aCCI-HBV-ACLF: A Novel Predictive Model for Hepatitis B Virus-Related Acute-On-Chronic Liver Failure

Area of Science:

• Hepatology
• Clinical Prognostics
• Biostatistics

Background:

• Early identification of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is critical for patient management and survival.
• Existing prognostic models for HBV-ACLF often neglect the patient's underlying clinical conditions, limiting their predictive accuracy.

Purpose of the Study:

• To develop a precise prognostic model for transplant-free mortality in HBV-ACLF patients.
• To incorporate the age-adjusted Charlson comorbidity index (aCCI) into a predictive model for HBV-ACLF.

Main Methods:

• A cohort of 906 patients with HBV-ACLF was divided into training (70%) and temporal validation (30%) cohorts.
• Univariate, logistic regression, and LASSO regression analyses were employed to construct the prognostic model.
• The model was further validated in a temporal and an external validation cohort.

Main Results:

• Total bilirubin, neutrophils, international normalized ratio, and aCCI were significantly associated with 28-day transplant-free mortality.
• The developed aCCI-HBV-ACLF model demonstrated strong predictive performance (AUC 0.859 for 28-day mortality in the training cohort).
• The model maintained high predictive accuracy in temporal (AUC 0.869) and external (AUC 0.868) validation cohorts for 28-day mortality.

Conclusions:

• A novel prognostic model, aCCI-HBV-ACLF, was developed for predicting transplant-free mortality in HBV-ACLF.
• The aCCI-HBV-ACLF model shows excellent predictive ability for 28- and 90-day transplant-free mortality.
• This model offers improved precision for clinical decision-making in HBV-ACLF management.