Acute coronary syndromes (ACS) are a result of sudden luminal thrombosis. These pathologic events are a significant clinical problem, not only because of their frequency, but also due to the diagnostic challenge in stratifying risk for coronary lesions (i.e., stable versus rupture-prone) and identifying lesions that will undergo rapid progression and increased vulnerability (i.e., lesion prognostication). Although invasive imaging modalities can characterize plaque composition and phenotype, the use of imaging to risk stratify coronary lesions that will precipitate an ACS event has proven less accurate. Thus, plaque risk stratification strategies should move beyond image-based morphologic markers and focus on identifying the local environmental factor(s) that contribute to rapid coronary artery disease (CAD) progression, heightened vulnerability, and rupture risk. The overall goal of this R01 proposal, therefore, is to examine the predictive value of mechanical metrics for lesion risk stratification and prognostication in prospective studies evaluating the natural history of coronary atherosclerosis. Our central hypothesis is that mechanical indices will advance the identification of high-risk coronary lesions and promote the ability to predict plaque rupture. To realize this goal, we will approach this research through two hypothesis-driven Specific Aims: (i) examine the predictive value of plaque material stiffness in stratifying risk for coronary lesion rupture and (ii) evaluate the prognostic value of deformation- induced wall stress for identifying rapidly progressing CAD and increased plaque vulnerability. We propose to develop and validate computational frameworks to extract the heterogeneous material properties of coronary arteries and predict the 3D patient-specific coronary plaque mechanical environment through forward finite element analysis. Subsequently, these frameworks will be clinically translated to establish their clinical value. Successful completion of the proposed research will advance understanding of the prognostic value of mechanics in the natural history of CAD and advance patient management and treatment strategies towards minimizing adverse events associated with ACS.
Reducing the mortality rates associated with coronary artery disease will require the development of accurate strategies to risk stratify coronary lesions that will precipitate an acute coronary event. The proposed research will establish and validate computational frameworks to characterize the in vivo plaque stiffness and mechanical stress field, and evaluate the predictive accuracy of these frameworks to identify high-risk coronary lesions in the clinical setting. The outcomes of this research will promote the prognostication of coronary lesions to guide patient management and treatment strategies towards minimizing major adverse cardiac events.