Machine Learning Prediction of 90-Day Mortality in HBV-Related ACLF Using Olink-Derived Inflammatory Protein Signatures.

ABSTRACT:

Background

Hepatitis B virus-induced acute-on-chronic liver failure (HBV-ACLF) has extremely high mortality rates and is closely linked to inflammatory responses. Early interventions in high-risk patients can significantly improve survival rates.

Methods

In this prospective cohort study, we used the Olink proximity extension assay to compare 96 inflammation-related proteins in survivors and non-survivors of ACLF. Functional enrichment analysis of differentially expressed proteins (DEPs) was performed using KEGG to analyse their biological characteristics and correlation with disease severity. The key proteins identified in the discovery cohort (n = 32) were quantified by enzyme-linked immunosorbent assay in the modelling cohort (n = 100) to develop a machine-learning model,

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12628111 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature