Stratifying and predicting progression to acute liver failure during the early phase of acute liver injury.

ABSTRACT: Acute liver failure (ALF) is a serious disease that progresses from acute liver injury (ALI) and that often leads to multiorgan failure and ultimately death. Currently, effective treatment strategies for ALF, aside from transplantation, remain elusive, partly because ALI is highly heterogeneous. Furthermore, clinicians lack a quantitative indicator that they can use to predict which patients hospitalized with ALI will progress to ALF and the need for liver transplantation. In our study, we retrospectively analyzed data from 319 patients admitted to the hospital with ALI. By applying a machine-learning approach and by using the SHapley Additive exPlanations (SHAP) algorithm to analyze time-course blood test data, we identified prothrombin time activity percentage (PT%) as a biomarker reflec

SUBMITTER: Yoshimura R 

PROVIDER: S-EPMC11801268 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature