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人工智能在慢加急性肝衰竭预后预测模型中的研究现状

姜伟 常秀君 曾帆 兰蕴平

引用本文:
Citation:

人工智能在慢加急性肝衰竭预后预测模型中的研究现状

DOI: 10.12449/JCH240927
基金项目: 

四川省科技攻关计划 (2023YFS0134)

利益冲突声明:本文不存在任何利益冲突。
作者贡献声明:姜伟负责课题设计,资料分析,撰写论文;姜伟、常秀君参与文献检索和归纳;曾帆、兰蕴平负责拟定写作思路,指导撰写文章并最后定稿。
详细信息
    通信作者:

    兰蕴平, ashley.lan@uestc.edu.cn (ORCID: 0000-0002-9378-9498)

Current status of research on artificial intelligence in prognostic prediction models for acute-on-chronic liver failure

Research funding: 

Sichuan Provincial Science and Technology Research Program (2023YFS0134)

More Information
  • 摘要: 慢加急性肝衰竭(ACLF)是在慢性肝病基础上出现的急性肝功能恶化,且以肝脏和/或肝外器官衰竭和短期高病死率为主要特征的复杂临床综合征。目前缺乏有效的治疗手段,内科综合治疗下病死率高达50%~90%。开发简单快捷、准确性高的ACLF预后预测模型,能帮助临床医师早期准确判断ACLF患者预后,识别预后不良患者,从而实施早期干预,可在一定程度上改善预后,有助于降低病死率。随着计算机科学的不断发展,数据处理能力愈发强大,人工智能越来越受到重视,在肝脏疾病的诊断、治疗、预后预测等多方面均有应用。本文结合国内外研究现状,对常见的ACLF预后模型和机器学习预后预测模型进行综述,总结最新研究进展,为ACLF预后预测模型未来发展提供新思路。

     

  • 表  1  传统ACLF预后模型总结及比较

    Table  1.   The summary and comparison of common prognostic models for ACLF patients

    评分模型 构建模型样本量 纳入指标 模型构建方式 优点 缺点
    CTP9 - HE分级、腹腔积液程度、TBil、Alb、PT 分层分析 计算简单,数据易获取,多方面评估基础肝功能情况 未对患者一般情况及其他器官进行评估
    MELD10 - TBil、INR、Cr、病因、年龄 3.78×ln(TBil)+11.2×ln(INR)+ 9.57×ln(Cr)+6.43×(胆汁性或酒精性0,其他1)(线性回归) 纳入年龄和病因,评估患者的一般状况 未对感染、肝性脑病等预后高危因素进行评价
    APACHEⅡ11 100例酒精相关性ACLF APS、CPS 线性回归 客观地反映出患者疾病严重程度 指标涉及面广泛,复杂且难以实施
    CLIF-C12 1 349例CANONIC研究患者 PaO2/FiO2、SPO2/FiO2、WBC、HE分级、TBil、Cr、INR、MAP CLIF-C ACLF=10×[0.33×CLIF-OF+0.04×年龄+0.63×ln(WBC)-2](线性回归) 评估了除肝脏以外的循环系统、呼吸系统等器官的情况 计算繁琐,基于西方国家酒精性肝硬化患者,对其他病因患者预测性能未得到验证
    AARC-ACLF13 1 402例符合APASL定义的ACLF患者 INR、Cr、TBil、HE分级、血乳酸 分层分析 计算简单,检验结果易获取,样本量大 缺乏大样本量验证模型,对其他标准下的预测性能不详
    COSSH-ACLF14 1 322例重症乙型肝炎患者 INR、TBil、血尿素、年龄、中性粒细胞数量、HE分级 1.649×ln(INR)+0.457 8×HE评分+0.425×ln(中性粒胞)+ 0.396×ln(TBil)+0.576×ln(尿素氮)+0.33×年龄(线性回归) 基于大样本量HBV-ACLF,对HBV-ACLF患者预测价值较高 缺乏大样本量验证模型,对其他病因ACLF患者预测性能不详

    注:APACHE Ⅱ,急性生理慢性健康评分;CLIF-C,慢性肝脏疾病研究协作组评分;AARC-ACLF,亚太肝病学会慢加急性肝衰竭评分;COSSH-ACLF,中国肝病协作组慢加急性肝功能衰竭评分;INR,国际标准化比值;PT,凝血酶原时间;Cr,血清肌酐;APS,急性生理评分;CPS,慢性健康评分;PaO2,动脉血氧浓度;FiO2,吸入氧浓度;SPO2,血氧饱和度;MAP,平均动脉压;APASL,亚太肝病学会;CLIF-OF,CLIF-器官衰竭评分系统。

    下载: 导出CSV

    表  2  ACLF中的机器学习模型

    Table  2.   Mechine learning model of ACLF

    作者 方法 诊断标准 比较对象 28天预测准确性 90天预测准确性
    Zheng等23 ANN APASL-ACLF MELD - AUC=0.765,P<0.001
    Hou等24 ANN APASL-ACLF

    MELD、MELD-Na、CTP、

    CLIF-ACLF

    AUC=0.748,95%CI:0.673~0.822 AUC=0.754,95%CI:0.697~0.812
    Shi等25 CART、LR APASL-CLIF MELD - CART:AUC=0.896,LR:AUC=0.914,P值均<0.001
    Verma等26 XGB-CV APASL-ACLF

    XGB、GBM、RF、DL、NB、

    FLM、DT、SVM、MELD

    AUC=0.878,95%CI:0.854~0.898(30天) XGB-CV:AUC=0.919,95%CI:0.908~0.930

    注:SVM,支持向量机。

    下载: 导出CSV
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  • 收稿日期:  2023-12-19
  • 录用日期:  2024-01-18
  • 出版日期:  2024-09-25
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