Developing Machine Learning Models for Decision Support and Allocation Optimization in Heart Transplantation

开发用于心脏移植决策支持和分配优化的机器学习模型

• 批准号: 10735348
• 负责人: Arman Kilic
• 金额: $ 56.6万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-04 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735348
• 关键词: Acceleration; Address; Adoption; Adult; Artificial Intelligence; Clinical; Complex; Computer Analysis; Computer Simulation; Data; Data Set; Decision Making; Decision Support Model; Decision Support Systems; Development; Evaluation; FAIR principles; Face; Failure; Future; Goals; Heart; Heart Transplantation; Heart failure; Human; Individual; Judgment; Machine Learning; Modeling; Multicenter Trials; Organ; Outcome; Output; Patients; Performance; Policies; Policy Maker; Policy Making; Predictive Analytics; Preparation; Process; Provider; Reporting; Research; Resources; Risk; Specific qualifier value; Survival Rate; Technology Assessment; Time; Transplant Recipients; Transplantation; Visual; Waiting Lists; Work; clinical candidate; clinical decision-making; clinically relevant; data registry; experience; high risk; implementation science; implementation strategy; improved; improved outcome; innovation; machine learning method; machine learning model; mortality risk; novel; open data; personalized decision; post-transplant; predictive modeling; programs; prototype; risk prediction; statistics; stem; success; support tools; tool; transplant centers; transplant database; trend; usability; web-based tool

PROJECT SUMMARY/ABSTRACT The impact of heart transplantation (HTx) remains limited by donor shortages, with an estimated 250,000 adults who may benefit from transplant despite only 3,500 being performed each year in the US. Unfortunately, donor discard rates remain high at 70-80%, with substantial unexplained variability in donor evaluation and acceptance practices between centers. Recent data also demonstrate that higher risk recipients are being transplanted under the new 2018 allocation policy with worse post-transplant survival rates nationally. These trends collectively underscore current limitations in allocation policy and the ability for individual programs to assess donor quality and to pair suitable donors with appropriately selected recipients. The latter stems from a suboptimal process whereby clinicians have to make time-sensitive decisions relying solely upon experience and judgement without data-driven tools that can analyze numerous donor and recipient data and their complex interactions to provide rapid and accurate outcome projections. Existing risk models have failed to garner widespread utilization due to major limitations, including 1) narrow focus on only one of a set of relevant outcomes, 2) simplistic approach with only modest discriminatory capability (c-statistics <0.70), 3) failure to account for complex interactions between donor and recipient variables, and 4) use of only static, cross-sectional data. Our proposal seeks to advance the field by leveraging a novel, comprehensive dataset and machine learning (ML) to develop robust models that can maximize predictive performance for relevant outcomes and to better align a candidate's clinical trajectory and anticipated transplant outcome. These models will better account for complex interrelationships between donor and recipient variables, and will also account for dynamic changes in candidate and donor parameters. Optimized models will then be incorporated into a decision support system guided by key stakeholders. In addition, a previously developed artificial intelligence (AI) framework will be used to optimize heart allocation policy. We have these specific aims: 1) Establish the feasibility and usability of a stakeholder-guided, ML-derived decision support system for adult HTx; 2) Demonstrate the adaptability of a previously developed AI-based policy-optimization framework to heart allocation; and 3) Inform and evaluate the processes and outputs of Specific Aims 1 and 2 using stakeholder engagement and implementation science to refine and optimize working prototypes and promote the understanding, adoption, and use of data-driven decision support tools created for HTx. This work will optimize the allocation of scarce resources and ultimately improve outcomes of HTx.

项目摘要/摘要 心脏移植（HTX）的影响仍然受捐助者短缺的限制，估计有25万成人 尽管在美国每年只进行3500次进行3,500人，但他们可能会受益于移植。不幸的是，捐助者 丢弃率仍然很高，为70-80％，捐助者评估和接受度有很大的无法解释的变异性 中心之间的实践。最近的数据还表明，正在移植较高的风险收件人 根据新的2018年分配政策，在全国范围内移植后生存率较差。这些趋势 分配政策的当前限制以及单个计划评估的能力 捐助者质量并将合适的捐助者与适当选择的接受者配对。后者源于 临床医生必须仅依靠经验做出时间敏感的决定，临床医生必须做出时间敏感的决定 没有数据驱动工具的判断，可以分析众多捐助者和收件人数据及其复杂 相互作用以提供快速准确的结果预测。现有风险模型未能获得 由于主要限制，包括1）仅关注一组相关的一组 结果，2）仅具有适度歧视能力的简单方法（C统计算法<0.70），3）未能 捐助者和受体变量之间的复杂相互作用，以及4）仅使用静态，横截面 数据。我们的建议旨在通过利用小说，全面的数据集和机器来推进该领域 学习（ML）开发可强大的模型，以最大程度地提高相关结果的预测性能和 更好地使候选人的临床轨迹和预期的移植结果保持一致。这些模型将更好 对于捐助者和收件人变量之间的复杂相互关系，还将解释动态变化 在候选人和捐助者参数中。然后，优化模型将被纳入决策支持系统 在主要利益相关者的指导下。此外，将使用先前开发的人工智能（AI）框架 优化心脏分配政策。我们有这些具体目标：1）确定一个的可行性和可用性 利益相关者指导成人HTX的ML衍生的决策支持系统； 2）证明A的适应性 以前将基于AI的政策优化框架开发为心脏分配； 3）告知和评估 特定目标1和2的过程和输出使用利益相关者的参与和实施科学来 完善和优化工作原型，并促进对数据驱动的理解，采用和使用 为HTX创建的决策支持工具。这项工作将优化稀缺资源的分配，并最终 改善HTX的结果。