Prediction Model Based on Computed Tomography Liver Volume for the Short-term Mortality in Hepatitis B Related Acute-on-Chronic

Hepatitis B, Chronic Clinical Trial

Official title: Prediction Model Based on Computed Tomography Liver Volume for Predicting Short-term Mortality in Hepatitis B-Acute-on-Chronic Liver Failure

Status Completed

Clinical Trial Summary

HBV-related acute-on-chronic liver failure (HBV-ACLF) deteriorates rapidly with a high short-term mortality. Early identification and accurate prognostic prediction was critical to improve survival rate. This study was sought to determine the liver volumetry as predictor for short-term mortality in HBV-ACLF and develop a simpler prognostic model based on liver morphology. Liver volumetry were determined from CT at admission. Univariate and multivariate logistic regression were used to identify the optimum prognostic indicators and develop prognostic model. Additionally, receiver operating characteristic curves were analyzed to evaluate the predictive ability of the model.

Clinical Trial Description

HBV-ACLF deteriorates rapidly with a high short-term mortality and early identification and accurate prognostic prediction may be the key to make clinical decision and improve survival rate. Liver volume, non-invasively reflects the balance of structural collapse with hepatic regeneration, predicting the prognosis of liver diseases. However, there is no idea whether the liver volume is an indicator to predict the mortality of HBV-ACLF patients. The present study evaluated the difference of liver volume between 28-day survival group and non-survivals and developed a new prognostic model based on liver volume . This study highlighted the significance of liver morphology in predicting the outcome of HBV-ACLF for the first time. ;

Related Conditions & MeSH terms

• Acute-on-Chronic Liver Failure
• End Stage Liver Disease
• Hepatic Insufficiency
• Hepatitis
• Hepatitis A
• Hepatitis B
• Hepatitis B, Chronic
• Liver Failure

• NCT number: NCT03977857
• Study type: Observational
• Source: First Affiliated Hospital Xi'an Jiaotong University
• Status: Completed
• Start date: March 1, 2019
• Completion date: December 31, 2019