Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jul 16, 2023; 11(20): 4833-4842
Published online Jul 16, 2023. doi: 10.12998/wjcc.v11.i20.4833
Risk prediction model for distinguishing Gram-positive from Gram-negative bacteremia based on age and cytokine levels: A retrospective study
Wen Zhang, Tao Chen, Hua-Jun Chen, Ni Chen, Zhou-Xiong Xing, Xiao-Yun Fu
Wen Zhang, Tao Chen, Hua-Jun Chen, Ni Chen, Zhou-Xiong Xing, Xiao-Yun Fu, Department of Critical Care Medicine, The Affiliated Hospital of Zunyi Medical University, Zunyi 563000, Guizhou Province, China
Author contributions: Zhang W, Chen T, Chen HJ, Chen N, Xing ZX and Fu XY conceived and designed the study; Chen T, Xing ZX and Fu XY guided the study; Zhang W, Chen T, Chen HJ and Chen N collected the clinical date; Zhang W and Xing ZX analyzed the data; All authors drafted and revised the manuscript.
Supported by Guizhou Provincial Health Commission Science and Technology Department, No. GZWKJ2023-009; Guizhou Science and Technology Department, No. QIANKEHEZHICHEN[2022]YIBAN179; and Guizhou Science and Technology Department, No. QIANKEHEZHICHEN[2022]YIBAN087.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of Affiliated Hospital of Zunyi Medical University, Approval No. KLL-2023-020.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xiao-Yun Fu, PhD, Chief Doctor, Chief Physician, Department of Critical Care Medicine, The Affiliated Hospital of Zunyi Medical University, No. 149 Dalian Road, Huichuan District, Zunyi 563000, Guizhou Province, China. 422318085@qq.com
Received: April 1, 2023
Peer-review started: April 1, 2023
First decision: May 8, 2023
Revised: May 13, 2023
Accepted: June 12, 2023
Article in press: June 12, 2023
Published online: July 16, 2023
Processing time: 102 Days and 6.8 Hours
Abstract
BACKGROUND

Severe infection often results in bacteremia, which significantly increases mortality rate. Different therapeutic strategies are employed depending on whether the blood-borne infection is Gram-negative (G-) or Gram-positive (G+). However, there is no risk prediction model for assessing whether bacteremia patients are infected with G- or G+ pathogens.

AIM

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

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.

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 curve analysis, age and IL-6 yielded an area under the curve of 0.761 and distinguished G+ from G- infection with specificity of 0.756 and sensitivity of 0.692. Serum IL-6 and IL-10 levels were upregulated by more than 10-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.

CONCLUSION

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

Keywords: Interleukin 6; Cytokine; Bacteremia; Infection; Prediction model

Core Tip: This study was designed to assess whether the cytokine profile and other clinical variables can distinguish Gram-positive from Gram-negative bacteremia. A reliable predicted model could prove valuable for facilitating early identification of the causative pathogen and the rational use of antibiotics, thereby preventing progression into potentially fatal septic shock.