# Development of a short-term prognostic model for hepatitis B-related acute-on-chronic liver failure in a dual-center cohort

## TL;DR

This study developed a new model to predict short-term survival in patients with hepatitis B-related liver failure, showing better performance than existing tools.

## Contribution

A novel nomogram model (ICI) was developed and validated for predicting 28-day mortality in HBV-related ACLF patients.

## Key findings

- The ICI model demonstrated good discriminative ability with AUCs of 0.826 and 0.814 in training and validation cohorts.
- The ICI model showed better calibration and clinical benefit compared to traditional scoring systems.
- ln(INR), ln(Cr), and ln(IL-6) were identified as independent risk factors for 28-day mortality.

## Abstract

Acute-on-chronic liver failure (ACLF) is a severe clinical syndrome marked by rapid progression and high short-term mortality. In the Asia-Pacific region, where the hepatitis B virus (HBV) is endemic, HBV-related ACLF (HBV-ACLF) represents the most common subtype. The aim of this study was to develop and validate a concise and accurate nomogram for predicting 28-day mortality in patients with HBV-related ACLF.

A total of 159 patients with HBV-ACLF were enrolled, including 113 in the training cohort and 46 in the validation cohort. Clinical characteristics, routine laboratory parameters, and inflammatory cytokine levels were collected. In the training cohort, least absolute shrinkage and selection operator (LASSO) regression was applied to select variables, followed by multivariate logistic regression to construct a nomogram model (ICI). Model performance was evaluated in both cohorts using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA), and compared with COSSH-ACLF II, CLIF-C ACLF, AARC, CLIF-OFs, MELD, and CTP scores.

LASSO regression identified ln(INR), ln(Cr), ln(WBC), and ln(IL-6) as candidate predictors. Multivariate logistic regression further confirmed that ln(INR), ln(Cr), and ln(IL-6) were independent risk factors. The new model (ICI) demonstrated good discriminative ability with an area under the receiver operating characteristic curve (AUC) of 0.826 in the training cohort and 0.814 in the validation cohort. Calibration analysis showed excellent consistency, and decision curve analysis (DCA) indicated that the ICI model provided higher net clinical benefit across different threshold probabilities. Furthermore, compared with traditional models, the ICI model exhibited significant advantages in terms of discriminative ability and clinical benefit.

The newly developed model (ICI) showed superior predictive performance for short-term prognosis in patients with HBV-ACLF, outperforming conventional scoring systems. It is anticipated that the ICI model will serve as a valuable instrument in complementing the conventional scoring system for the HBV-ACLF population in the future.