Xu Xiaowei, Yang Yun, Tan Xinru, Zhang Ziyang, Wang Boxiang, Yang Xiaojie, Weng Chujun, Yu Rongwen, Zhao Qi, Quan Shichao
Department of Gastroenterology Nursing Unit, Ward 192, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
School of Nursing, Wenzhou Medical University, Wenzhou 325001, China.
Comput Struct Biotechnol J. 2024 Jul 10;24:493-506. doi: 10.1016/j.csbj.2024.07.008. eCollection 2024 Dec.
Transjugular intrahepatic portosystemic shunt (TIPS) is an essential procedure for the treatment of portal hypertension but can result in hepatic encephalopathy (HE), a serious complication that worsens patient outcomes. Investigating predictors of HE after TIPS is essential to improve prognosis. This review analyzes risk factors and compares predictive models, weighing traditional scores such as Child-Pugh, Model for End-Stage Liver Disease (MELD), and albumin-bilirubin (ALBI) against emerging artificial intelligence (AI) techniques. While traditional scores provide initial insights into HE risk, they have limitations in dealing with clinical complexity. Advances in machine learning (ML), particularly when integrated with imaging and clinical data, offer refined assessments. These innovations suggest the potential for AI to significantly improve the prediction of post-TIPS HE. The study provides clinicians with a comprehensive overview of current prediction methods, while advocating for the integration of AI to increase the accuracy of post-TIPS HE assessments. By harnessing the power of AI, clinicians can better manage the risks associated with TIPS and tailor interventions to individual patient needs. Future research should therefore prioritize the development of advanced AI frameworks that can assimilate diverse data streams to support clinical decision-making. The goal is not only to more accurately predict HE, but also to improve overall patient care and quality of life.
经颈静脉肝内门体分流术（TIPS）是治疗门静脉高压的重要手段，但可能导致肝性脑病（HE），这是一种严重的并发症，会恶化患者的预后。研究TIPS术后HE的预测因素对于改善预后至关重要。本综述分析了风险因素并比较了预测模型，权衡了传统评分，如Child-Pugh评分、终末期肝病模型（MELD）和白蛋白-胆红素（ALBI）评分，与新兴的人工智能（AI）技术。虽然传统评分能初步洞察HE风险，但在处理临床复杂性方面存在局限性。机器学习（ML）的进展，特别是与影像学和临床数据相结合时，能提供更精确的评估。这些创新表明AI有潜力显著改善TIPS术后HE的预测。该研究为临床医生提供了当前预测方法的全面概述，同时倡导整合AI以提高TIPS术后HE评估的准确性。通过利用AI的力量，临床医生可以更好地管理与TIPS相关的风险，并根据个体患者需求定制干预措施。因此，未来的研究应优先开发先进的AI框架，该框架能够整合各种数据流以支持临床决策。目标不仅是更准确地预测HE，还要改善整体患者护理和生活质量。
