Abstract:Objective: To explore the prognostic risk factors of the elderly liver failure patients and establish a new predictive model for prognosis. Methods: The clinical data of the 201 cases of elderly liver failure patients in the Affiliated Third Hospital of Jiangsu University from January 2012 to July 2020 were collected. The patients were divided into improved group (n=109) and deteriorated group (n=92). The baseline clinical parameters and laboratory markers of the two groups were analyzed through univariate analysis and multivariate Logistic regression analysis to obtain independent risk factors for prognosis of the elderly liver failure patients. Logistic regression was further used to establish a predictive model of joint indicators, the receiver operator characteristic (ROC) curve was used to evaluate value of the predictive model. Results: The univariate analysis displayed that the infection, leukocyte, elevated alanine aminotransferase, upper gastrointestinal hemorrhage, blood ammonia, hepatorenal syndrome, total bilirubin, hepatic encephalopathy and pothrombin time were significantly different between the two groups (P<0.05). Multivariate Logistic regression analysis showed that the infection (OR=8.344,P<0.05), pothrombin time (OR=1.185,P<0.01), total bilirubin (OR=1.005,P<0.01) and hepatic encephalopathy (OR=8.359,P<0.01) were the independent risk factors for the prognosis of elderly liver failure patients. Therefore, a new predictive model was established for prognosis: Y=-6.082+0.005×total bilirubin (μmol/L)+0.170×pothrombin time (s)+2.123×\[hepatic encephalopathy (no: 0, with: 1)\]+2.122×\[infection (no: 0, with: 1)\]. The area under the ROC curve of predictive model was 0.923(95%CI:0.885-0.961,P<0.001). Conclusion: The infection, pothrombin time, total bilirubin and hepatic encephalopathy were the independent risk factors for the prognosis of elderly liver failure patients; and the predictive model of combined 4 indicators has good value in predicting for the elderly liver failure patients.
[4]Biggins SW, Kim WR, Terrault NA, et al. Evidencebased incorporation of serum sodium concentration into MELD \[J\]. Gastroenterology, 2006, 130(6): 1652-1660.
[5]Cheng XP, Zhao J, Chen Y, et al. Comparison of the ability of the PDDICG clearance test, CTP, MELD and MELDNa to predict shortterm and mediumterm mortality in patients with decompensated hepatitis B cirrhosis\[J\]. Eur J Gastroenterol Hepatol, 2016, 28(4): 444-448.
[14]Flamm SL, Yang YX, Singh S, et al. American gastroenterological association institute guidelines for the diagnosis and management of acute liver failure\[J\]. Gastroenterology, 2017, 152(3): 644-647.
[16]Feng HL, Li Q, Cao WK, et al. Changes in thyroid function in patients with liver failure and their clinical significance: A clinical study of nonthyroidal illness syndrome in patients with liver failure\[J\]. Hepatob Pancreat Dis, 2020, 19(6): 561-566.
[17]Shalimar, Acharya SK, Kumar R, et al. Acute liver failure of nonAE viral hepatitis etiologyprofile, prognosis, and predictors of outcome\[J\]. J Clin Exp Hepatol, 2020, 10(5): 453-461.
[18]Mishra A, Rustgi V. Prognostic models in acute liver failure\[J\]. Clin Liver Dis, 2018, 22(2): 375-388.