Different models in predicting the short-term prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure
Journal Title: Annals of Hepatology - Year 2012, Vol 11, Issue 3
Abstract
Background and aims. Effective assessing the prognosis of patients with end-stage liver disease is always challenging. This study aimed to investigate the accuracy of different models in predicting short-term prognosis of patients with hepatitis B virus (HBV)-related acute-on-chronic liver failure (ACLF). Material and methods. We retrospectively evaluated survival of a cohort of patients with at least 3-month follow up. The receiver-operating-characteristic curves (ROC) were drawn for Child-Turcotte-Pugh (CTP) classification, King’s College Hospital (KCH) criteria, model for end-stage liver disease (MELD), MELD combined with serum sodium (Na) concentration (MELDNa), integrated MELD (iMELD) and logistic regression model (LRM). Results. Of the 273 eligible patients, 152 patients (55.7%) died within 3-month follow up. In cirrhoticpatients (n = 101), the AUCs of LRM (0.851), MELDNa (0.849), iMELD (0.845) and MELD (0.840) were all significantly higher than those of KCH criteria (0.642) and CTP (0.625) (all p < 0.05), while the differences among LRM, MELD, MELDNa and iMELD were not significant, and the most predictive cutoff value was 0.5176 for LRM, 30 for MELDNa, 47.87 for iMELD and 29 for MELD, respectively. In non-cirrhotic patients (n = 172), the AUC of LRM (0.897) was significantly higher than that of MELDNa (0.776), iMELD (0.768), MELD (0.758), KCH criteria (0.647) and CTP (0.629), respectively (all p < 0.05), and the most predictive cutoff value for LRM was -0.3264. Conclusions. LRM, MELD, MELDNa and iMELD are with similar accuracy in predicting the shortterm prognosis of HBV-ACLF patients with liver cirrhosis, while LRM is superior to MELD, MELDNa and iMELD in predicting the short-term prognosis of HBV-ACLF patients without liver cirrhosis.
Authors and Affiliations
Wen-Bin Yang, En-Qiang Chen, Hong-Xia Bi, Lang Bai, Xue-Bin Chen, Ping Feng, Hong Tang
Keywords
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