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

项目摘要 在美国，有450万成年人患有肝病，而肝移植(LT)是治疗 那些患有肝硬变的人；移植中心负责确定救命器官的接受者。 被列入移植名单的患者存在差异：黑人患者在美国81%的移植中心比例偏低 等待名单上，62%的人代表不到西班牙裔患者。IT中心评估每个患者是否适合 移植，最终决定是挂牌移植还是拒绝。如果列出，则根据以下内容对患者进行优先排序 疾病严重，将要么接受肝脏，要么因各种原因被除名，如死亡。而当 之前的差异研究针对影响上市后结果的因素(例如，等待名单辍学、LT后 生存)，上游的重点是预先列出患者层面的障碍，结构性/制度性种族主义，以及 尽管人际种族主义对LT患者的公平性有很高的影响，但尚未得到很好的研究。LT上市 决策是可变的。目的采用临床措施，但健康的社会决定因素(SDOH， 例如，种族主义、社会经济地位)和主观性渗透到数据收集、临床观察和 心理社会评估。LT上市的数据驱动方法尚未得到描述。预测分析 (有监督的机器学习)可以被利用来加强客观性并最大限度地减少复杂性的偏差 决策。我的定性工作的初步数据是第一个全面概述潜力的 导致上市差异的途径，并显示移植中心提供者持谨慎乐观态度 基于机器学习的临床决策支持工具在LT评估中的应用。假设是适时的 访问汇总的、客观的数据可以改善提供商的决策和列表差异。使用 从公平的角度应用数据科学技术的多学科方法，施特劳斯博士将 利用她与约翰·霍普金斯医学中心专家的密切关系：有经验的移植 移植研究实验室团队，马龙医疗工程中心，公共卫生社会学院 流行病学家和伯曼生物伦理学研究所。项目的总体目标是提高大连市的公平性 使用数据驱动和基于团队的干预进行决策；首要培训目标是获得技能 在机器学习、健康公平干预和实施科学方面。目标1：发展和内部 验证基于机器学习的模型，以辅助LT上市决策。目标2：创建数据驱动的 以公平为中心的团队决策干预在LT评估中的应用。目标3：设计多中心试点 实施以数据为导向、以公平为重点的干预措施以进行LT评估。影响：通过以下方式 在这个项目中，施特劳斯博士将开发一种数据驱动的、以股票为重点的干预措施，以解决LT中的差异 正在挂牌。这项指导奖将把施特劳斯博士培养成一位由R01资助的独立内科科学家 具备机器学习、健康公平研究和实施科学方面的高级技能。

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.