Background: Sepsis is considered to be a systemic inflammatory response due to infection, resulting in organ dysfunction. Timely targeted interventions can reduce mortality and improve prognosis. Therefore, it is important to identify potential sepsis in time. Inflammation plays a crucial role in the process of sepsis. We combined inflammatory markers to develop and validate a nomogram model and a simple risk scoring model for predicting sepsis in critically ill patients. Furthermore, comparing the prediction performance of the two models.
Methods: The medical records of adult patients admitted to our intensive care unit (ICU) from August 2017 to December 2020 were analyzed. The finally included patients were randomly divided into training cohort (70%) and validation cohort (30%). A nomogram model for sepsis was developed through multivariate logistic regression analysis in the training cohort. 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 for predicting sepsis. The receiver operating characteristic curves (ROC) were constructed and the area under the curve (AUC) was calculated to evaluate the discrimination performance of the two models.
Results: According to our inclusion and exclusion criteria, 2074 patients were included in study. 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 was 0.854 (95%CI: 0.835-0.872). The AUC of the simple risk scoring model was 0.842 (95%CI: 0.822-0.861). When the cut-off value was 7.5 points, the sensitivity was 77.03% and the specificity was 75.75%. The prediction performance of the two models on sepsis is comparable (p=0.1298) and better than that of Sequential Organ Failure Assessment (SOFA) scores (AUC=0.759).
Conclusions: 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 make treatment strategies.