Development and Validation of a Nomogram Prediction Model for Sepsis-Induced Coagulopathy: A Multicenter...

Area of Science:

• Critical Care Medicine
• Hematology
• Medical Informatics

Background:

• Sepsis-induced coagulopathy (SIC) is a severe complication associated with increased mortality in sepsis patients.
• Early identification of patients at risk for SIC is crucial for timely intervention and improved outcomes.

Purpose of the Study:

• To develop and validate a predictive model for sepsis-induced coagulopathy (SIC) in sepsis patients.
• To identify key clinical variables associated with the development of pre-SIC.

Main Methods:

• Retrospective study of 309 sepsis patients from Intensive Care Units.
• LASSO and logistic regression analysis to identify predictors of pre-SIC.
• Development and validation of a nomogram prediction model using R software, evaluated with ROC curves, calibration curves, and DCA.

Main Results:

• The pre-SIC group exhibited significantly higher mortality (44.8%) and disseminated intravascular coagulation (DIC) incidence (56.3%) compared to the non-SIC group.
• Lactate, coagulation index, creatinine, and SIC scores were identified as significant predictors of pre-SIC.
• The nomogram model showed good predictive performance with an AUC of 0.766 (development) and 0.776 (validation).

Conclusions:

• SIC is a significant risk factor for mortality in sepsis.
• The developed nomogram model offers a reliable tool for early identification of patients at risk for SIC.
• Early SIC detection through this model may enhance sepsis management strategies and patient survival rates.