Split Liver Transplantation: An Analytical Decision Support Model
This research study introduces a decision support model for split liver transplantation (SLT). SLT is a procedure that can save two lives with one donated liver, thereby increasing the total benefit derived from the limited number of donated livers available. SLT may also improve equity by giving transplant candidates who are physically smaller, including children, increased access to liver transplants. However, SLT is rarely used in the United States. To help quantify the benefits of increased SLT utilization and provide decision support tools, this research presents a deceased-donor liver allocation model that incorporates both efficiency and fairness objectives. We formulate the liver waitlists as a multiqueue fluid system, incorporating specifics of donor-recipient size matching and patients’ dynamically changing health conditions. Leveraging a novel decomposition result, the study finds the exact optimal matching procedure, enabling policy makers to benchmark the performance of different allocation policies against the theoretical optimal. Numerical results, utilizing data from the Organ Procurement and Transplantation Network, show that increased utilization of SLT can significantly reduce patient deaths, increase total quality-adjusted life years, and improve fairness among different patient groups.
Authors
- John P. Roberts (ORCID: https://orcid.org/0000-0003-3303-9406)
- Alan Scheller‐Wolf (ORCID: https://orcid.org/0000-0001-6871-2360)
- Emily R. Perito (ORCID: https://orcid.org/0000-0002-2911-9684)
- Yanhan Tang (ORCID: https://orcid.org/0000-0002-7372-9738)
- Sridhar Tayur (ORCID: https://orcid.org/0000-0002-8008-400X)
Institutions
- Southern Methodist University (US)
- University of California, San Francisco (US)
- Carnegie Mellon University (US)
Publication Details
- Journal
- Operations Research
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1287/opre.2022.0131
- Citations
- 1
- Primary Topic
- Liver Disease and Transplantation
- Type
- article
- Field-Weighted Citation Impact
- 1.32