Development of Machine Learning Composite Measures for Graft Outcome Selection in Pediatric Liver Transplantation

开发用于小儿肝移植移植结果选择的机器学习综合措施

• 批准号: 10484107
• 负责人: George V Mazariegos
• 金额: $ 24.94万
• 依托单位: HEALTHTECH SOLUTIONS, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2022-11-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10484107
• 关键词: Abdomen; Adoption; Adult; Algorithms; Artificial Intelligence; Awareness; Cessation of life; Child; Childhood; Clinical; Clinical assessments; Communication; Complex; Computer software; Country; Data; Data Science; Data Set; Decision Making; Development; Donor person; Effectiveness; Foundations; Future; Geography; Goals; Graft Survival; Health Insurance Portability and Accountability Act; Hour; Human; Individual; Industry; Journals; Kidney Transplantation; Link; Liver; Living Donor Liver Transplantation; Living Donors; Logistics; Machine Learning; Manuscripts; Measures; Medical center; Methods; Modeling; Morbidity - disease rate; Operative Surgical Procedures; Organ; Organ Donations; Organ Donor; Organ Transplantation; Outcome; Outcome Measure; Patient-Focused Outcomes; Patients; Performance; Phase; Policies; Positioning Attribute; Problem Solving; Productivity; Provider; Publications; Quality of life; Recovery; Reporting; Risk; Small Business Innovation Research Grant; Software Tools; Telephone; Testing; Time; Training; Transplant Recipients; Transplantation; Universities; Validation; Variant; Vulnerable Populations; Waiting Lists; Work; base; clinical decision support; cohesion; data registry; deep learning; design; exhaust; experience; hazard; health information technology; improved; liver transplantation; machine learning algorithm; machine learning method; mobile application; mortality; multi-site trial; novel; organ allocation; post-transplant; predictive modeling; prevent; programs; simulation; success; supervised learning; support tools; survival outcome; survival prediction; tool; transplant centers

ABSTRACT Outcomes in pediatric liver transplantation (pLT) are not limited by the donated organ supply. Kids are dying waiting for organs even when these deaths are completely preventable through proper organ selection. Instead of dying, children can live a full and active lifetime with a properly selected liver graft for transplant. Critical to achieving zero waitlist mortality and long-term transplant benefit is the capacity to intervene in a timely manner with a suitable organ and graft type. Decisions to proceed with pLT are complicated, ultimately based on the alignment of transplant team experience, clinical assessment, and organ availability. In an era of organ shortages, the use of technical variant (TV) grafts, including split liver transplantation and living donor liver transplant, has the potential to expand graft choice and enable timelier surgical intervention. Most transplant programs that have prior experience with TV grafts have low patient mortality and excellent transplant outcomes. However, some transplant programs that have limited prior experience with TV grafts have reported many poor outcomes for patients receiving TV transplants. Despite improvements in overall outcomes, national registry data have confirmed significant variation among transplant centers in waitlist mortality, TV graft use, and post-transplant outcomes. Integrally linked to this variation is the intricacy of transplant decision making. Collectively, donor and graft acceptance, prioritization of candidates, and allocation policies depict a complex scenario. More than 100 variables can be considered in a single donor-recipient ‘‘best matching’’ decision, with a risk of subjectivity and mismatch because of human limitations that should not be underestimated. Recognizing these limitations, artificial intelligence classifiers, including machine learning and deep learning, have been recognized for their potential to support or confirm decision making within the field of transplantation. Still, overall data-driven support for optimal graft selection and dissemination of graft decision support is lacking. Opportunities for, and the impact of, discovery are high. This project will result in a composite decision support software tool that uses machine learning to predict and model the best survival for the patient using pre-transplant mortality, post- transplant outcomes, and prior center experience. The decision support tool can be established to supplement current graft selection practices in pLT. We anticipate that modeling based on composite measures will demonstrate equivalent outcomes in recipients of TV grafts. We will develop an algorithm for optimal pediatric graft-type selection that will be commercialized for use through the Starzl Network for Excellence in Pediatric Transplantation and after further multi-center validation it will be available for all pediatric transplant programs. We will accomplish our objective through the following three aims. One, determine the optimal feature space for predictive variables for patient and pLT graft survival. Two, develop survival prediction models, “PSELECT,” for remaining on the waitlist or receiving various graft types. Three, demonstrate the simulated technical feasibility to eliminate the waitlist mortality based on the PSELECT performance on previously held-out data.

抽象的 儿童肝移植 (pLT) 的结果不受捐赠器官供应的限制。 即使通过适当的器官选择完全可以预防这些死亡，也要等待器官。 临终时，儿童可以通过正确选择的肝移植物过上充实而积极的一生。 实现零候补死亡率和长期移植效益是及时干预的能力 进行 pLT 的决定很复杂，最终取决于 在器官短缺的时代，移植团队的经验、临床评估和器官可用性相一致。 使用技术变异（TV）移植，包括劈裂肝移植和活体肝移植， 扩大移植选择并实现更及时的手术干预的潜力。 先前的电视移植经验具有较低的患者死亡率和良好的移植效果。 先前电视移植经验有限的移植计划报告了许多不良结果 尽管总体结果有所改善，但国家登记数据有所改善。 证实移植中心之间在候补死亡率、电视移植物使用和移植后方面存在显着差异 与这种差异密切相关的是移植决策的复杂性。 贪污接受、候选人的优先顺序和分配政策描绘了一个复杂的场景。 在单个供体-受体“最佳匹配”决策中可以考虑 100 个变量，存在主观性风险 由于人类的局限性而导致的不匹配，不应低估这些局限性， 人工智能分类器，包括机器学习和深度学习，因其 仍然有可能支持或确认移植领域的决策，总体而言是数据驱动的。 缺乏对最佳移植物选择的支持和传播移植物决策支持的机会。 该项目将产生一个使用复合决策支持软件工具的结果。 机器学习利用移植前死亡率、移植后死亡率来预测和建模患者的最佳生存率 可以建立决策支持工具来补充移植结果和先前的中心经验。 我们预计基于综合测量的建模将是当前 pLT 中的移植物选择实践。 在电视移植受者中证明相同的结果我们将开发一种最佳儿科算法。 移植类型选择将通过 Starzl 儿科卓越网络进行商业化使用 移植并经过进一步的多中心验证后，将可用于所有儿科移植项目。 我们将通过以下三个目标来实现我们的目标：第一，确定最佳特征空间。 患者和 pLT 移植物存活的预测变量 第二，开发存活预测模型“PSELECT”。 留在候补名单上或接受各种移植类型三，演示模拟技术可行性。 根据先前保留的数据的 PSELECT 表现来消除候补名单死亡率。