Comprehensive Cardiac Structure-Function Analysis in Heart Transplantation - Renewal
Northwestern University
Summary
Heart transplantation (HTx) is a well-established life-saving procedure but is associated with severe complications. Regular monitoring of heart transplant recipients is thus important for the early detection of these complications. Current standard clinical tests, however, rely on frequent invasive procedures including endomyocardial biopsies (EMB) and catheter angiography (Cath). In addition, these standard tests are limited by sampling error, the diffuse nature of HTx complications, and high health care utilization cost, estimated at \>$150,000 per year per patient in the US. To address these limitations, our group has developed a non-invasive multiparametric cardiac MRI, which can quantify abnormal changes in heart tissue and function. Our efforts during the initial period of this study (NIH funded 2014-2019) have focused on the two major complications of HTx: 1) acute cardiac rejection (ACR), the leading cause of death in the first year after heart transplant; and 2) cardiac allograft vasculopathy (CAV), the greatest risk factor for 5-year mortality beyond the first year after heart transplantation. For these major compilation, our previous cardiac MRI studies have identified new non-invasive cardiac MRI measures that can detect abnormalities of heart tissue and function. In addition, the data was able to show that heart donor and recipient mismatch (age, sex, height, weight, etc.) can cause changes in tissue and function of the transplanted heart.
Description
Aim 1: To develop dedicated multiparametric cardiac MRI protocols that account for wide range body sizes and patient physiology (e.g., heart rates, breathing patterns) of heart transplant recipients, critical for the wide age range in HTx from pediatric to adult. Second, to facilitate clinical translation and multi-site portability of the often time-consuming data analysis methodology, the development of artificial intelligence (AI) deep learning concepts to enable automated cardiac MRI analysis across large cohorts. The hypothesis to be tested will verify that automated AI analysis can detec…