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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要心脏移植 (HTx) 的影响仍然受到捐赠者短缺的限制，估计有 250,000 名成年人尽管美国每年只进行 3,500 例移植手术，但他们仍可能受益于移植手术。不幸的是，捐助者丢弃率仍然高达 70-80%，捐赠者评估和接受方面存在大量无法解释的差异中心之间的实践。最近的数据还表明，接受移植的风险较高根据2018年新的分配政策，全国移植后存活率较差。这些趋势共同强调当前分配政策的局限性以及个别计划评估的能力捐赠者质量并将合适的捐赠者与适当选择的接受者配对。后者源于一个临床医生必须仅依靠经验做出时间敏感的决策的次优过程无需数据驱动工具即可进行判断，这些工具可以分析大量的捐赠者和接受者数据及其复杂的数据互动以提供快速、准确的结果预测。现有的风险模型未能获得由于主要限制而被广泛使用，包括 1) 仅关注一组相关的其中一个结果，2) 简单化方法，只有适度的歧视能力（c 统计量 <0.70），3) 未能考虑捐赠者和接受者变量之间复杂的相互作用，以及 4) 仅使用静态、横截面数据。我们的提案旨在通过利用新颖、全面的数据集和机器来推动该领域的发展学习（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，并更好地调整候选人的临床轨迹和预期的移植结果。这些模型将更好地解释用于捐助者和接受者变量之间复杂的相互关系，并且还将考虑动态变化候选者和捐赠者参数。然后优化的模型将被纳入决策支持系统由主要利益相关者指导。此外，还将使用之前开发的人工智能（AI）框架优化心脏配置政策。我们有以下具体目标： 1) 建立利益相关者引导的、基于机器学习的成人 HTx 决策支持系统； 2）展示适应性之前开发了基于人工智能的心脏分配政策优化框架； 3) 告知并评估利用利益相关者参与和实施科学来确定具体目标 1 和 2 的流程和产出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，并最终改善 HTx 的结果。