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

点击翻译按钮获取中文摘要

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 award will develop Dr. Strauss into an R01-funded, independent physician-scientist with advanced skills in machine learning, health equity research, and implementation science.