Ignite Creation Date:
2025-12-25 @ 2:10 AM
Ignite Modification Date:
2025-12-27 @ 11:20 PM
Study NCT ID:
NCT01826760
Status:
COMPLETED
Last Update Posted:
2013-04-08
First Post:
2013-03-30
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Study of the Model to Predict 3-month Mortality Risk of Acute-on-chronic Hepatitis B Liver Failure
Sponsor:
Wenzhou Medical University
Organization:
Study Overview
Official Title:
Study of 3-month Mortality Risk of Acute-on-chronic Hepatitis B Liver Failure Using Artificial Neural Network
Status:
COMPLETED
Status Verified Date:
2013-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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.
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 Oversight
Has Oversight DMC:
True
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: