# Developing Machine Learning Models for Decision Support and Allocation Optimization in Heart Transplantation

> **NIH NIH R01** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2024 · $642,433

## Abstract

PROJECT SUMMARY/ABSTRACT
The impact of heart transplantation (HTx) remains limited by donor shortages, with an estimated 250,000 adults
who may benefit from transplant despite only 3,500 being performed each year in the US. Unfortunately, donor
discard rates remain high at 70-80%, with substantial unexplained variability in donor evaluation and acceptance
practices between centers. Recent data also demonstrate that higher risk recipients are being transplanted
under the new 2018 allocation policy with worse post-transplant survival rates nationally. These trends
collectively underscore current limitations in allocation policy and the ability for individual programs to assess
donor quality and to pair suitable donors with appropriately selected recipients. The latter stems from a
suboptimal process whereby clinicians have to make time-sensitive decisions relying solely upon experience
and judgement without data-driven tools that can analyze numerous donor and recipient data and their complex
interactions to provide rapid and accurate outcome projections. Existing risk models have failed to garner
widespread utilization due to major limitations, including 1) narrow focus on only one of a set of relevant
outcomes, 2) simplistic approach with only modest discriminatory capability (c-statistics <0.70), 3) failure to
account for complex interactions between donor and recipient variables, and 4) use of only static, cross-sectional
data. Our proposal seeks to advance the field by leveraging a novel, comprehensive dataset and machine
learning (ML) to develop robust models that can maximize predictive performance for relevant outcomes and to
better align a candidate's clinical trajectory and anticipated transplant outcome. These models will better account
for complex interrelationships between donor and recipient variables, and will also account for dynamic changes
in candidate and donor parameters. Optimized models will then be incorporated into a decision support system
guided by key stakeholders. In addition, a previously developed artificial intelligence (AI) framework will be used
to optimize heart allocation policy. We have these specific aims: 1) Establish the feasibility and usability of a
stakeholder-guided, ML-derived decision support system for adult HTx; 2) Demonstrate the adaptability of a
previously developed AI-based policy-optimization framework to heart allocation; and 3) Inform and evaluate the
processes and outputs of Specific Aims 1 and 2 using stakeholder engagement and implementation science to
refine and optimize working prototypes and promote the understanding, adoption, and use of data-driven
decision support tools created for HTx. This work will optimize the allocation of scarce resources and ultimately
improve outcomes of HTx.

## Key facts

- **NIH application ID:** 10919849
- **Project number:** 5R01HL162882-02
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** Arman Kilic
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $642,433
- **Award type:** 5
- **Project period:** 2023-09-04 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10919849

## Citation

> US National Institutes of Health, RePORTER application 10919849, Developing Machine Learning Models for Decision Support and Allocation Optimization in Heart Transplantation (5R01HL162882-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10919849. Licensed CC0.

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