Clinical trial NCT01826760: Study of the Model to Predict 3-month Mortality Risk of Acute-on-chronic Hepatitis B Liver Failure
Official title: Study of 3-month Mortality Risk of Acute-on-chronic Hepatitis B Liver Failure Using Artificial Neural Network
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Acute-on-chronic Hepatitis B Liver Failure
Lead sponsor: Wenzhou Medical University
Sponsor class: OTHER
First posted: 2013-04-08
Enrollment: 583
Sex: ALL
Minimum age (years): 19
Maximum age (years): 87
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
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.

Brief 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.

Eligibility criteria: Inclusion Criteria:

* Acute hepatic insult manifesting as jaundice and coagulopathy
* Complicated within 4 weeks by ascites
* And/or encephalopathy in a patient with chronic HBV infection

Exclusion Criteria:

* Patients with evidence of non-B hepatitis virus
* alcohol abuse leads to liver failure
* autoimmune leads to liver failure
* oxic or other causes that might lead to liver failure
* past or current hepatocellular carcinoma
* liver transplantation
* serious diseases in other organ systems

Primary outcomes:
measure: Living status
timeFrame: Up to 08 months
measure: Calculating MELD-based Scoring Systems
timeFrame: Up to 02 months
measure: Construction of ANN
timeFrame: Up to 01 months
Secondary outcomes:
measure: Statistical Analysis
timeFrame: Up to 02 months
measure: Laboratory Tests
timeFrame: Up to 07 months
Other outcomes:
Locations:
city: Wenzhou
country: China
facility: Wenzhou Medical College
state: Zhejiang
Study officials:
affiliation: First Affiliated Hospital of Wenzhou Medical College
name: Ming Hua Zheng, Medical Master
role: STUDY_CHAIR
affiliation: Wenzhou Medical University
name: Xian Feng Lin, Medical undergraduate
role: STUDY_CHAIR
affiliation: First Affiliated Hospital of Wenzhou Medical College
name: Ke Qing Shi, Medical Master
role: STUDY_CHAIR
affiliation: Wenzhou Medical University
name: Wen Yue Liu, Medical undergraduate
role: PRINCIPAL_INVESTIGATOR
affiliation: Wenzhou Medical University
name: Chen Chen Zhao, Medical undergraduate
role: PRINCIPAL_INVESTIGATOR
References:
citation: Zheng MH, Shi KQ, Lin XF, Xiao DD, Chen LL, Liu WY, Fan YC, Chen YP. A model to predict 3-month mortality risk of acute-on-chronic hepatitis B liver failure using artificial neural network. J Viral Hepat. 2013 Apr;20(4):248-55. doi: 10.1111/j.1365-2893.2012.01647.x. Epub 2012 Aug 3.