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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
  • [1] MOREAU R, JALAN R, GINES P, et al. Acute-on-chronic liver failure is a distinct syndrome that develops in patients with acute decompensation of cirrhosis[J]. Gastroenterology, 2013, 144( 7): 1426- 1437. e 9. DOI: 10.1053/j.gastro.2013.02.042.
    [2] MIAO N, WANG FZ, ZHENG H, et al. Estimation of incidence of viral hepatitis B and analysis on case characteristics in China, 2013-2020[J]. Chin J Epidemiol, 2021, 42( 9): 1527- 1531. DOI: 10.3760/cma.j.cn112338-20210319-00227.

    缪宁, 王富珍, 郑徽, 等. 中国2013—2020年乙型肝炎发病情况估算和病例特征分析[J]. 中华流行病学杂志, 2021, 42( 9): 1527- 1531. DOI: 10.3760/cma.j.cn112338-20210319-00227.
    [3] ARROYO V, MOREAU R, JALAN R. Acute-on-chronic liver failure[J]. N Engl J Med, 2020, 382( 22): 2137- 2145. DOI: 10.1056/nejmra1914900.
    [4] JALAN R, GUSTOT T, FERNANDEZ J, et al.‘Equity’ and‘Justice’ for patients with acute-on chronic liver failure: A call to action[J]. J Hepatol, 2021, 75( 5): 1228- 1235. DOI: 10.1016/j.jhep.2021.06.017.
    [5] LING SB, JIANG GJ, QUE QY, et al. Liver transplantation in patients with liver failure: Twenty years of experience from China[J]. Liver Int, 2022, 42( 9): 2110- 2116. DOI: 10.1111/liv.15288.
    [6] LI P, LIANG X, LUO JJ, et al. Predicting the survival benefit of liver transplantation in HBV-related acute-on-chronic liver failure: An observational cohort study[J]. Lancet Reg Health West Pac, 2022, 32: 100638. DOI: 10.1016/j.lanwpc.2022.100638.
    [7] YANG LS, SHAN LL, SAXENA A, et al. Liver transplantation: A systematic review of long-term quality of life[J]. Liver Int, 2014, 34( 9): 1298- 1313. DOI: 10.1111/liv.12553.
    [8] HERNAEZ R, LIU Y, KRAMER JR, et al. Model for end-stage liver disease-sodium underestimates 90-day mortality risk in patients with acute-on-chronic liver failure[J]. J Hepatol, 2020, 73( 6): 1425- 1433. DOI: 10.1016/j.jhep.2020.06.005.
    [9] PUGH RN, MURRAY-LYON IM, DAWSON JL, et al. Transection of the oesophagus for bleeding oesophageal varices[J]. Br J Surg, 1973, 60( 8): 646- 649. DOI: 10.1002/bjs.1800600817.
    [10] MALINCHOC M, KAMATH PS, GORDON FD, et al. A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts[J]. Hepatology, 2000, 31( 4): 864- 871. DOI: 10.1053/he.2000.5852.
    [11] KNAUS WA, ZIMMERMAN JE, WAGNER DP, et al. APACHE-acute physiology and chronic health evaluation: A physiologically based classification system[J]. Crit Care Med, 1981, 9( 8): 591- 597. DOI: 10.1097/00003246-198108000-00008.
    [12] HERNAEZ R, SOLÀ E, MOREAU R, et al. Acute-on-chronic liver failure: An update[J]. Gut, 2017, 66( 3): 541- 553. DOI: 10.1136/gutjnl-2016-312670.
    [13] CHOUDHURY A, JINDAL A, MAIWALL R, et al. Liver failure determines the outcome in patients of acute-on-chronic liver failure(ACLF): Comparison of APASL ACLF research consortium(AARC) and CLIF-SOFA models[J]. Hepatol Int, 2017, 11( 5): 461- 471. DOI: 10.1007/s12072-017-9816-z.
    [14] WU T, LI J, SHAO L, et al. Development of diagnostic criteria and a prognostic score for hepatitis B virus-related acute-on-chronic liver failure[J]. Gut, 2018, 67( 12): 2181- 2191. DOI: 10.1136/gutjnl-2017-314641.
    [15] DEO RC. Machine learning in medicine[J]. Circulation, 2015, 132( 20): 1920- 1930. DOI: 10.1161/CIRCULATIONAHA.115.001593.
    [16] ZHOU J, HU B, FENG W, et al. An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT[J]. NPJ Digit Med, 2023, 6( 1): 119. DOI: 10.1038/s41746-023-00866-z.
    [17] WALSH JA, ROZYCKI M, YI E, et al. Application of machine learning in the diagnosis of axial spondyloarthritis[J]. Curr Opin Rheumatol, 2019, 31( 4): 362- 367. DOI: 10.1097/BOR.0000000000000612.
    [18] VAMATHEVAN J, CLARK D, CZODROWSKI P, et al. Applications of machine learning in drug discovery and development[J]. Nat Rev Drug Discov, 2019, 18( 6): 463- 477. DOI: 10.1038/s41573-019-0024-5.
    [19] TIAN D, YAN HJ, HUANG H, et al. Machine learning-based prognostic model for patients after lung transplantation[J]. JAMA Netw Open, 2023, 6( 5): e2312022. DOI: 10.1001/jamanetworkopen.2023.12022.
    [20] AHN JC, CONNELL A, SIMONETTO DA, et al. Application of artificial intelligence for the diagnosis and treatment of liver diseases[J]. Hepatology, 2021, 73( 6): 2546- 2563. DOI: 10.1002/hep.31603.
    [21] KINOSHITA F, TAKENAKA T, YAMASHITA T, et al. Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer[J]. Sci Rep, 2023, 13( 1): 15683. DOI: 10.1038/s41598-023-42964-8.
    [22] SIDEY-GIBBONS JAM, SIDEY-GIBBONS CJ. Machine learning in medicine: A practical introduction[J]. BMC Med Res Methodol, 2019, 19( 1): 64. DOI: 10.1186/s12874-019-0681-4.
    [23] ZHENG MH, SHI KQ, LIN XF, et al. A model to predict 3-month mortality risk of acute-on-chronic hepatitis B liver failure using artificial neural network[J]. J Viral Hepat, 2013, 20( 4): 248- 255. DOI: 10.1111/j.1365-2893.2012.01647.x.
    [24] HOU YX, ZHANG QQ, GAO FY, et al. Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure[J]. BMC Gastroenterol, 2020, 20( 1): 75. DOI: 10.1186/s12876-020-01191-5.
    [25] SHI KQ, ZHOU YY, YAN HD, et al. Classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees[J]. J Viral Hepat, 2017, 24( 2): 132- 140. DOI: 10.1111/jvh.12617.
    [26] VERMA N, CHOUDHURY A, SINGH V, et al. APASL-ACLF Research Consortium-Artificial Intelligence(AARC-AI) model precisely predicts outcomes in acute-on-chronic liver failure patients[J]. Liver Int, 2023, 43( 2): 442- 451. DOI: 10.1111/liv.15361.
    [27] LUO W, PHUNG D, TRAN T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: A multidisciplinary view[J]. J Med Internet Res, 2016, 18( 12): e323. DOI: 10.2196/jmir.5870.
    [28] YU KH, BEAM AL, KOHANE IS. Artificial intelligence in healthcare[J]. Nat Biomed Eng, 2018, 2( 10): 719- 731. DOI: 10.1038/s41551-018-0305-z.
    [29] CAO ZJ, LI FD, XIANG XG, et al. Circulating cell death biomarker: Good candidates of prognostic indicator for patients with hepatitis B virus related acute-on-chronic liver failure[J]. Sci Rep, 2015, 5: 14240. DOI: 10.1038/srep14240.
    [30] ARIZA X, GRAUPERA I, COLL M, et al. Neutrophil gelatinase-associated lipocalin is a biomarker of acute-on-chronic liver failure and prognosis in cirrhosis[J]. J Hepatol, 2016, 65( 1): 57- 65. DOI: 10.1016/j.jhep.2016.03.002.
    [31] JUANOLA A, GRAUPERA I, ELIA C, et al. Urinary L-FABP is a promising prognostic biomarker of ACLF and mortality in patients with decompensated cirrhosis[J]. J Hepatol, 2022, 76( 1): 107- 114. DOI: 10.1016/j.jhep.2021.08.031.
    [32] PENG H, ZHANG Q, LUO L, et al. A prognostic model of acute-on-chronic liver failure based on sarcopenia[J]. Hepatol Int, 2022, 16( 4): 964- 972. DOI: 10.1007/s12072-022-10363-2.
    [33] HE TC, FONG JN, MOORE LW, et al. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer[J]. Comput Med Imaging Graph, 2021, 89: 101894. DOI: 10.1016/j.compmedimag.2021.101894.
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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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