Forecasting Lung Transplant Benefit: A Dynamic Risk Modeling Approach

项目摘要 对肺移植的临床需求不断增加，加剧了对这一有限生命的配给问题- 节约社会资源。肺分配评分（LAS）系统的开发是为了提高总生存率 通过确定哪些患者可能从移植中受益最大。尽管有这些努力， 等待名单上的死亡率上升，移植后长期存活率下降， 在医疗成本和移植患者中的使用率。 我们的项目重点是通过以下方式改进LAS系统：1）设计更好的方法， 确定等待移植的患者的疾病进展，2）预测移植的理想时机 最大限度地增加从移植中获得的年数，以及3）评估不同的分配策略， 对个体和群体生存的影响。我们将通过实现以下目标来实现这一目标： 目的1：更新肺分配评分（LAS）基础风险模型，以更好地适应 肺移植候选人之间随时间推移的亚群水平差异。 目的2：开发并验证肺移植候选人动态健康状态的预测模型， 移植的可能性随着时间的推移使用基于系统的微观模拟建模方法。 目的3：评价优化患者和人群水平功能的肺分配策略的影响 和生存结果。 这项工作的结果将为改善美国的肺分配提供基础。我们将 优化肺移植的时机，以使个体患者和人群的移植获益最大化 程度.在这个项目中确定的方法可以用于其他情况下，有限的生命挽救 资源必须合理分配。

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.