Acute-on-chronic Hepatitis B Liver FailureNCT01826760
Study of the Model to Predict 3-month Mortality Risk of Acute-on-chronic Hepatitis B Liver Failure
An observational study of Acute-on-chronic Hepatitis B Liver Failure, run by Wenzhou Medical University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 19 to 87 yearsHealthy volunteers can take part
- Conditions the study is for
- Acute-on-chronic Hepatitis B Liver Failure
- What is being tested?
- Using training and testing groups to construct ANN based on laboratory tests
- How many people
- 583 participants
- Where
- 1 siteChina
- Study dates
- Apr 2010to Jun 2010
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 19 to 87 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- Acute hepatic insult manifesting as jaundice and coagulopathy
- Complicated within 4 weeks by ascites
- And/or encephalopathy in a patient with chronic HBV infection
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Study of 3-month Mortality Risk of Acute-on-chronic Hepatitis B Liver Failure Using Artificial Neural Network
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.
Read the 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.
- Being tested
- Using training and testing groups to construct ANN based on laboratory tests
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Using training and testing groups to construct ANN based on laboratory testsOther
| Study group | Role | What they receive |
|---|---|---|
| acute-on-chronic hepatitis B liver failure, training group | 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 according to consensus recommendations of the Asian Pacific Association for the Study of the Liver in 2009. ACHBLF patients were assigned to a training cohort and a validation cohort randomly. One of the major limitations of ANN is over-training, which can lead to good performance on training sets but poor performance on relatively independent validation sets. To avoid over-training during building ANN, a part of ACHBLF patients were again randomly selected from the training group to train the network and the remaining were used for cross-validation. Receives: Other: Using training and testing groups to construct ANN based on laboratory tests | |
| acute-on-chronic hepatitis B liver failure, testing group | 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 according to consensus recommendations of the Asian Pacific Association for the Study of the Liver in 2009. To avoid over-training during building ANN, a part of ACHBLF patients were again randomly selected from the training group to train the network and the remaining were used for cross-validation. Receives: Other: Using training and testing groups to construct ANN based on laboratory tests |
What is being measured
The primary outcome is the main question the study is designed to answer.
Living status
The routine therapy of patients were same, including absolute bed rest, energy supplements and vitamins, intravenous drop infusion albumin, maintenance water, electrolyte and acid-base equilibrium, and prevention and treatment complications, etc. The start date of the follow-up was the date of the diagnosis of ACHBLF. In this study, patients receiving liver transplantation within 3 months were considered as death. All patients with ACHBLF were followed up for at least 3 months and the outcome (death or survival) of corresponding patient was recorded.
Measured: Up to 08 months
Calculating MELD-based Scoring Systems
MELD score (R = 9.57 × ln (creatinine (mg/dL)) + 3.78×ln (bilirubin (mg/dL)) + 11.2×ln (INR) + 6.43) was used to measure the mortality risk in patients with end-stage liver disease. Given the lack of donors, MELD was used as organ allocation tool to increase graft success rate and patient survival rates, which was generally accepted. Recently, some adjustments were added to the original MELD formula to overcome limitations of MELD score. Published data suggested that MELD-Na (R = MELD + 1.59 × (135 - serum sodium (mmol/L))) might improve the prognostic accuracy [5]. Furthermore, several other scoring systems such as MELDNa (R = MELD - serum sodium (mmol/L) - (0.025 × MELD × (140 - serum sodium (mmol/L))) + 140), MESO (R = (MELD/serum sodium (mmol/L)) × 100), iMELD (R = MELD + (age(year) × 0.3) - (0.7 × serum sodium (mmol/L)) + 100)), etc had been described for predicting the mortality of end-stage liver disease accurately.
Measured: Up to 02 months
Construction of ANN
ANN can mimic a biological neural system both structurally and functionally. It consists of a set of highly complex, interconnected processing units (neurons) linked with weighted connections, and include an input layer, an output layer and one or more hidden layers. The input layer contains neurons which receive the data available for the analysis (e.g. various clinical, demographic or laboratory data) and the output layer contains neurons which export different predictive outcomes (e.g. clinical diagnosis or prognosis). The hidden layers are used to allow complex relations between the input and output neurons to evolve.In this study, we built ANN by using a graphical neural network development tool NeuroSolution V5.05 (Neurodimension, Florida, United State).
Measured: Up to 01 months
Secondary outcomes (2)
Statistical Analysis
Statistical analysis was performed using SPSS 13.0 software and MedCalc 10.0 software. The Kolmogorov-Smirnov test was applied to determine whether sample data were likely to be derived from a normal distribution population. Continuous variables were expressed by mean ± standard deviation and compared using Wilcoxon signed rank test or Mann-Whitney U test when necessary. Categorical variables were described by proportions or count and compared using proportions Chi-square test or the Fisher's exact test when necessary.
Performances of the ANN prediction in the training cohort and in the validation cohort were tested using ROC analysis, in which AUROC was used to compare the performance of ANN and MELD-based scoring series using the Hanley and McNeil method. A value of P < 0.05 was considered statistically significant.
Measured: Up to 02 months
Laboratory Tests
Liver function tests, complete blood count and coagulation tests were performed within the first 24h after admission. The liver function tests included alanine aminotranferase, aspartate aminotranferase, total bilirubin (TBil), albumin, serum sodium, alpha-fetoprotein (AFP) and creatinine. Complete blood count was made up of platelet and hemoglobin (Hb). Coagulation tests contained prothrombin activity (PTA) and international normalized ratio (INR). Additionally, hepatitis B e antigen (HBeAg) was detected by conventional serological assays. Serum HBV DNA was measured by quantitative polymerase chain reaction(PCR) assay (Roche Amplicor, limit of detectability of 100 IU/ml) after admission.
Measured: Up to 07 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Apr 2013 (estimated)
- Study startApr 2010
- Primary completionMay 2010
- Study completionJun 2010
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Acute-on-chronic Hepatitis B Liver FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 583 participants (actual)
Sponsor and investigators
- Lead sponsor
- Wenzhou Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ming-Hua Zheng, Wenzhou Medical University
- Investigators
- Ming Hua Zheng, Medical Master, Study chair, First Affiliated Hospital of Wenzhou Medical CollegeXian Feng Lin, Medical undergraduate, Study chair, Wenzhou Medical UniversityKe Qing Shi, Medical Master, Study chair, First Affiliated Hospital of Wenzhou Medical CollegeWen Yue Liu, Medical undergraduate, Principal investigator, Wenzhou Medical UniversityChen Chen Zhao, Medical undergraduate, Principal investigator, Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT01826760
- Sponsor's protocol ID
- wenzhouMC 023
- Status last verified
- Apr 2013
- First submitted
- 30 Mar 2013
- Last updated
- 8 Apr 2013
- Results posted
- No
- Data monitoring committee
- Yes
Publications
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.
PubMed 23490369
Study at a glance
From the registry record, last updated by the sponsor on 8 Apr 2013. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Study of 3-month Mortality Risk of Acute-on-chronic Hepatitis B Liver Failure Using Artificial Neural Network
- ClinicalTrials.gov ID
- NCT01826760
- Acronym / sponsor ID
- wenzhouMC 023
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 583 participants (actual)
- Lead sponsor
- Wenzhou Medical UniversityUniversity, hospital or other
- Study start
- Apr 2010
- Primary completion
- May 2010 (actual)
- Study completion
- Jun 2010 (actual)
- First posted
- 8 Apr 2013 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| acute-on-chronic hepatitis B liver failure, training group | – | Other: Using training and testing groups to construct ANN based on laboratory testsACHBLF 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 according to consensus recommendations of the Asian Pacific Association for the Study of the Liver in 2009. ACHBLF patients were assigned to a training cohort and a validation cohort randomly. One of the major limitations of ANN is over-training, which can lead to good performance on training sets but poor performance on relatively independent validation sets. To avoid over-training during building ANN, a part of ACHBLF patients were again randomly selected from the training group to train the network and the remaining were used for cross-validation. |
| acute-on-chronic hepatitis B liver failure, testing group | – | Other: Using training and testing groups to construct ANN based on laboratory testsACHBLF 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 according to consensus recommendations of the Asian Pacific Association for the Study of the Liver in 2009. To avoid over-training during building ANN, a part of ACHBLF patients were again randomly selected from the training group to train the network and the remaining were used for cross-validation. |
Who may qualify
- Ages
- 19 to 87 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- Acute hepatic insult manifesting as jaundice and coagulopathy
- Complicated within 4 weeks by ascites
- And/or encephalopathy in a patient with chronic HBV infection
Key exclusion criteria (7 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Using training and testing groups to construct ANN based on laboratory tests | Other | acute-on-chronic hepatitis B liver failure, testing group, acute-on-chronic hepatitis B liver failure, training group |
Primary outcome
Living status
Time frame: Up to 08 months
The routine therapy of patients were same, including absolute bed rest, energy supplements and vitamins, intravenous drop infusion albumin, maintenance water, electrolyte and acid-base equilibrium, and prevention and treatment complications, etc. The start date of the follow-up was the date of the diagnosis of ACHBLF. In this study, patients receiving liver transplantation within 3 months were considered as death. All patients with ACHBLF were followed up for at least 3 months and the outcome (death or survival) of corresponding patient was recorded.
Calculating MELD-based Scoring Systems
Time frame: Up to 02 months
MELD score (R = 9.57 × ln (creatinine (mg/dL)) + 3.78×ln (bilirubin (mg/dL)) + 11.2×ln (INR) + 6.43) was used to measure the mortality risk in patients with end-stage liver disease. Given the lack of donors, MELD was used as organ allocation tool to increase graft success rate and patient survival rates, which was generally accepted. Recently, some adjustments were added to the original MELD formula to overcome limitations of MELD score. Published data suggested that MELD-Na (R = MELD + 1.59 × (135 - serum sodium (mmol/L))) might improve the prognostic accuracy [5]. Furthermore, several other scoring systems such as MELDNa (R = MELD - serum sodium (mmol/L) - (0.025 × MELD × (140 - serum sodium (mmol/L))) + 140), MESO (R = (MELD/serum sodium (mmol/L)) × 100), iMELD (R = MELD + (age(year) × 0.3) - (0.7 × serum sodium (mmol/L)) + 100)), etc had been described for predicting the mortality of end-stage liver disease accurately.
Construction of ANN
Time frame: Up to 01 months
ANN can mimic a biological neural system both structurally and functionally. It consists of a set of highly complex, interconnected processing units (neurons) linked with weighted connections, and include an input layer, an output layer and one or more hidden layers. The input layer contains neurons which receive the data available for the analysis (e.g. various clinical, demographic or laboratory data) and the output layer contains neurons which export different predictive outcomes (e.g. clinical diagnosis or prognosis). The hidden layers are used to allow complex relations between the input and output neurons to evolve.In this study, we built ANN by using a graphical neural network development tool NeuroSolution V5.05 (Neurodimension, Florida, United State).
2 secondary outcomes
Statistical Analysis
Time frame: Up to 02 months
Laboratory Tests
Time frame: Up to 07 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Apr 2013.
- Results publication: PubMed 23490369Zheng 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.
- A model to predict 3-month mortality risk of acute-on-chronic hepatitis B liver failure using artificial neural network.Link provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ming Hua Zheng, Medical Master study chair
First Affiliated Hospital of Wenzhou Medical College
Xian Feng Lin, Medical undergraduate study chair
Wenzhou Medical University
Ke Qing Shi, Medical Master study chair
First Affiliated Hospital of Wenzhou Medical College
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Acute-on-chronic Hepatitis B Liver Failure, run by Wenzhou Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 8 Apr 2013. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Living status (time frame: Up to 08 months). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT01826760, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Apr 2013; AzTrials last refreshed its copy on 6 Nov 2026.