Forecasting Lung Transplant Benefit: A Dynamic Risk Modeling Approach

项目摘要 越来越多的临床对肺移植的需求加剧了对这一有限但生命的配给问题- 节约社会资源。开发了肺分配评分(LAS)系统以提高总体存活率 通过确定哪些患者可能从移植中受益最多。尽管做出了这些努力，但还是有 等待名单死亡率的上升，移植后长期存活率的下降和急剧增加 在医疗费用和移植患者的利用率方面。 我们的项目专注于通过以下方式改进LAS系统：1)设计更好的方法以更准确地 确定等待移植的患者的病情进展，2)预测理想的移植时机 以最大化从移植中获得的年数，以及3)评估不同的分配策略和 它们对个人和人口层面的生存的影响。我们将通过实现以下目标来实现这一目标： 目标1：更新肺分配分数(LAS)潜在风险模型以更好地适应 肺移植受者之间的亚群水平随时间的差异。 目的2：建立并验证肺移植受者动态健康状态预测模型 使用基于系统的微模拟建模方法，随着时间的推移移植的可能性。 目标3：评估优化患者和人群水平功能的肺分配策略的影响 以及生存结果。 这项工作的结果将为改善美国的肺分配提供基础。我们会 优化肺移植时机，最大限度地提高个体患者和人群的移植效益 级别。本项目中确定的方法可以用于其他挽救有限生命的场景中 资源必须配给。

Project Summary Increasing clinical demand for lung transplants has exacerbated the problem of rationing this limited yet life- saving societal resource. The Lung Allocation Score (LAS) system was developed to improve overall survival by identifying patients who would likely benefit the most from transplant. Despite this effort, there have been increasing rates of waiting list mortality, declines in long-term survival after transplant and dramatic increases in healthcare costs and utilization among transplant patients. Our project focuses on improving the LAS system by: 1) designing better methodologies to more accurately identify the progression of illness in a patient who is awaiting transplant, 2) predicting ideal timing of transplant to maximize the number of years gained from a transplant, and 3) evaluating different allocation strategies and their impact on individual and population level survival. We will achieve this by carrying out the following aims: Aim 1: Update the lung allocation score (LAS) underlying risk models to better accommodate subpopulation-level differences over time among lung transplant candidates. Aim 2: Develop and validate a forecasting model for lung transplant candidates’ dynamic health state and likelihood of transplantation over time using a systems-based microsimulation modeling approach. Aim 3: Evaluate the impact of lung allocation strategies that optimize patient- and population-level functional and survival outcomes. The results of this work will provide the foundation for improving lung allocation in the United States. We will optimize timing of lung transplantation to maximize transplant benefit at the individual patient and population levels. The methods identified in this project can be utilized in other scenarios where limited life saving resources must be rationed.