ArticleFrontiers in medicine2025
An individualized nomogram for predicting risk of sepsis in patients with pyogenic liver abscesses: a 10 years retrospective analysis.
Article in Frontiers in medicine, 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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Who cites it
2 citing papers in PubMed.
- Risk Factors and Predictive Value of Sepsis in Patients with Liver Abscess.Infection and drug resistance · 2026Article
- Clinical Predictors for Early Aspiration in Patients With Liver Abscesses: Insights From a Combined-Risk Model.Cureus · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
Introduction: The incidence of pyogenic liver abscess (PLA) has been increasing. A poor prognosis and high mortality rate are observed when PLA progresses to sepsis. Thus, it is crucial to identify patients at high risk of sepsis early and develop personalized treatment plans to reduce the disease burden of patients with liver abscesses. However, a substantial research gap exists in the prediction of sepsis in patients with PLA. Methods: A retrospective study involving 490 patients with PLA was conducted. In chronological order, patients treated from August 2014 to September 2021 were employed as the training cohort ( Results: A total of 108 (22%) patients with PLA were complicated with sepsis. In patients with liver abscesses, independent risk factors for sepsis, including white blood cell count, international normalized ratio (INR), presence of gas, and sequential (sepsis-related) organ failure assessment (SOFA) score, were identified through multivariate logistic regression analysis. For the training and validation cohorts, the area under the curve (AUC) values of the nomogram were 0.880 (95% CI: 0.832-0.929) and 0.901 (95% CI: 0.839-0.964), respectively, showed that the newly established nomogram exhibited superior predictive performance and clinical utility. The Hosmer-Lemeshow test (χ Conclusion: A model with four clinical features was developed to predict the risk of sepsis in patients with liver abscesses. The model exhibited good predictive ability during time verification.
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