What question did this study set out to answer?

To create and validate a predictive model for all-cause in-hospital mortality in cirrhotic patients with sepsis.

April 4, 2026Open Access

Development and external validation of a predictive model for in-hospital mortality in patients with liver cirrhosis and sepsis

Key Points

To create and validate a predictive model for all-cause in-hospital mortality in cirrhotic patients with sepsis.
Extracted data from MIMIC-IV and eICU-CRD databases.
Divided MIMIC-IV data into training and internal validation sets.
Screened variables using LASSO regression.
Evaluated model performance using ROC and calibration curves.
Compared the nomogram with SAPS-II score.
Achieved AUC of 0.783 in the training set.
Achieved AUC of 0.763 in the internal validation set.
Achieved AUC of 0.745 in the external validation set.
Calibration showed good agreement.
Decision curve analysis indicated favorable clinical utility.

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.

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