Few studies have investigated the diagnostic utilities of biomarkers for predicting bacteremia among septic patients admitted to intensive care units (ICU). Therefore, this study evaluated the prediction power of laboratory biomarkers to utilize those markers with high performance to optimize the predictive model for bacteremia. This retrospective cross-sectional study was conducted at the ICU department of Gyeongsang National University Changwon Hospital in 2019. Adult patients qualifying SEPSIS-3 (increase in sequential organ failure score greater than or equal to 2) criteria with at least two sets of blood culture were selected. Collected data was initially analyzed independently to identify the significant predictors, which was then used to build the multivariable logistic regression (MLR) model. A total of 218 patients with 48 cases of true bacteremia were analyzed in this research. Both CRP and PCT showed a substantial area under the curve (AUC) value for discriminating bacteremia among septic patients (0.757 and 0.845, respectively). To further enhance the predictive accuracy, we combined PCT, bilirubin, neutrophil lymphocyte ratio (NLR), platelets, lactic acid, erythrocyte sedimentation rate (ESR), and Glasgow Coma Scale (GCS) score to build the predictive model with an AUC of 0.907 (95% CI, 0.843 to 0.956). In addition, a high association between bacteremia and mortality rate was discovered through the survival analysis (0.004). While PCT is certainly a useful index for distinguishing patients with and without bacteremia by itself, our MLR model indicates that the accuracy of bacteremia prediction substantially improves by the combined use of PCT, bilirubin, NLR, platelets, lactic acid, ESR, and GCS score.

翻译：既往鲜有研究探讨生物标志物在预测ICU入院脓毒症患者菌血症中的诊断价值。本研究旨在评估实验室生物标志物的预测效能，筛选高性能指标以优化菌血症预测模型。这项回顾性横断面研究于2019年在庆尚国立大学昌原医院ICU开展，纳入符合SEPSIS-3标准（序贯器官衰竭评分增加≥2分）且至少完成两次血培养的成年患者。通过单因素独立分析筛选显著预测因子后，构建多变量逻辑回归（MLR）模型。研究共纳入218例患者（其中48例确诊菌血症），结果显示：CRP与PCT在鉴别脓毒症患者是否伴发菌血症时均具有较高曲线下面积（AUC值分别为0.757和0.845）。为进一步提升预测精度，我们联合PCT、胆红素、中性粒细胞淋巴细胞比值（NLR）、血小板、乳酸、红细胞沉降率（ESR）及格拉斯哥昏迷评分（GCS）构建预测模型，其AUC达0.907（95% CI: 0.843-0.956）。此外，生存分析发现菌血症与死亡率存在显著关联（P=0.004）。虽然单独使用PCT即可有效区分菌血症患者，但本研究的MLR模型表明，联合应用PCT、胆红素、NLR、血小板、乳酸、ESR和GCS评分可显著提升菌血症预测准确性。