Risk prediction model for distinguishing Gram-positive from Gram-negative bacteremia based on age and cytokine levels: A retrospective study

ARTICLE HIGHLIGHTS

Research background

Severe infection often results in bacteremia, which significantly increases mortality rates. Different therapeutic strategies are employed depending on whether the blood-borne infection is Gram-negative (G^-) or Gram-positive (G⁺).

Research motivation

There is no risk prediction model for assessing whether bacteremia patients are infected with G^- or G⁺ pathogens.

Research objectives

To establish a clinical prediction model to distinguish G^- from G⁺ infection.

Research methods

A total of 130 patients with positive blood culture admitted to a single intensive care unit were recruited, and Th1 and Th2 cytokine concentrations, routine blood test results, procalcitonin and C-reactive protein concentrations, liver and kidney function test results and coagulation function were compared between G⁺ and G^- groups. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed to optimize the selection of predictive variables by running cyclic coordinate descent and K-fold cross-validation (K = 10). The predictive variables selected by LASSO regression analysis were then included in multivariate logistic regression analysis to establish a prediction model. A nomogram was also constructed based on the prediction model. Calibration chart, receiver operating characteristic curve and decision curve analysis were adopted for validating the prediction model.

Research results

Age, plasma interleukin 6 (IL-6) concentration and plasma aspartate aminotransferase concentration were identified from 57 measured variables as potential factors distinguishing G⁺ from G^- infection by LASSO regression analysis. Inclusion of these three variables in a multivariate logistic regression model identified age and IL-6 as significant predictors. In receiver operating characteristic analysis, age and IL-6 yielded an area under the curve of 0.761, and distinguished G⁺ from G^- infection with a specificity of 0.756 and a sensitivity of 0.692. Serum IL-6 and IL-10 levels were upregulated by more than ten-fold from baseline in the G^- bacteremia group but by less than ten-fold in the G⁺ bacteremia group. The calibration curve of the model and Hosmer-Lemeshow test indicated good model fit (P > 0.05). When the decision curve analysis curve indicated a risk threshold probability between 0% and 68%, a nomogram could be applied in clinical settings.

Research conclusions

A simple prediction model distinguishing G^- from G⁺ bacteremia can be constructed based on reciprocal association with age and IL-6 level.

Research perspectives

Through the method of predicting pathogens, we can know that clinical preemptive treatment and relatively accurate use of antibiotics are beneficial to improve clinical outcomes. Through the method of predicting pathogens, we can know that clinical preemptive treatment and relatively accurate use of antibiotics are beneficial to improve clinical outcomes.