[F32] Data-Driven Solutions for Heart Allocation Policy Challenges: the LVAD Dilemma and the Status Exception Crisis
Ente: National Heart Lung and Blood Institute
Scadenza: 2028-08-31
Importo max: $105,476
Paese: US
Descrizione
PROJECT SUMMARY
Heart transplants are a potentially life-saving treatment for hundreds of thousands of patients living with
advanced heart failure, but there are only enough donor hearts to perform a few thousand transplants annually.
Under federal law, donor hearts must be allocated in order of medical urgency based on objective medical criteria.
The current donor heart allocation system follows this mandate by ranking candidates based on six statuses,
reflecting the acuity of therapies they are receiving. However, there are two critical shortcomings that threaten
the fairness of donor heart allocation. First, durable left ventricular assist devices (LVADs) are a treatment that
allows select candidates to delay heart transplant until they stabilize clinically. In this setting, candidates with
durable LVADs are assigned low waitlist priority because they have low medical urgency; however, extended duration
of LVAD support is associated with complications that significantly reduce post-transplant survival. Solutions to
appropriately prioritize these candidates before developing critical complications are severely lacking. Second, status
exceptions, which are special requests to upgrade candidates' waitlist priority, now account for nearly 40% of all
heart transplant listings. I have previously shown that exceptions are granted to candidates with low medical
urgency and that the approval rate is near 100%. This proposal aims to address the critical and urgent need for
solutions to these significant problems highlighted by the heart transplant community. The central hypothesis of
this proposal is that applying methodologically rigorous techniques to novel sources of data, such as free-text
narratives written by transplant clinicians, will yield practical policy-based solutions. In Aim 1, I will construct a
mixed-effects Cox proportional hazards model incorporating restricted cubic splines to estimate the survival
benefit of heart transplantation as a function of durable LVAD support duration, identify the point at which the
survival benefit is maximal, and determine which candidates should receive upgraded priority. In Aim 2, I will
employ a hybrid manual and large language model-based thematic analysis of more than 32,000 clinical
narratives submitted with exception applications to identify distinct clinical phenotypes that correlate with medical
urgency, providing standardized criteria for review of exception requests. This work will be completed at Stanford
University under the mentorship of Dr. Kiran Khush (primary sponsor), Dr. William Parker (co-sponsor), and Dr.
Arden Morris (co-sponsor), with collaboration from Dr. Suzanne Tamang. The training plan emphasizes three
learning aims: qualitative data analysis and large language model utilization, clinical medical ethics, and research
dissemination to organ allocation stakeholders. Upon completion, this research will provide methodologically
rigorous, evidence-based solutions to two maximum-prior
Istituzione: STANFORD UNIVERSITY
PI: DANIEL JAECHUL AHN
Progetto: 1F32HL189471-01
Settori: National Heart Lung and Blood Institute
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