Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer methods and programs in biomedicine Pub Date : 2024-09-29 DOI:10.1016/j.cmpb.2024.108442
Kiruthika Balakrishnan , Sawyer Olson , Gyorgy Simon , Lisiane Pruinelli
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Abstract

Background

The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the waiting list, they do not predict post-LT survival accurately. There is a need for predictive models that can forecast post-LT survival beyond the immediate period after transplantation.

Method

This study introduces new temporal variation features for predicting post-LT survival during the waiting list period. Cox Proportional-Hazards regression (CoxPH), Random Survival Forest (RSF), and Extreme Gradient Boosting (XGB) models are utilized, along with patient demographics and waiting list duration. Data from 716 LT patients from the University of Minnesota CTSI (2011–2021) are used to develop, evaluate, and compare post-LT survival prediction models.

Results

The temporal variation features, particularly when combined with the RSF model, proved most effective in predicting post-LT survival, with a C-index of 0.71 and an IBS of 0.151. This outperformed the predictive capability of the most recent MELD score, which had a C-index of <0.51 in the same cohort.

Conclusions

Incorporating temporal variation features with the RSF model enhances long-term post-LT survival predictions. These insights can assist clinicians and patients in making more informed decisions about organ allocation and understanding the utility of LT, ultimately leading to improved patient outcomes.
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肝移植后存活率的机器学习:通过时间变化特征缩小长期结果的差距。
背景:肝移植(LT)受者的长期存活率对于优化器官分配和估计死亡率结果至关重要。虽然肝病终末期模型(MELD)等模型可以预测候选名单上的 90 天死亡率,但却不能准确预测肝移植后的存活率。因此,我们需要能预测移植术后存活率的预测模型:本研究引入了新的时间变化特征,用于预测等待名单期间的 LT 后存活率。方法:本研究引入了新的时间变化特征,用于预测等待名单期间的 LT 后存活率。研究利用了 Cox 比例-危险回归(CoxPH)、随机生存森林(RSF)和极端梯度提升(XGB)模型,以及患者人口统计学和等待名单持续时间。明尼苏达大学CTSI的716名LT患者(2011-2021年)的数据被用来开发、评估和比较LT后生存预测模型:结果:时间变化特征,尤其是与 RSF 模型相结合时,在预测长管治疗后生存率方面被证明是最有效的,C 指数为 0.71,IBS 为 0.151。这优于最新 MELD 评分的预测能力,后者的 C 指数为结论:将时间变化特征与 RSF 模型相结合可提高 LT 后的长期生存预测能力。这些见解可帮助临床医生和患者就器官分配做出更明智的决定,并了解 LT 的效用,最终改善患者的预后。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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