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

项目总结/摘要 心脏移植（HTx）的影响仍然受到供体短缺的限制，估计有25万成年人 尽管在美国每年只有3,500例移植手术，不幸的是，捐赠者 丢弃率仍然高达70- 80%，在供体评估和接受方面存在大量无法解释的变化 中心之间的实践。最近的数据也表明，高风险的受体正在被移植， 根据新的2018年分配政策，全国移植后存活率更差。这些趋势 共同强调了当前分配政策的局限性和个别项目评估的能力， 捐助者质量和配对合适的捐助者与适当选择的受体。后者源于A 次优过程，临床医生必须仅依靠经验做出时间敏感的决策 没有数据驱动的工具，可以分析大量的捐助者和受援者的数据， 互动，以提供快速和准确的结果预测。现有的风险模型未能获得 广泛使用由于主要的局限性，包括1）狭隘的重点，只有一套相关的 结果，2）简单的方法，只有适度的区分能力（c-统计<0.70），3）未能 考虑到捐赠者和接受者变量之间的复杂相互作用，以及4）仅使用静态，横截面 数据我们的提案旨在通过利用一个新颖的，全面的数据集和机器来推进该领域 学习（ML）开发强大的模型，可以最大限度地提高相关结果的预测性能， 更好地调整候选人的临床轨迹和预期的移植结果。这些模型将更好地说明 捐助方和受援方变量之间复杂的相互关系，还将考虑到动态变化 候选人和捐赠者参数。然后将优化模型纳入决策支持系统 由关键利益攸关方指导。此外，还将使用此前开发的人工智能（AI）框架 优化心脏配置政策。我们有这些具体的目标：1）建立一个可行性和可用性， 成人HTx的决策支持系统; 2）证明一个 先前开发的基于人工智能的心脏分配策略优化框架;以及3）告知和评估 利用利益攸关方参与和实施科学， 完善和优化工作原型，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配置， 改善HTx的结果。

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.