Acute-on-chronic Hepatitis B Liver Failure Clinical Trial
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.
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.
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Observational Model: Case Control, Time Perspective: Cross-Sectional
| Status | Clinical Trial | Phase | |
|---|---|---|---|
| Not yet recruiting |
NCT06190002 -
Characteristics and Risk Factors for Invasive Fungal Infection With Acute-on-chronic Hepatitis B Liver Failure
|