Logistic regression model can reduce unnecessary artificial liver support in hepatitis B virus-associated acute-on-chronic liver failure: decision curve analysis

Gang Qin,Lei Zhang,Xiao-Hong Zhu,Yan-Mei Liu,Zhao-Lian Bian,Yi Shen,Jian-Guo Shao

doi:10.1186/s12911-016-0302-7

Abstract

BackgroundSeveral models have been proposed to predict the short-term outcome of acute-on-chronic liver failure (ACLF) after treatment. We aimed to determine whether better decisions for artificial liver support system (ALSS) treatment could be made with a model than without, through decision curve analysis (DCA).MethodsThe medical profiles of a cohort of 232 patients with hepatitis B virus (HBV)-associated ACLF were retrospectively analyzed to explore the role of plasma prothrombin activity (PTA), model for end-stage liver disease (MELD) and logistic regression model (LRM) in identifying patients who could benefit from ALSS. The accuracy and reliability of PTA, MELD and LRM were evaluated with previously reported cutoffs. DCA was performed to evaluate the clinical role of these models in predicting the treatment outcome.ResultsWith the cut-off value of 0.2, LRM had sensitivity of 92.6 %, specificity of 42.3 % and an area under the receiving operating characteristic curve (AUC) of 0.68, which showed superior discrimination over PTA and MELD. DCA revealed that the LRM-guided ALSS treatment was superior over other strategies including “treating all” and MELD-guided therapy, for the midrange threshold probabilities of 16 to 64 %.ConclusionsThe use of LRM-guided ALSS treatment could increase both the accuracy and efficiency of this procedure, allowing the avoidance of unnecessary ALSS.

Highlights

Several models have been proposed to predict the short-term outcome of acute-on-chronic liver failure (ACLF) after treatment
We reanalyzed the data in order to determine whether better decisions for artificial liver support system (ALSS) treatment could be made with a model (e.g. model for end-stage liver disease (MELD) or logistic regression model (LRM)) than without, through decision curve analysis
Eligible patients were enrolled with the following criteria: (i) aged between 18 and 70 years; (ii) presumptively diagnosed as hepatitis B surface antigen (HBsAg) carrier, chronic hepatitis B (CHB) or Hepatitis B virus (HBV)-related liver cirrhosis (HBC); (iii) progressive hyperbilirubinemia, with serum total bilirubin (TBil) ≥10 mg/dL; (iv) coagulopathy with plasma prothrombin activity (PTA) ≤40 % or international normalized ratio (INR) >1.5; (v) within 4 weeks from symptom onset complicated by ascites and/or hepatic encephalopathy (HE)

Summary

Introduction

Several models have been proposed to predict the short-term outcome of acute-on-chronic liver failure (ACLF) after treatment. Zheng et al, in a population of 452 patients with diagnosis of HBV-ACLF, established the logistic regression model (LRM) score, with an area under the receiving operating characteristic curve (AUC) of 0.844. LRM has shown promising results for prognosis prediction in HBV-ACLF ever since its introduction into clinical application. Yang et al compared the predictive performance of MELD with that of LRM in a population of 273 HBV-ACLF patients. In ACLF patients with liver cirrhosis (LC), the AUC of LRM (0.851) was comparable with that of MELD (0.840). In patients with noncirrhotic ACLF, the AUC of LRM (0.897) was significantly higher than that of MELD (0.758) [12]

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