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Volume 38 Issue 11
Nov.  2022
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Article Navigation >  Journal of Clinical Hepatology  > 2022  >  38(11): 2493-2498

Developing the prediction model of esophagogastric variceal rebleeding in patients with liver cirrhosis based on artificial neural network

DOI: 10.3969/j.issn.1001-5256.2022.11.011

  • Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China

Research funding:

National Natural Science Foundation of China (81774234);

Beijing Municipal Science & Technology Commission (Z191100006619033)

More Information
  • Corresponding author: WANG Xianbo, wangxianbo638@163.com(ORCID: 0000-0002-3593-5741)
  • Received Date: 2022-03-07
  • Accepted Date: 2022-04-19
  • Published Date: 2022-11-20
    Abstract
  •   Objective  To develop an artificial neural network model to predict the risk of rebleeding within one year in cirrhotic patients with esophagogastric variceal bleeding.  Methods  We retrospectively collected 441 cirrhotic patients with esophagogastric variceal bleeding hospitalized at Beijing Ditan Hospital, Capital Medical University, from August 2008 to October 2017. The enrolled patients were followed up for one year. According to the primary endpoint which was rebleeding within one year, patients were divided into rebleeding (249 cases) and non-rebleeding (192 cases) groups. Fisher exact test or chi-square test were used for comparison of categorical data. Comparison of continuous data with normal distribution between groups was performed using t test, while comparison of non-normally distributed data was performed by Mann-Whitney U test. Cox univariate and multivariate regression were used to identify independent factors affecting rebleeding within one year in cirrhotic patients with esophagogastric variceal bleeding, and then an artificial neural network prediction model was constructed using identified factors. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC).  Results  In total, 249 (56.5%) patients developed esophagogastric variceal rebleeding within one year. Cox multivariate regression showed INR (AHR=1.566, 95%CI: 1.023~2.398, P=0.039) and NLR (AHR=1.033, 95%CI: 1.009~1.058, P=0.006) were risk factors for 1-year rebleeding, while CHB (AHR=0.769, 95%CI: 0.597~0.991, P=0.042), Na (AHR=0.967, 95%CI: 0.936~0.999, P=0.044), endoscopic (AHR=0.829, 95%CI: 0.743~0.926, P=0.001) and surgical treatment (AHR=0.246, 95%CI: 0.120~0.504, P < 0.001) were protective factors. Using the above six independent influence factors, we successfully constructed an artificial neural network model (https://lixuan.me/annmodel/myg-v3/). The model's ability to predict 1-year rebleeding had an AUC of 0.782 (95%CI: 0.740-0.825), which was higher than 0.672 (95%CI: 0.622-0.722, P < 0.001) of Cox regression model, 0.557 (95%CI: 0.504-0.610, P < 0.001) of Child-Pugh and 0.562 (95%CI: 0.509-0.616, P < 0.001) of MELD scores.  Conclusion  The artificial neural network model has good individualized prediction performance and can be used as a risk assessment tool for esophagogastric variceal rebleeding.

     

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    • References(19)
  • [1]
    HOLSTER IL, TJWA ET, MOELKER A, et al. Covered transjugular intrahepatic portosystemic shunt versus endoscopic therapy+β-blocker for prevention of variceal rebleeding[J]. Hepatology, 2016, 63(2): 581-589. DOI: 10.1002/hep.28318.
    [2]
    Chinese Society of Hepatology, Chinese Medical Association; Chinese Society of Gastroenterology, Chinese Medical Association; Chinese Society of Endoscopy, Chinese Medical Association. Guidelines for the diagnosis and treatment of esophageal and gastric variceal bleeding in cirrhotic portal hypertension[J]. J Clin Hepatol, 2016, 32(2): 203-219. DOI: 10.3969/j.issn.1001-5256.2016.02.002.

    中华医学会肝病学分会, 中华医学会消化病学分会, 中华医学会内镜学分会. 肝硬化门静脉高压食管胃静脉曲张出血的防治指南[J]. 临床肝胆病杂志, 2016, 32(2): 203-219. DOI: 10.3969/j.issn.1001-5256.2016.02.002.
    [3]
    VERDA D, PARODI S, FERRARI E, et al. Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods[J]. BMC Bioinformatics, 2019, 20(Suppl 9): 390. DOI: 10.1186/s12859-019-2953-8.
    [4]
    CHI S, TIAN Y, WANG F, et al. A novel lifelong machine learning-based method to eliminate calibration drift in clinical prediction models[J]. Artif Intell Med, 2022, 125: 102256. DOI: 10.1016/j.artmed.2022.102256.
    [5]
    GARCIA-TSAO G, SANYAL AJ, GRACE ND, et al. Prevention and management of gastroesophageal varices and variceal hemorrhage in cirrhosis[J]. Hepatology, 2007, 46(3): 922-938. DOI: 10.1002/hep.21907.
    [6]
    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.
    [7]
    FONG TV, HUNG FC, CHIU KW, et al. Model for end-stage liver disease (MELD) score for predicting late esophageal varices rebleeding in cirrhotic patients[J]. Hepatogastroenterology, 2008, 55(84): 1055-1058.
    [8]
    DING RH, HU YM, LI XG, et al. Current status of prevention and treatment of esophagogastric variceal bleeding in cirrhotic portal hypertension patients in Ningxia region: a multicenter study[J]. Chin J Dig Surg, 2021, 20(10): 1078-1084. DOI: 10.3760/cma.j.cn115610-20210928-00464.

    丁荣华, 胡燕梅, 李小果, 等. 宁夏地区肝硬化门静脉高压食管胃底静脉曲张出血防治现状的多中心研究[J]. 中华消化外科杂志, 2021, 20(10): 1078-1084. DOI: 10.3760/cma.j.cn115610-20210928-00464.
    [9]
    DENG SM, ZHANG JX, QI Y, et al. Study on the application of endoscopic ligation in esophageal varices and the risk factors of postoperative rebleeding[J]. Chin J Med Offic, 2021, 49(6): 718-720. DOI: 10.16680/j.1671-3826.2021.06.43.

    邓水苗, 张嘉星, 齐晔, 等. 内镜下套扎术在食管静脉曲张中应用及其术后再出血高危因素研究[J]. 临床军医杂志, 2021, 49(6): 718-720. DOI: 10.16680/j.1671-3826.2021.06.43.
    [10]
    HAO Y, TAN P, ZHAO YG, et al. Antibiotic prophylaxis in gastroesophageal variceal bleeding: A prospective study[J]. J Pract Hepatol, 2012, 15(1): 29-31. DOI: 10.3969/j.issn.1672-5069.2012.01.010.

    郝勇, 谭萍, 赵亚刚, 等. 肝硬化食管胃底静脉曲张出血患者预防性抗菌治疗的前瞻性研究[J]. 实用肝脏病杂志, 2012, 15(1): 29-31. DOI: 10.3969/j.issn.1672-5069.2012.01.010.
    [11]
    YANG H, LIU YX, LI P, et al. Analysis of risk factors for early rebleeding from esophageal and gastric varices in patients with liver cirrhosis[J]. J Clin Hepatol, 2014, 30(6): 540-542. DOI: 10.3969/j.issn.1001-5256.2014.06.18.

    杨花, 刘云霞, 李鹏, 等. 肝硬化患者食管胃底静脉曲张破裂早期再出血的危险因素分析[J]. 临床肝胆病杂志, 2014, 30(6): 540-542. DOI: 10.3969/j.issn.1001-5256.2014.06.18.
    [12]
    LIU X, HE L, HAN J, et al. Association of neutrophil-lymphocyte ratio and T lymphocytes with the pathogenesis and progression of HBV-associated primary liver cancer[J]. PLoS One, 2017, 12(2): e0170605. DOI: 10.1371/journal.pone.0170605.
    [13]
    PLEVRIS JN, DHARIWAL A, ELTON RA, et al. The platelet count as a predictor of variceal hemorrhage in primary biliary cirrhosis[J]. Am J Gastroenterol, 1995, 90(6): 959-961.
    [14]
    CHEN LH, HU NZ, WANG YL. The value of MESO (MELD/Na index) scoring system in predicting prognosis of patients with cirrhosis[J]. J Clin Hepatol, 2011, 27(10): 1044-1046, 1054. DOI: 10.3969/j.issn.1001-5256.2011.10.008.

    陈丽红, 胡乃中, 王亚雷. 终末期肝病模型与血清钠比值在肝硬化患者预后判断中的价值[J]. 临床肝胆病杂志, 2011, 27(10): 1044-1046, 1054. DOI: 10.3969/j.issn.1001-5256.2011. 10.008.
    [15]
    HONG JB, LYU NH, WANG AJ, et al. Analysis of the risk factors for early rebleeding in oesophageal and gastric varices[J]. Chin J Dig, 2010, 30(11): 836-837. DOI: 10.3760/cma.j.issn.0254-1432.2010.11.013.

    洪军波, 吕农华, 汪安江, 等. 食管胃静脉曲张早期再出血的危险因素分析[J]. 中华消化杂志, 2010, 30(11): 836-837. DOI: 10.3760/cma.j.issn.0254-1432.2010.11.013.
    [16]
    HUO TI, LIN HC, WU JC, et al. Proposal of a modified Child-Turcotte-Pugh scoring system and comparison with the model for end-stage liver disease for outcome prediction in patients with cirrhosis[J]. Liver Transpl, 2006, 12(1): 65-71. DOI: 10.1002/lt.20560.
    [17]
    KAMATH PS, WIESNER RH, MALINCHOC M, et al. A model to predict survival in patients with end-stage liver disease[J]. Hepatology, 2001, 33(2): 464-470. DOI: 10.1053/jhep.2001.22172.
    [18]
    HEUMAN DM, ABOU-ASSI SG, HABIB A, et al. Persistent ascites and low serum sodium identify patients with cirrhosis and low MELD scores who are at high risk for early death[J]. Hepatology, 2004, 40(4): 802-810. DOI: 10.1002/hep.20405.
    [19]
    ANGELI P, WONG F, WATSON H, et al. Hyponatremia in cirrhosis: Results of a patient population survey[J]. Hepatology, 2006, 44(6): 1535-1542. DOI: 10.1002/hep.21412.
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