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.

评估了实验室生物标志物的预测能力，构建了研究了血流感染的预测模型，发现血流感染与死亡率高度相关。