Published in:
01-10-2012 | Original Article
Entecavir treatment prevents disease progression in hepatitis B virus-related acute-on-chronic liver failure: establishment of a novel logistical regression model
Published in: Hepatology International | Issue 4/2012
Login to get accessAbstract
Background
Hepatitis B-related acute-on-chronic liver failure (ACLF) has a poor prognosis with very high mortality. Unfortunately, most prognostic predictive models of liver failure are complicated and offer suboptimal sensitivity. Experience in entecavir (ETV)-treated patients with hepatitis B virus (HBV)-ACLF is limited.
Aims
This study was designed to evaluate the efficacy and safety of ETV in patients with HBV-ACLF and to develop a novel model (Tongji prognostic predictor model, TPPM) for prognostic prediction of HBV-ACLF patients.
Method
In this retrospective study, 248 patients with HBV-ACLF were enrolled. There were no significant differences in baseline clinical and virologic characteristics between patients treated with and without ETV.
Results
The 1- and 3-month survival rates of patients in the ETV-treated group (n = 124) were 72.58 and 61.29%, respectively, significantly higher than that in NA-free group (n = 124), which were 53.23 and 45.97%, respectively. By Hosmor and Lemeshow test, TPPM for HBV-ACLF had a very good degree of fit with disease prognosis. Based on this unique group of patients, the TPPM scoring offered a better prediction value in both specificity and sensitivity for 3-month mortality of patients with HBV-ACLF compared with MELD scoring system with statistically significant difference. In the patients with HBV-ACLF, using a cutoff of 0.22 for 3-month predicted mortality by TPPM, the positive predictive value was 93.6% and negative predictive value 91.3%.
Conclusion
ETV treatment prevented disease progression and increased the survival of patients with HBV-ACLF. The established TPPM scoring system offers superior predictor value in both specificity and sensitivity for HBV-ACLF patients when compared with MELD.