Pontes Balanza Beatriz, Castillo Tuñón Juan M, Mateos García Daniel, Padillo Ruiz Javier, Riquelme Santos José C, Álamo Martinez José M, Bernal Bellido Carmen, Suarez Artacho Gonzalo, Cepeda Franco Carmen, Gómez Bravo Miguel A, Marín Gómez Luis M
Department of Computer Languages and Systems, Sevilla University, Seville, Spain.
HPB Surgery Unit, Virgen Macarena University Hospital, Seville, Spain.
Front Surg. 2023 Sep 22;10:1048451. doi: 10.3389/fsurg.2023.1048451. eCollection 2023.
The complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it.
Liver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated "" for transplantation, and those discarded after the "" evaluation were considered as no transplantable liver grafts, while those grafts transplanted after "" evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed.
A total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound ( < 0.05). When studying the developed model, we observe that the best performance reordering in terms of accuracy corresponds to 76.29% with an area under the curve of 0.79. Furthermore, the model provides a classification together with a confidence index of reliability, for most cases in our data, with the probability of success in the prediction being above 0.85.
The tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation.
肝移植评估的复杂过程是肝移植领域有待改进之处。本研究的主要目的是开发一种工具，该工具可利用肝移植供体可获取的初始变量，辅助负责肝捐赠的外科医生在决定是否接受供肝时进行决策。
对脑死亡供体肝移植候选肝移植样本进行研究。所有样本均接受“移植评估”，“移植评估”后被弃用的样本被视为不可移植的肝移植样本，而“移植评估”后进行移植的样本则被视为可移植的肝移植样本。首先，开展一项单中心、回顾性队列研究，确定与不可移植组相关的危险因素。然后，开发一种基于机器学习的预测模型决策支持系统，该系统使用树集成增强分类器，能够辅助决定接受或拒绝供肝。
共研究了350例接受肝移植评估的肝移植样本。脂肪变性是将样本分类为不可移植的最常见原因，单变量研究中确定的主要危险因素包括年龄、血脂异常、个人病史、个人手术史、胆红素血症以及既往肝脏超声检查结果（P<0.05）。研究所开发的模型时，我们观察到，就准确率而言，最佳性能排序对应的准确率为76.29%，曲线下面积为0.79。此外，对于我们数据中的大多数病例，该模型提供了一个分类以及可靠性置信指数，预测成功的概率高于0.85。
本研究中提出的工具基于分配给它的初始变量，在预测肝移植样本是否会被移植或被视为不可移植方面具有较高的准确性。系统固有的改进能力使正确预测率随着新数据的输入而提高。因此，我们认为它是一种有助于优化肝移植供肝库 的工具。
