ArticleBMC microbiology2025
Early prediction of invasive fungal infection risk in acute-on-chronic liver failure: a prediction model based on admission indicators.
Article in BMC microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Epidemiology, Risk Factors, and Routine Blood Biomarkers of Aspergillosis in Cancer Patients in Jiangxi, China.Infection and drug resistance · 2026Article
- The role of bacterial outer membrane vesicles in inflammatory response of acute-on-chronic liver failure.Frontiers in microbiology · 2025Review
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Authors and funding
10 authors.
Funding
Abstract
backgroundAcute-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.
methodsA 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 (LASSO) regression. The discrimination, accuracy, and clinical utility of the model were assessed using receiver operating characteristic curves, Hosmer-Lemeshow tests, calibration plots, and decision curve analysis.
resultsKaplan-Meier survival analysis confirmed that the median survival time of ACLF patients with IFI was significantly lower (by 68 days) than that of ACLF patients without IFI, and there were significant differences in the 90-day, 180-day, and 360-day survival rates between the two groups (P < 0.05). Based on LASSO regression, the following factors were identified as significant risk factors for predicting IFI: aminotransferase levels, prothrombin activity, hemoglobin, neutrophil-to-lymphocyte ratio, and serum total bilirubin. A nomogram was constructed incorporating these variables. The nomogram demonstrated good discriminative ability, with an area under the receiver operating characteristic curve (AUC) of 0.78 (95% confidence interval [CI]: 0.72-0.84) in the training cohort and 0.79 (95% CI: 0.70-0.87) in the validation cohort. Decision curve analysis further validated the clinical applicability of the nomogram.
conclusionACLF patients with IFI have lower survival time than those without IFI. A nomogram was developed and validated to assist clinicians in the early prediction of IFI in hospitalized patients with ACLF. CLINICAL TRIAL NUMBER: Not applicable.
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