍了利用这一重要信息来改善患者的治疗。 我们的目标是利用ESS来指导心导管术患者的最佳管理。 一系列广泛的冠状动脉综合征。我们将通过开发和验证单个 导管/计算机控制台系统，将自动实时计算ESS(RT-ESS)。组件 包括一种新型多模光学相干断层扫描(OCT)导管，该导管通过 检测单壁碳纳米管(SWCNT)涂层的荧光应变敏感变化 在它的鞘内。使用与SWCNT荧光同时获得的OCT图像，我们将开发 用于为CFD自动创建解剖学上正确的3D动脉模型的算法。通过加速CFD 过程中，人体冠脉的详细ESS地图将在1-3分钟内计算出来，并显示在 解剖的OCT图像。RT-ESS技术将首先使用猪冠状动脉粥样硬化进行验证 模型，然后在接受经皮冠状动脉介入治疗(PCI)的患者中。在最终目标中，我们将 进行一项临床研究，以确定ESS与分数血流储备(FFR)的关系。这个 病理生物学/解剖RT-ESS数据和FFR的结合可能会改善个体的预后 冠状动脉病变，导致更知情的临床决策和更好的患者结果。

Management of CAD is hindered by our inability to investigate fundamental pathobiologic processes that lead to individual coronary plaque progression, destabilization, and adverse clinical events. A critical mechanism responsible for plaque behavior is local endothelial shear stress (ESS), the frictional force of blood flowing across the endothelium, which is governed by the artery’s detailed local geometry. Focal regions of low ESS drive a host of proatherogenic, proinflammatory, and prothrombotic processes; it is therefore not surprising that low ESS has now been found to be the most powerful predictor of future coronary events. However, current methods to compute local ESS in vivo require unique expertise and are extremely time- and resource-intensive, involving a lengthy, off-line procedure for reconstructing the 3D artery lumen from separate OCT and angiographic images and a computational fluid dynamics (CFD) simulation that takes many hours. These limitations prevent the use of this vital information to improve the treatment of patients. Our goal is to utilize ESS to guide optimal management during cardiac catheterization for patients with a broad array of coronary syndromes. We will accomplish this goal by developing and validating a single catheter/computer console system that will automatically compute ESS in real time (RT-ESS). Components include a novel, multimodality optical coherence tomography (OCT) catheter that senses its own shape by detecting strain-sensitive changes in fluorescence from single-walled carbon nanotubes (SWCNT) coated inside its sheath. Using OCT images acquired simultaneously with SWCNT fluorescence, we will develop algorithms to automatically create an anatomically-correct 3D artery model for CFD. By accelerating the CFD process, detailed ESS maps from human coronaries will be computed in 1-3 minutes and displayed with anatomic OCT images. The RT-ESS technology will be first validated using a swine coronary atherosclerosis model and then in patients undergoing percutaneous coronary intervention (PCI). In the final Aim, we will conduct a clinical study to determine the relationship between ESS and fractional flow reserve (FFR). The combination of pathobiologic/anatomic RT-ESS data and FFR will likely improve prognostication of individual coronary lesions, leading to more informed clinical decision-making and better patient outcomes.