Heart Transplantation - Renewal

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.

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 detect altered cardiac MRI metrics with improved efficiency and reduced inter-rater variability

Aim 2:

Comprehensive cardiac MRI measures will be evaluated for the identification of complications after heart transplantation (ACR, CAV) in children. The anticipated enrollment of n=80 (20 per year) pediatric HTx patients (\<21 years) in years 2-5 at Lurie Children's Hospital. Inclusion criteria include a clinically indicated routine cardiac MRI for HTx graft surveillance. The hypothesis to be tested is that cardiac MRI measures can inform pediatric donor selection by providing important new data on the impact of donor-recipient mismatch (e.g. age, sex, heart size, etc.) on changes in tissue and function of the transplanted heart.

Aim 3:

The slow progression of complications in heart transplant recipients requires a long-term (\>5-year) follow-up study to determine the diagnostic value of cardiac MRI for the detection of patient outcomes. Cardiac MRI data will be collected at long-term (\>5-year) follow-up in 80 HTx recipients who have already received their baseline cardiac MRI during the initial NIH-funded study period (2014-2019). will study the diagnostic value of cardiac MRI to improve the monitoring of heart transplant recipients for the major complications of acute cardiac rejection (ACR) and cardiac allograft vasculopathy (CAV). The anticipated follow-up enrollment of a total of 80 HTx patients during years 2-5 with a minimum of 5-year follow-up (20 HTx patients/year returning for HTx surveillance, baseline MRI scan was performed during the initial funding period. To clarify, our aim isn't to perfectly match donor-recipient but rather to study the clinical implications of mismatch and to help define the threshold for "too much" mismatch. In other words, today the human body may sometimes not accept a heart if the mismatch will be too great, this is sometimes hard to do and literature is scarce, especially in terms of functional rather than anatomic implications. The goal of the study could better inform these decisions.

Eligibility Criteria:
Inclusion Criteria:

At Northwestern University:

* At least 18 years of age
* Able to complete the MR safety screening form as required by SOC
* Able to comprehend and provide informed consent

Adult Heart Transplant patients scanned at CTI

-Baseline cardiac MRI prior to 4/2017

Pediatric controls scanned at Lurie

* All ages
* No known CHD
* Receiving clinical cardiac MRI: Some pediatric patients are scheduled to receive cardiac -MRI's to rule out congenital heart disease. In some instances, the outcome is normal - these patients would be considered healthy controls. Pediatric controls are getting MRI scans of heart only.

Pediatric heart transplant patients scanned at Lurie

* All ages
* Receiving baseline clinical cardiac MRI
* Past heart Tx

Exclusion Criteria:

* Abnormal kidney function (eGFR \< 30 mL/min/). Patients with a history of kidney problems (GFR \< 30 ml/min) or have had a kidney and/or liver transplant will be excluded from the study or may undergo the MRI exam without the use of a contrast agent, per standard MR exclusion criteria
* Contraindication to MRI i.e. device implants, metal hardware, etc as determined by staff technologists
* Adults unable to consent

Pediatric heart transplant patients scanned at Lurie

* Patients who have not received a heart transplant
* Patients who have not received a cardiac MRI at baseline
* Individuals not receiving the supplemental 10-minute scan, due to GA concerns, will be counted towards the desired study population.
* If a scan returns with an abnormality, they would no longer be considered control and would not be counted towards the desired study population. It is not until the exam is completed the data may be able to determine whether a patient will qualify as a control. Ordering providers routinely order MRI exams to rule out a certain abnormality. This is similar to ordering providers to request a brain MRI if a patient presents with certain symptoms, such as a headache, to rule out a brain tumor. In many cases those exams may return unremarkable.