Case Western Reserve University Receives NIH Grant for Computer Assisted Coronary Artery Stent Interventions
Case Western Reserve University Receives a 2019 NIH Grant for $732,934 for Computer Assisted Coronary Artery Stent Interventions. The principal investigator is David Wilson. The program began in 2018 and ends in 2022. Below is a summary of the proposed work.
Computer assisted coronary artery stent interventions Summary When treating highly calcified coronary artery lesions with stents, interventional cardiologists, with limited information, make stressful treatment decisions, which can lead to inadequate stent deployment and poor outcomes, or even rare calamitous events. When calcification is present, a cardiologist must choose to use a normal sized angioplasty balloon; a larger angioplasty balloon with increasingly high, prolonged pressures to fracture the calcification; a scoring or cutting balloon; any one of a number of atherectomy devices; and/or stent post-dilation balloon pressures up to 30 atm. The cardiologist must choose stent diameter and length, taking into account distal artery size, vessel taper, and extent (arc length and thickness) of calcification. Some potential negative consequences are that a stent can under deploy and/or have malapposed struts; a balloon can rupture; a vessel can dissect; or an atherectomy device can perforate the wall. Intravascular optical coherence tomography (IVOCT) evaluations of stent deployment show that large eccentric calcifications often lead to under deployment with malapposition of struts and small vessel dissections. To address these challenges, specific aims are: 1) Develop methods for imaging/quantifying calcifications in pre-stent images. 2) Perform ex vivo experiments to obtain detailed biomechanical data and demonstrate key issues in stent deployment. 3) Develop well-validated, lesion specific FEM models. 4) Use in silico experiments and physical measurements to elucidate issues in stent deployment and establish guidelines for treatment. With success, the project will lead to future decision-support software for live-time treatment planning from IVOCT or other imaging modalities. The project team will build on expertise in interventions, quantitative image analysis of IVOCT, and finite element modeling.