Early prediction of invasive fungal infection risk in acute-on-chronic liver failure: a prediction model based on admission indicators.

ABSTRACT:

Background

Acute-on-chronic liver failure (ACLF) is a severe clinical syndrome, and the incidence of invasive fungal infection (IFI) among hospitalized patients with ACLF is steadily increasing. The aim of this study is to develop a diagnostic nomogram to assist in the identification of IFI in these patients.

Methods

A retrospective study included 705 patients from January 1, 2019, to October 31, 2023, randomly divided into training (n = 493) and validation (n = 212) cohorts. The diagnosis of IFI includes proven diagnosis and probable diagnosis. Kaplan analysis was performed to analyze the survival prognosis of ACLF patients with and without IFI. A nomogram was developed based on a logistic regression model derived through least absolute shrinkage and selection operator (LAS

SUBMITTER: Yang X 

PROVIDER: S-EPMC11900632 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature