Zhang Wen, Chen Tao, Chen Hua-Jun, Chen Ni, Xing Zhou-Xiong, Fu Xiao-Yun
Department of Critical Care Medicine, The Affiliated Hospital of Zunyi Medical University, Zunyi 563000, Guizhou Province, China.
World J Clin Cases. 2023 Jul 16;11(20):4833-4842. doi: 10.12998/wjcc.v11.i20.4833.
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.
To establish a clinical prediction model to distinguish G from G infection.
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.
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 ( > 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.
A simple prediction model distinguishing G from G bacteremia can be constructed based on reciprocal association with age and IL-6 level.
严重感染常导致菌血症，这显著增加了死亡率。根据血行感染是革兰阴性（G）还是革兰阳性（G），采用不同的治疗策略。然而，目前尚无用于评估菌血症患者是否感染G或G病原体的风险预测模型。
建立一种临床预测模型以区分G感染和G感染。
招募了入住单一重症监护病房的130例血培养阳性患者，比较G组和G组之间的Th1和Th2细胞因子浓度、血常规检查结果、降钙素原和C反应蛋白浓度、肝肾功能检查结果及凝血功能。采用最小绝对收缩和选择算子（LASSO）回归分析，通过运行循环坐标下降法和K折交叉验证（K = 10）来优化预测变量的选择。然后将LASSO回归分析选择的预测变量纳入多因素逻辑回归分析以建立预测模型。还基于该预测模型构建了列线图。采用校准图、受试者工作特征曲线和决策曲线分析来验证该预测模型。
通过LASSO回归分析从57个测量变量中确定年龄、血浆白细胞介素6（IL-6）浓度和血浆天冬氨酸氨基转移酶浓度为区分G感染和G感染的潜在因素。将这三个变量纳入多因素逻辑回归模型后，确定年龄和IL-6为显著预测因子。在受试者工作特征曲线分析中，年龄和IL-6的曲线下面积为0.761，区分G感染和G感染的特异性为0.756，敏感性为0.692。G菌血症组血清IL-6和IL-10水平较基线上调超过10倍，而G菌血症组上调不到10倍。模型的校准曲线和Hosmer-Lemeshow检验表明模型拟合良好（>0.05）。当决策曲线分析曲线表明风险阈值概率在0%至68%之间时，列线图可应用于临床。
基于年龄和IL-6水平的相互关联可构建一种简单的区分G菌血症和G菌血症的预测模型。
