PDF(Pubmed)
Abstract：
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.
摘要：
经颈静脉肝内门体分流术(TIPS)是门脉高压症的重要治疗方法,但可导致肝性脑病(HE)。恶化患者预后的严重并发症。研究TIPS后HE的预测因素对改善预后至关重要。这篇综述分析了风险因素，并比较了预测模型，权衡传统分数，如Child-Pugh，终末期肝病模型（MELD），和白蛋白-胆红素（ALBI）对抗新兴的人工智能（AI）技术。虽然传统评分提供了对HE风险的初步见解，它们在处理临床复杂性方面存在局限性.机器学习(ML)的进步，特别是当与成像和临床数据集成时，提供完善的评估。这些创新表明AI有可能显着改善TIPS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，同时倡导人工智能的整合，以提高TIPS后HE评估的准确性。通过利用人工智能的力量，临床医生可以更好地管理与TIPS相关的风险,并根据患者个人需求定制干预措施.因此，未来的研究应该优先开发先进的人工智能框架，这些框架可以吸收不同的数据流，以支持临床决策。我们的目标不仅是更准确地预测HE，同时也改善了患者的整体护理和生活质量。
