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

解决肝移植等候名单中的种族和民族差异：以数据科学为中心、基于团队的方法

• 批准号: 10681485
• 负责人: Alexandra Teresa Strauss
• 金额: $ 17.25万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10681485
• 关键词: Address; Adherence; Adult; Affect; Award; Awareness; Bioethics; Bioethics Consultants; Cessation of life; Characteristics; Charge; Cirrhosis; Clinical; Complex; Data; Data Display; Data Science; Data Scientist; Decision Making; Decision Support Systems; Delphi Technique; Disparity; Documentation; Dropout; Engineering; Epidemiologist; Equity; Ethnic Origin; Evaluation; Evidence based intervention; Feedback; Focus Groups; Funding; Future; Goals; Health Disparities Research; Healthcare; Hepatology; Hispanic; Informatics; Institutional Racism; Intervention; Learning; Life; Liver; Liver diseases; Machine Learning; Measures; Medical; Medical center; Mentors; Methods; Modeling; Molds; Organ; Outcome; Pathway interactions; Patients; Physicians; Predictive Analytics; Process; Process Measure; Promoting Action on Research Implementation in Health Services framework; Protocols documentation; Provider; Psychosocial Assessment and Care; Public Health Schools; Qualitative Methods; Qualitative Research; Race; Research; Scientist; Severity of illness; Socioeconomic Status; Supervision; System; Techniques; Testing; Time; Training; Transplant Recipients; Transplant Surgeon; Transplantation; Vulnerable Populations; Waiting Lists; Work; access disparities; black patient; clinical decision support; cohort; comorbidity; curative treatments; demographics; design; ethnic disparity; experience; health disparity; health equity; implementation science; implementation trial; improved; insight; interdisciplinary approach; liver transplantation; machine learning method; machine learning model; mathematical model; mortality; multilevel analysis; patient-level barriers; predictive modeling; primary outcome; psychosocial; racial disparity; racism; research clinical testing; satisfaction; secondary outcome; skills; social; social health determinants; supervised learning; support tools; tool development; transplant centers

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.

项目概要 在美国，450 万成年人患有肝病，肝移植 (LT) 是治疗肝病的唯一方法 肝硬化患者；移植中心负责确定挽救生命的器官的接受者。 接受 LT 治疗的患者存在差异：美国移植中心 81% 的患者中黑人患者的比例不足 候补名单和 62% 的西班牙裔患者代表性不足，LT 中心评估每位患者的适合性。 移植，最终决定是否列出移植或拒绝如果列出，则根据患者的优先顺序进行排序。 疾病严重程度，要么接受肝脏，要么因死亡等各种原因被除名。 先前的差异研究针对影响上市后结果的因素（例如，候补名单退出、LT后 生存），上游重点关注预先列出的患者层面的障碍、结构性/制度性种族主义，以及 尽管人际种族主义对 LT 患者的公平性有很大影响，但尚未得到充分研究。 决策的制定是可变的，但采用了健康的社会决定因素（SDOH、 例如，种族主义、社会地位）和主观性渗透到数据收集、临床观察和 LT 列表的数据驱动方法尚未描述。 （监督机器学习）可以用来增强客观性并最大限度地减少复杂性偏差 我的定性工作中的初步数据首先全面概述了潜力。 由此产生的上市差异的途径，并表明移植中心提供商持谨慎乐观的态度 LT 评估中基于机器学习的临床决策支持工具的假设是及时的。 访问汇总的客观数据可以改善提供商的决策和列表差异。 Strauss 博士将采用多学科方法从公平的角度应用数据科学技术 利用她与约翰·霍普金斯医疗中心专家的牢固关系：经验丰富的移植 公共卫生学院马龙医疗工程中心移植研究实验室团队 流行病学家和伯曼生物伦理学研究所的总体目标是提高 LT 的公平性。 使用数据驱动和基于团队的干预进行决策；总体培训目标是获得技能； 目标 1：开发和内部科学。 验证基于机器学习的模型以协助 LT 上市决策 AIM 2：创建数据驱动的、 目标 3：设计多中心试点 实施数据驱动、以公平为中心的 LT 评估干预试验 影响：通过此。 在该项目中，施特劳斯博士将开发一种数据驱动且注重公平的干预措施，以解决 LT 方面的差异 该指导奖项将把 Strauss 博士培养成为一名由 R01 资助的独立医师科学家。 机器学习、健康公平研究和实施科学方面的先进技能。