Development of a Machine Learning Model for Liver Transplantation

项目摘要/摘要 目前，有近1.3万名患者等待肝脏移植，但只有三分之二的患者将接受肝移植 移植，2018年约有2500名患者死亡或因医疗原因被从等待名单中删除 恶化。可供移植的供体肝脏的短缺导致了边缘肝脏的使用 比典型的供体肝脏风险更高，但可能可以安全地移植到精心挑选的受者身上。 这些人包括老年捐赠者(70岁)、脂肪变性肝脏，以及通过心脏术后捐赠获得的肝脏。 死亡。由于它们的风险，这些边缘肝脏经常下降，但多达84%的患者 在移植等待名单上死亡的患者在死亡前拒绝了一个或多个边缘肝脏。有鉴于此，一定 等待名单上的候选人可能通过接受边缘移植物移植而获得生存益处 而不是留在等待名单上(即，在移植后，他们的存活时间会更长 边缘肝脏比他们在等待名单上活下来的时间更长)。 目前，关于特定边缘肝是否适合特定候选者的决定是基于 使用传统回归模型的临床格式塔或简单亚组分析，这可能不完全 大致描述供者、受者和移植因素之间的复杂相互作用。为了说明这一点，我们 将利用机器学习(它可以结合复杂的、更高阶的交互)来预测 特定的候选者将通过接受特定的边缘肝脏移植而获得生存益处， 并面试移植候选人和外科医生，以了解如何最好地将这些预测转化为 立即在临床上有用的决策辅助。 为了实现这一目标，我们将利用移植受者科学登记处(SRTR)的国家数据 (n=293,140)，并使用机器技术(随机森林)来解决以下目标：(1)预测 等待肝移植患者的存活率；(2)预测肝移植后存活率 边缘肝移植受者；以及(3)创建一个决策辅助工具，将预测的等待名单进行比较 边缘肝移植患者的存活率和移植后存活率的预测。这些 考虑到我们小组在肝移植方面的专业知识，分析国家登记数据， 和机器学习技术。 我们假设，利用SRTR和机器学习，我们可以准确地预测移植后的存活率 对于患有特定边缘肝脏的特定候选人，以及该候选人的等待生存 没有肝脏。我们还假设我们的决策辅助工具可以实时用于通知临床 决策。如果建议的目标得以实现，我们的辅助决策就可以用来改善临床。 通过将高质量的风险预测直接带给患者和移植专业人员直接 通知实时临床决策，候选人是否应该接受边缘肝脏移植。

PROJECT SUMMARY/ABSTRACT Currently, there are nearly 13,000 patients waitlisted for liver transplant, yet only two-thirds will receive a transplant, and in 2018 approximately 2,500 patients died or were removed from the waitlist due to medical deterioration. This shortage of donor livers available for transplant has led to the use of marginal livers – livers that are higher risk than typical donor livers but may be safely transplantable in carefully selected recipients. These include older donors (>70 years), steatotic livers, and livers procured through donation after cardiac death. Due to their riskiness, these marginal livers are often declined, yet as many as 84% of patients who died on the transplant waitlist declined one or more marginal livers prior to death. In light of this, certain waitlisted candidates might have derived a survival benefit from undergoing transplantation with a marginal organ rather than remaining on the waitlist (i.e. they would have survived longer after a transplant with a marginal liver than they would have survived on the waitlist). Currently, decisions about whether a particular marginal liver is suitable for a particular candidate are based on clinical gestalt or simple subgroup analysis using traditional regression models, which likely do not fully approximate the complex interactions between donor, recipient, and transplant factors. To account for this, we will utilize machine learning (which can incorporate complex, higher-order interactions) to predict whether a specific candidate would derive a survival benefit from undergoing transplantation with a specific marginal liver, and interview transplant candidates and surgeons to understand how best to translate these predictions into an immediately clinically-useful decision aid. To accomplish this, we will leverage Scientific Registry of Transplant Recipient (SRTR) national data (n=293,140) and use a machine technique (random forests) to address the following aims: (1) To predict waitlist survival for waitlisted liver transplant candidates; (2) To predict post-transplant survival for liver transplant recipients of a marginal liver; and (3) To create a decision aid that compares predicted waitlist survival and predicted post-transplant survival for a specific transplant candidates with marginal liver. These aims are highly feasible given our group’s expertise in liver transplantation, analysis of national registry data, and machine learning techniques. We hypothesize that utilizing SRTR and machine learning, we can accurately predict post-transplant survival for a particular candidate with a particular marginal liver, as well as waitlist survival for that same candidate without a liver. We also hypothesize that our decision aid could be utilized in real-time to inform clinical decision-making. If the proposed aims are achieved, our decision aid could be utilized to improve clinical practice by bringing high-quality risk prediction directly to patients and transplant professionals to directly inform the real-time clinical decision of whether a candidate should undergo transplant with a marginal liver.