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

作　　者：Wen Zhang Tao Chen Hua-Jun Chen Ni Chen Zhou-Xiong Xing Xiao-Yun Fu 

机构地区：[1]Department of Critical Care Medicine,The Affiliated Hospital of Zunyi Medical University,Zunyi 563000,Guizhou Province,China

出　　处：《World Journal of Clinical Cases》2023年第20期4833-4842,共10页世界临床病例杂志

基　　金：Supported by Guizhou Provincial Health Commission Science and Technology Department,No.GZWKJ2023-009;Guizhou Science and Technology Department,No.QIANKEHEZHICHEN[2022]YIBAN179;Guizhou Science and Technology Department,No.QIANKEHEZHICHEN[2022]YIBAN087.

摘　　要：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 Ggroups.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

关 键 词：Interleukin 6 CYTOKINE BACTEREMIA INFECTION Prediction model 

分 类 号：R515.3[医药卫生—内科学]