Evaluation of risk factors for hepatic encephalopathy in patients with acute-on-chronic hepatitis B liver failure using artificial neural network

Abstract

Abstract: Objective To investigate the value of artificial neural network (ANN) in evaluating the risk factors of hepatic encephalopathy in patients with hepatitis B virus - related acute-on-chronic liver failure (HBV-ACLF).Methods In this retrospective study, 479 patients with HBV-ACLF were enrolled. Sex, age, diabetes, celiac infection,pulmonary infection, upper gastrointestinal hemorrhage, albumin, globulin, total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase, cholinesterase, total cholesterol, potassium, sodium, creatinine, prothrombin time international normalized ratio (INR), white blood cell (WBC), hemoglobin (HGB), platelet(PLT), alpha-fetoprotein and HBV DNA were analyzed with univariate analysis for screening the significant risk factors for hepatic encephalopathy. Then the ANN model and the logistic regression model were used to determine the risk factors by multivariate analysis. The area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the value of ANN model and logistic regression model.Results The ANN model indicated that risk factors of hepatic encephalopathy in patients with HBV-ACLF were INR,WBC, HGB, age, sodium, total bilirubin, creatinine, AFP, celiac infection, upper gastrointestinal bleeding and pulmonary infection sequentially in turn. The logistic regression model showed that INR (B=0.45, P<0.001,OR=1.566), WBC (B=0.12, P<0.001,OR=1.125), HGB (B=-0.01, P=0.015,OR=0.987), and age (B=0.02, P=0.026,OR=1.021) were the significant risk factors. The AUC of the ANN model and logistic model were 0.881±0.016 and 0.825±0.020 (P=0.029), respectively.Conclusion ANN model might have a powerful clinic value in evaluating risk factors of hepatic encephalopathy in patients with HBV-ACLF, which revealed that higher INR, higher WBC, lower HGB, and older age were the significant risk factors of hepatic encephalopathy.

Key words: Liver failure, Hepatic encephalopathy, Risk factors, Artificial neural network (ANN), Regression analysis

LIU Bao-rong, FANG Jian-kai, LIN Ming-hua, GAO Hai-bing, PAN Chen.. Evaluation of risk factors for hepatic encephalopathy in patients with acute-on-chronic hepatitis B liver failure using artificial neural network[J]. Chinese Hepatolgy, 2017, 22(12): 1085-1089.

References

[1] 阿里木江,张静颖,刘祥,等.乙型肝炎相关慢加急性肝衰竭死亡相关因素分析240例.中国肝脏病杂志(电子版),2014,6:42-46.
[2] 汤伟亮,赵钢德,董志霞,等.肝衰竭预后危险因素及预后模型建立的研究.传染病信息,2011,24:159-162.
[3] 张千,何鸿雁,彭颖,等.慢加急性乙型肝炎肝衰竭患者的临床特点及预后影响因素分析.中国全科医学,2014,31:3713-3716.
[4] 中华医学会消化病学分会,中华医学会肝病学分会. 中国肝性脑病诊治共识意见(2013年,重庆). 中华肝脏病杂志, 2013, 21: 641-651.
[5] Cross SS, Harrison RF, Kennedy RL. Introduction to neural networks. Lancet, 1995, 346: 1075-1079.
[6] 中华医学会感染病学分会肝衰竭与人工肝学组,中华医学会肝病学分会重型肝病与人工肝学组. 肝衰竭诊治指南(2012年版). 中华肝脏病杂志, 2013, 21: 177-183.
[7] Zheng MH, Seto WK, Shi KQ, et al. Artificial neural network accurately predicts hepatitis B surface antigen seroclearance. PLoS One, 2014, 9: e99422.
[8] Cucchetti A, Vivarelli M, Heaton ND, et al. Artificial neural network is superior to MELD in predicting mortality of patients with end-stage liver disease. Gut, 2007, 56: 253-258.
[9] Bottaci L, Drew PJ, Hartley JE, et al. Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. Lancet, 1997, 350: 469-472.
[10] 吕日英,吴继周,李仕雄. 重型肝炎并发肝性脑病的影响因素研究. 中华全科医学, 2012, 10: 1847-1849.
[11] 潘晨,许利军,周锐,等. 乙型肝炎肝衰竭患者发生肝性脑病的多因素分析. 中华肝脏病杂志, 2012, 20: 434-437.
[12] Bernuau J, Durand F. Early prediction of encephalopathy in hospitalized patients with severe acute liver disease: the narrow window of opportunity for transplant-free survival. J Hepatol, 2009, 51: 977-980.
[13] Cordoba J, Ventura-Cots M, Simon-Talero M, et al. Characteristics, risk factors, and mortality of cirrhotic patients hospitalized for hepatic encephalopathy with and without acute-on-chronic liver failure (ACLF). J Hepatol, 2014, 60: 275-281.
[14] 潘纯凯,张延艳,马伟. 乙型肝炎肝衰竭患者发生肝性脑病的影响因素. 世界华人消化杂志,2014,22:4980-4984.
[15] Romero-Gomez M, Montagnese S, Jalan R. Hepatic encephalopathy in patients with acute decompensation of cirrhosis and acute-on-chronic liver failure. J Hepatol, 2015, 62: 437-447.
[16] Goral V, Atayan Y, Kaplan A. The relation between pathogenesis of liver cirrhosis, hepatic encephalopathy and serum cytokine levels: what is the role of tumor necrosis factor alpha? Hepatogastroenterology, 2011, 58: 943-948.
[17] Jain L, Sharma BC, Sharma P, et al. Serum endotoxin and inflammatory mediators in patients with cirrhosis and hepatic encephalopathy. Dig Liver Dis, 2012, 44: 1027-1031.
[18] Shawcross DL, Davies NA, Williams R, et al. Systemic inflammatory response exacerbates the neuropsychological effects of induced hyperammonemia in cirrhosis. J Hepatol, 2004, 40: 247-254.
[19] Berni E, Poole CD, Conway P, et al. Cost Effectiveness of rifaximin-A 550mg in the reduction of recurrence of overt hepatic encephalopathy in united Kingdom. Value Health, 2015, 18: A626.
[20] Ziada DH, Soliman HH, El Yamany SA, et al. Can Lactobacillus acidophilus improve minimal hepatic encephalopathy? A neurometabolite study using magnetic resonance spectroscopy. Arab J Gastroenterol, 2013, 14: 116-122.
[21] Dam G, Keiding S, Munk OL, et al. Hepatic encephalopathy is associated with decreased cerebral oxygen metabolism and blood flow, not increased ammonia uptake. Hepatology, 2013, 57: 258-265.