背景:肝移植(LT)是终末期肝病患者的一种挽救生命的干预措施。然而,公平分配稀缺的捐赠器官仍然是一个巨大的挑战。预后工具对于确定最合适的移植候选者至关重要。传统上,像终末期肝病模型这样的评分系统在这一过程中发挥了重要作用。然而,随着机器学习(ML)和人工智能模型的集成,预测的格局正在经历一场变革。
目的:为了评估ML模型在LT预测中的实用性,将它们的性能和可靠性与已建立的传统评分系统进行比较。
方法:遵循系统评价和荟萃分析指南的首选报告项目,我们使用PubMed/MEDLINE数据库进行了全面和标准化的文献检索.我们的搜索对出版年份没有任何限制,年龄,或性别。排除标准包括非英语stu死亡,评论文章,病例报告,会议文件,缺失数据的研究,或者那些表现出明显的方法论缺陷的人。
结果:我们的搜索共产生了64篇文章,23人符合纳入标准。在选定的研究中,60.8%来自美国和中国的总和。只有一项儿科研究符合标准。值得注意的是,91%的研究是在过去五年内发表的。在所有研究中,ML模型始终显示出令人满意的接收器工作特征曲线值(范围从0.6到1)下的出色区域。超越传统评分系统的性能。随机森林对LT后90天死亡率表现出优异的预测能力,脓毒症,急性肾损伤(AKI)。相比之下,梯度增强在预测移植物抗宿主病的风险方面表现出色,肺炎,AKI。
结论:这项研究强调了ML模型在指导同种异体移植分配和LT相关决策方面的潜力,标志着预测领域的重大演变。
BACKGROUND: Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of
prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models.
OBJECTIVE: To assess the utility of ML models in
prognostication for LT, comparing their per formance and reliability to established traditional scoring systems.
METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies,
review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws.
RESULTS: Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI.
CONCLUSIONS: This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of
prognostication.