# Using ethics, epidemiology and high-quality data to optimize the allocation of livers for transplantation

> **NIH NIH R01** · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · 2022 · $512,808

## Abstract

7. Project Summary/Abstract
The demand for livers suitable for a liver transplant (LT) in the US outnumbers the supply. In 2017, although
7,500 patients received a LT, more than 2,500 were removed from the waitlist after dying or becoming “too sick
to transplant.” These numbers don't account for the fact that 5 out of 6 patients with end-stage liver disease
(ESLD) meeting LT criteria are never waitlisted. Consequently, patients inevitably die on a waiting list, or die
never having been waitlisted. An optimal transplant system should allocate organs by realizing three values: 1)
equality—fair access to transplant for all; 2) priority to the worst off, and 3) utility—maximizing the `expected
net amount of overall good' for the population of patients. By using the Model for End Stage Liver Disease
(MELD) score to prioritize patients, the current system focuses predominantly on urgency, favoring the
currently sickest as the `worst off.' This minimizes waitlist mortality. However, this does not consider fair access
(equality) for different patient subgroups (i.e., traditionally disadvantaged groups), or utility (e.g., maximizing
survival or net benefit of LT). As a result, many LTs are allocated to patients with limited life expectancy after
LT, while others on the waitlist with equally strong ethical claims to LT die. Improving LT allocation requires
new data and an innovative paradigm that seeks to optimize use of a scarce resource that can extend life by
>15 years. This requires more than simply a revised Model for End-Stage Liver Disease (MELD) score (the
score used for waitlist prioritization). First, to estimate survival without a LT, robust longitudinal data are
needed that extend beyond waitlist registry data, given that median waitlist time is <180 days and >50%
patients receive a LT within one year of listing. Second, long-term post-LT risk models must be developed, to
account for factors that are highly discriminatory for long-term survival (e.g., age, acute vs chronic kidney
failure). Third, allocation policies for patients with hepatocellular carcinoma (HCC) must consider the relative
long-term benefit of LT vs. other HCC treatments given the unintended consequences of prioritizing HCC
patients over those with decompensated cirrhosis. This work will be fundamental to the future design of an
improved allocation system that drives major increases in survival for patients with ESLD. We will leverage
powerful and detailed data from the Veterans Health Administration, the largest US provider of liver care, and
United Network for Organ Sharing (UNOS), to address these aims: 1) to develop time-updated, long-term pre-
LT survival models accounting for acute and chronic kidney disease; 2) to develop a time-updated, long-term
pre-LT survival model using tumor, laboratory data, and clinical variables for patients with HCC; 3) to develop a
long-term post-LT risk model incorporating demographic, clinical, and laboratory variables; 4) to build allocati...

## Key facts

- **NIH application ID:** 10356830
- **Project number:** 5R01DK120561-05
- **Recipient organization:** UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
- **Principal Investigator:** David Seth Goldberg
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $512,808
- **Award type:** 5
- **Project period:** 2019-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10356830

## Citation

> US National Institutes of Health, RePORTER application 10356830, Using ethics, epidemiology and high-quality data to optimize the allocation of livers for transplantation (5R01DK120561-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10356830. Licensed CC0.

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