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.

项目摘要 在美国，450万成年人患有肝病，肝移植（LT）是唯一的治愈性治疗方法。 肝硬化患者;移植中心负责决定救命器官的接受者。 列出的LT患者存在差异：黑人患者在81%的美国移植中心代表性不足 等待名单，62%的西班牙裔患者代表不足。LT中心评估每位患者是否适合 移植，最终决定列出移植或拒绝。如果列出，则根据以下因素对患者进行优先排序 疾病的严重程度，并将接受肝脏或因各种原因（如死亡）而被除名。而 先前的差异研究已经将影响上市后结果的因素作为目标（例如，候补名单辍学，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 人际间的种族歧视虽然对LT患者的公平性有很大的影响，但尚未得到很好的研究。LT列表 决策是可变的。利用客观的临床措施，但健康的社会决定因素（SDOH， 例如，在一个实施例中，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， 心理社会评估。尚未描述以数据为驱动的长期列名方法。预测分析 （监督机器学习）可以用来加强客观性，最大限度地减少复杂的偏见。 决策的从我的定性工作的初步数据是第一次全面概述潜力 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 用于LT评估中基于机器学习的临床决策支持工具。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出差异。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牢固关系：经验丰富的移植 团队，移植研究实验室，马龙医疗保健工程中心，公共卫生学院社会 流行病学家和伯曼生物伦理学研究所。该项目的总体目标是提高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 在机器学习，健康公平干预和实施科学。目标1：开发和内部 验证基于机器学习的模型，以协助LT上市决策。目标2：创建一个数据驱动的， 以公平为中心的干预，在LT评估团队决策。目标3：设计多中心试点 在长期评估中实施数据驱动、注重公平的干预试验。影响：通过这个 Strauss博士将开发一个数据驱动和以公平为中心的干预措施，以解决LT中的差异。 在上市这个指导奖将发展施特劳斯博士成为一个R 01资助的，独立的医生，科学家与 在机器学习、健康公平研究和实施科学方面的高级技能。