Study of the Model to Predict 3-month Mortality Risk of Acute-on-chronic Hepatitis B Liver Failure
Condition: Acute-on-chronic Hepatitis B Liver Failure
MeSH terms: Hepatitis A; Hepatitis B; Hepatitis B, Chronic; Hepatitis; Hepatitis, Chronic; Liver Failure; Hepatic Insufficiency
Study type: Observational

Summary: 
      This study was to predict 3-month mortality risk of acute-on-chronic hepatitis B liver
      failure (ACHBLF) on an individual patient level using artificial neural network (ANN) system.
      The area under the curve of receiver operating characteristic (AUROC) were calculated for ANN
      and MELD-based scoring systems to evaluate the performances of the ANN prediction.
    

Detailed description: 
      Hepatitis B virus (HBV) is a major human pathogen which causes high morbidity and mortality
      worldwide. HBV is one of the leading causes for rapid deterioration of liver function, which
      is a serious condition termed as "acute-on-chronic liver failure (ACLF)" with high mortality.
      There is a high prevalence of HBV in Asian developing countries where acute-on-chronic
      hepatitis B liver failure (ACHBLF) accounts for more than 70% of ACLF and almost 120, 000
      patients died of ACHBLF each year. The transplantation of liver is the basic and strong
      effective therapeutic option for ACHBLF patients. However, liver transplantation is difficult
      to be extensively applied due to the shortage of liver donors and other socioeconomic
      problems. Thus, an early predictive model, which is objective, reasonable and accurate, is
      necessary for severity discrimination and organ allocation to decrease the mortality of
      ACHBLF.

      MELD-based scoring systems still failed to predict the mortality of a considerable proportion
      of patients and their predictive accuracy was not satisfying enough.

      The ANN is a novel computer model inspired by the working of human brain. It can build
      nonlinear statistical models to deal with the complex biological systems. In the recent
      years, ANN models have been introduced in clinical medicine for clinical validations,
      including predicting the hepatocellular carcinoma patients' disease-free survival and
      preoperative tumor grade, predicting the mortality of patients with end-stage liver disease
      and identifying the risk of prostate carcinoma.
    

Intervention name(s): Using training and testing groups to construct ANN based on laboratory tests
Intervention type(s): Other
Observational model: Case-Control
Time perspective: Cross-Sectional
Sampling method: Probability Sample
Study population: 
        The patients were collected who were diagnosed with ACLF at the First Affiliated Hospital
        of Wenzhou Medical College. Patients with ACHBLF were included in the study. ACHBLF was
        defined as an acute hepatic insult manifesting as jaundice and coagulopathy, complicated
        within 4 weeks by ascites and/or encephalopathy in a patient with chronic HBV infection.
        Patients with evidence of non-B hepatitis virus, alcohol abuse, autoimmune, toxic or other
        causes that might lead to liver failure, past or current hepatocellular carcinoma, liver
        transplantation, or serious diseases in other organ systems were excluded.

        Informed consent was obtained from each patient included in the study and the research
        protocol was approved by the Ethics Committee of the First Affiliated Hospital of Wenzhou
        Medical College.