PROJECT ABSTRACT
In 2018, heart allocation moved from using three statuses to six statuses, but the priority groups remain
undifferentiated and large, only coarsely reflecting each candidate’s risk of death on the waiting list. The new
system aimed to reduce exception requests and more accurately rank candidates; instead, exceptions, which
might be subjectively or inappropriately assigned, have risen to comprise 30% of heart transplants. The system
also incentivized dramatic changes in clinical practice: because intraortic balloon pumps yield higher priority for
transplant, balloon pump usage increased three-fold even though balloon pumps are associated with increased
risk of neurologic complications. Other disparities persist: sensitized candidates wait four times as long for
transplants but get no increased priority for hearts, in contrast to increased kidney priority for sensitized
candidates. Our study will first describe the landscape of disparities in heart transplant, diagnosing whether
transplants are equally available to candidates who are sensitized, of smaller or larger size, and of different
races and ethnicities. We will also determine whether exception statuses are justified by candidates’ individual
risks of waitlist death, and whether exceptions are being requested and granted in an equitable fashion. We
will use machine learning to build a MESH (Model for End State Heart disease) score that predicts death on
the waitlist for heart transplant candidates from national data. An individualized MESH score based on
hemodynamic criteria in the context of cardiac pathology, end organ function, cause of heart failure, and
eligibility for mechanical circulatory support therapies would better prioritize heart transplant candidates to
reduce waitlist deaths without distorting clinical practice. Implementing an analogous lung allocation score in
2005 reduced waitlist deaths from 500 to 300 per year, so this change is overdue in heart allocation. Finally,
we will design a composite allocation score for hearts. The Organ Procurement and Transplantation Network
has resolved to replace the 250 and 500 mile circles in current policy with continuous distribution by
implementing a composite allocation score. A composite allocation score is a weighted combination of medical
urgency (MESH), with distance between donor and candidate, and other priority considerations like blood type,
candidate and donor size, sensitization, and priority for prior living donors. However, eliminating geographic
boundaries in this way creates an enormous combinatorial design space with complex tradeoffs. We will use
simulation optimization to explore many possible choices for the numerical weights using clinically detailed
simulations, guided by a differential evolution algorithm. Our team is exceptionally well-suited to the task, with
dedicated quantitative scientists who have years of collaborative experience advancing transplantation
partnering with clinicians of a superior heart transplant program. The proposed research would correct an
arbitrary and coarse prioritization scheme, by establishing a validated heart allocation scoring system that
reduces waitlist deaths and increases equity in heart allocation.