Harnessing Dynamic Cardiac Parameters to Optimize Donor Heart Utilization

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PROJECT SUMMARY Heart failure is an extremely prevalent and morbid condition projected to affect over 8 million people in the United States by 2030. Heart transplantation is considered the final life-saving treatment option for patients with advanced heart failure. However, there is a large discrepancy between the number of patients on the heart transplant waiting list and the number of transplants performed each year. This discrepancy is largely due to the high proportion of donor hearts that are turned down by transplant centers due to lack of standardized donor selection criteria. Our limited understanding of which donor cardiac parameters are associated with poor recipient outcomes hampers our ability to expand the donor pool. Currently we rely on traditional donor selection criteria such as normal echocardiograms, normal electrocardiograms, and low doses of vasoactive and inotropic medications. We know that many donor factors influence transplant recipient outcomes, yet there is limited understanding of the degree of risk that each donor factor represents. Thus, there is a critical need to better understand the landscape of cardiac dysfunction present in donor hearts and their relationship with long term outcomes in order to expand access to heart transplantation and promote transplant longevity. The central hypothesis is that traditional donor selection criteria will be inversely correlated with transplant recipient mortality and acute rejection and that machine learning applied to recipient data will better predict outcomes such as acute rejection. This study will (1) investigate donor vasoactive/inotropic medication use, left ventricular hypertrophy/remodeling, left ventricular ejection fraction, and left ventricular wall motion abnormalities and determine their relationship with donor use and transplant recipient outcomes; (2) determine whether donor electrocardiogram abnormalities correlate with donor echocardiogram abnormalities; and (3) apply a previously validated machine learning algorithm to transplant recipient electrocardiograms for assessment of acute graft rejection. The results of this project will challenge traditional donor selection criteria, allow for expansion of donor heart use, and will contribute to improving heart transplant longevity via novel use of machine learning algorithms. This will ultimately expand access to life-saving heart transplantation and improve transplant longevity. This project will be conducted at Stanford University, an internationally renowned research institution and a leading site for cutting edge heart transplant research. Stanford was the site of the first heart transplant in the United States and is the 4th largest heart transplant program in the country. The candidate will receive expert mentorship from esteemed faculty in the field of Advanced Heart Failure and Transplant Cardiology. To complement this mentoring, an individually tailored training plan will enable the candidate to develop content expertise in echocardiograms, electrocardiograms, machine learning; advance their biostatistical skills; and improve their scientific management and communication through coursework and career development activities.