Abstract
Cirrhosis has an increasing prevalence globally, and sepsis is a common life-threatening comorbidity of cirrhosis. The cirrhotic population benefits less from the diagnostic decision-making in current guidelines for sepsis. To establish a predictive model and validate its efficiency for predicting the risk of all-cause in-hospital mortality (IHM) in cirrhosis with sepsis. We extracted data of cirrhosis patients with sepsis from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV dataset was assigned at 7:3 to a training set (n = 1701) and an internal validation set (n = 729), and the eICU-CRD dataset as an external validation set (n = 352). Statistically, variables were screened by LASSO regression. We assessed the model performance by ROC, calibration, and decision curve analysis (DCA) curves. Finally, we compared the nomogram with the SAPS-II score and conducted DeLong tests. The model achieved AUCs of 0.783 (95% CI 0.761–0.804), 0.763 (95% CI 0.729–0.796), and 0.745 (95% CI 0.692–0.797) in the training, internal validation, and external validation sets, respectively. Calibration curves showed good agreement. Decision curve analysis demonstrated favorable clinical utility. The nomogram is valuable in early identifying high-risk groups, implementing targeted interventions, reducing IHM, and ameliorating prognosis.
Data availability
MIMIC-IV 3.1 (https//physionet.org/content/mimiciv/3.1) and eICU-CRD (https//physionet.org/content/eicu-crd/2.0/).
Abbreviations
- ICU:
-
Intensive care unit
- SQL:
-
Structured query language
- SOFA:
-
Sepsis-related organ failure assessment
- SAPS-II:
-
Simplified Acute Physiology Score II
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Yanyu Hu: Conceptualization, Methodology, Formal analysis , Writing- Original draft preparation. Linzhu Zhang: Writing- Reviewing and Editing, Validation. Jiangning Yin: Methodology, Writing- Reviewing and Editing, Supervision. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Data sources
MIMIC-IV 3.1 (https//physionet.org/content/mimiciv/3.1) and eICU-CRD (https//physionet.org/content/eicu-crd/2.0/).
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Hu, Y., Zhang, L. & Yin, J. Development and external validation of a predictive model for in-hospital mortality in patients with liver cirrhosis and sepsis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43991-x
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DOI: https://doi.org/10.1038/s41598-026-43991-x
