MGH Receives NIH Grant for Unique Value of Real-TIme Shear Stress to Enhance Coronary Disease Management.
Massachusetts General Hospital Receives a 2019 NIH Grant for $729,778 for Unique Value of Real-TIme Shear Stress to Enhance Coronary Disease Management. The principal investigator is Guillermo Tearney. The program began in 2018 and ends in 2020. Below is a summary of the proposed work.
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