Comparison of Two Predictive Models of Sepsis in Critically Ill Patients Based on the Combined Use of Inflammatory Markers.

BACKGROUND

Sepsis is a systemic inflammatory response due to infection, resulting in organ dysfunction. Timely targeted interventions can improve prognosis. Inflammation plays a crucial role in the process of sepsis. To identify potential sepsis early, we developed and validated a nomogram model and a simple risk scoring model for predicting sepsis in critically ill patients.

METHODS

The medical records of adult patients admitted to our intensive care unit (ICU) from August 2017 to December 2020 were analyzed. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%). A nomogram model was developed through multivariate logistic regression analysis. The continuous variables included in nomogram model were transformed into dichotomous variables. Then, a multivariable logistic regression analysis was performed based on these dichotomous variables, and the odds ratio (OR) for each variable was used to construct a simple risk scoring model. The receiver operating characteristic curves (ROC) were constructed, and the area under the curve (AUC) was calculated.

RESULTS

A total of 2074 patients were enrolled. Finally, white blood cell (WBC), C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT) and neutrophil-to-lymphocyte ratio (NLR) were included in our models. The AUC of the nomogram model and the simple risk scoring model were 0.854 and 0.842, respectively. The prediction performance of the two models on sepsis is comparable (p = 0.1298).

CONCLUSION

This study combining five commonly available inflammatory markers (WBC, CRP, IL-6, PCT and NLR) developed a nomogram model and a simple risk scoring model to predict sepsis in critically ill patients. Although the prediction performance of the two models is comparable, the simple risk scoring model may be simpler and more practical for clinicians to identify potential sepsis in critically ill patients at an early stage and strategize treatments.