# Addressing racial and ethnic disparities in access to the liver transplant waiting list: a data science-focused and team-based approach

> **NIH NIH K08** · JOHNS HOPKINS UNIVERSITY · 2024 · $172,548

## Abstract

Project Summary
In the US, 4.5 million adults have liver disease, and liver transplantation (LT) is the only curative treatment for
those with cirrhosis; transplant centers are charged with determining recipients for a life-saving organ.
Disparities exist for patients listed for LT: Black patients are under-represented on 81% of US transplant center
waitlists, and 62% under-represent Hispanic patients. LT centers assess each patient’s appropriateness for
transplant, culminating in a decision to list for transplant or decline. If listed, patients are prioritized based on
disease severity and will either receive a liver or be de-listed for a variety of reasons, such as death. While
prior disparities research has targeted factors affecting post-listing outcomes (e.g., waitlist dropout, post-LT
survival), an upstream focus on pre-listing patient-level barriers, structural/institutional racism, and
interpersonal racism has not been well studied despite having high impact on equity for LT patients. LT listing
decision-making is variable. Objective clinical measures are utilized, but social determinants of health (SDOH,
e.g., racism, socioeconomic position) and subjectivity permeate data gathering, clinical observations, and
psychosocial assessments. A data-driven approach to LT listing has yet to be described. Predictive analytics
(supervised machine learning) can be harnessed to strengthen objectivity and minimize bias of complex
decision-making. Preliminary data from my qualitative work are the first to comprehensively outline potential
pathways resulting in the listing disparities and reveal that transplant center providers are cautiously optimistic
for machine learning-based clinical decision support tools in LT evaluation. The hypothesis is that timely
access to summarized, objective data can improve provider decision-making and listing disparities. Using a
multi-disciplinary approach to apply data science techniques from an equity perspective, Dr. Strauss will
leverage her strong relationships with experts from Johns Hopkins Medical Center: experienced transplant
team, transplant research lab, Malone Center for Engineering in Healthcare, School of Public Health social
epidemiologists, and the Berman Institute of Bioethics. The overarching project goal is to improve equity in LT
decision-making using a data-driven and team-based intervention; the overarching training goal is to gain skills
in machine learning, health equity interventions, and implementation science. AIM 1: Develop and internally
validate a machine learning-based model to assist LT listing decision-making. AIM 2: Create a data-driven,
equity-focused intervention for team decision-making in LT evaluation. AIM 3: Design a multicenter pilot
implementation trial of a data-driven, equity-focused intervention for LT evaluation. Impact: Through this
project, Dr. Strauss will develop a data-driven and equity-focused intervention that will address disparities in LT
listing. This mentored aw...

## Key facts

- **NIH application ID:** 10851815
- **Project number:** 5K08DK133638-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Alexandra Teresa Strauss
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $172,548
- **Award type:** 5
- **Project period:** 2022-08-15 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10851815

## Citation

> US National Institutes of Health, RePORTER application 10851815, Addressing racial and ethnic disparities in access to the liver transplant waiting list: a data science-focused and team-based approach (5K08DK133638-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10851815. Licensed CC0.

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