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Updated: Jan 13, 2026
Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis
Published on: December 9, 2022
Development and validation of a prediction model for in-hospital mortality in patients with intra-abdominal sepsis: a
Jianjun Zhang1,2, Yuhong Chen1, Cong-Cong Zhao1
1Department of Intensive Care Unit, Hebei Medical University Fourth Aliated Hospital and Hebei Provincial Tumor Hospital, Shijiazhuang, China.
Objectives:
To develop and validate a predictive model for assessing in-hospital mortality in patients with intra-abdominal sepsis (IAS), a leading cause of sepsis.
Design:
Secondary analysis of two retrospective critical care databases.
Setting:
Data extracted from the Intensive Care Medicine Information Marketplace IV (MIMIC-IV) and the eICU Collaborative Research Database.
Participants:
Patients with IAS from MIMIC-IV (2008-2019; 1300 patients, 264 deaths) for model training and internal validation, and eICU (2014-2015; 149 patients, 33 deaths) for external validation.
Interventions:
Clinical data were used for constructing a predictive model. Variable selection was performed using least absolute shrinkage and selection operator regression, followed by model development with multivariable logistic regression. The model was visualised as a nomogram.
Primary And Secondary Outcome Measures:
The primary outcome was in-hospital mortality. Secondary outcomes were model performance metrics, including the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis and clinical impact curves.
Results:
Six predictors (lactate, age, activated partial thromboplastin time, blood urea nitrogen, total bilirubin and platelets) were identified. The predictive model showed good performance with an AUC of 0.795 (95% CI 0.758 to 0.831) in the training set (n=910) and 0.846 (95% CI 0.772 to 0.919) in the external validation set (n=149).
Conclusion:
A robust predictive model was developed to estimate the risk of in-hospital mortality in patients with IAS. This tool may assist clinicians in enhancing patient management and decision-making.
