ArticleWorld journal of gastroenterology2025
Dynamic nomogram predicts sepsis risk in patients with acute liver failure: Analysis of intensive care database with external validation.
Article in World journal of gastroenterology, 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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2 citing papers in PubMed.
- Development of a mortality prediction nomogram for dementia patients using the MIMIC-IV database.Scientific reports · 2026Article
- Clinical implications of a dynamic nomogram for predicting sepsis in acute liver failure.World journal of gastroenterology · 2026Article
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7 authors.
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Abstract
backgroundAcute liver failure (ALF) with sepsis is associated with rapid disease progression and high mortality. Therefore, early detection of high-risk sepsis subgroups in patients with ALF is crucial.
aimTo develop and validate an accurate nomogram model for predicting the risk of sepsis in patients with ALF.
methodsWe retrieved data from the Medical Information Mart for Intensive Care (MIMIC) IV database and the Fifth Medical Center of Chinese PLA General Hospital (FMCPH). Univariate and multivariate logistic regression analysis were used to identify risk factors for sepsis in ALF and were subsequently incorporated to construct a nomogram model [sepsis in ALF (SIALF)]. The discrimination ability, calibration, and clinical applicability of the SIALF model were evaluated by the area under receiver operating characteristic curve, calibration curves, and decision curve analysis, respectively. The Kaplan-Meier curves were used for robustness check. The SIALF model was internally validated using the bootstrapping method with the MIMIC validation cohort and externally validated by the FMCPH cohort.
resultsA total of 738 patients with ALF patients were included in this study, with 510 from the MIMIC IV database and 228 from the FMCPH cohort. In the MIMIC IV cohort, 387 (75.89%) patients developed sepsis. Multivariate logistic regression analysis revealed that age [odds ratio (OR) = 1.016, 95% confidence interval (CI): 1.003-1.028,
conclusionBased on easily identifiable clinical data, we developed the SIALF model to predict the risk of sepsis in patients with ALF. The model demonstrated robust predictive efficiency, outperformed Sequential Organ Failure Assessment and systemic inflammatory response syndrome scores, and was validated in an external cohort. The model-based risk stratification and online calculator might further facilitate the early detection and appropriate treatment for this subpopulation.
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