![]() ![]() The models (CM and RM) were checked against a new-database that was not used to adjust the models. Using Boruta, the variables selected were iCa, creatinine, RBC, age and PLT, resulting in a model with a 0.67 AUC. The covariates selected by RFE were: iCa, creatinine, MCH, RBC, lactate, methemoglobin and PLT, establishing a model with a 0.71 AUC. Other methods, such as the backward-selection and stepwise-selection, presented the same variables as the RM to predict BSI-GN. ![]() The present study aimed to develop a model that predicts whether a bloodstream infection is caused by a GN or a GP organism using routine laboratory biomarkers (RLB). pointed out that specific combinations of hematological parameters can prove its power to distinguish patients with BSI caused by different pathogens, including GP and GN bacteria 14.Ĭonsidering the great importance of the topic and the complex and controversial results, new studies and research need to be reported to better understand the role of routine biomarkers used in laboratories in predicting the bacterial group involved in BSI. demonstrated that some biomarkers, when associated, had the potential to predict the bacterial group involved in BSI 12, 13. evaluated a large number of biomarkers with statistical analysis and did not obtain a satisfactory result 11. However, few studies have been conducted to search for other biomarkers that may be related to the bacterial group involved in BSI. In order to predict bacteremia, mortality and sepsis, several authors have proposed the use of laboratory biomarkers 1, 5, 6, 8, 9, 10. Thus, faster and more practical alternatives that can predict the bacterial group, whether Gram-positive (GP) or Gram-negative (GN), responsible for BSI, could be extremely important to target antibacterial therapy. Although the confirmation of BSI only occurs definitively with positive blood culture (BC), the complete identification and antimicrobial process of susceptibility testing of the etiologic agent takes time (48–72 h), which delays the correct choice of treatment 5, 6, 7. AUC of 0.69 using only 4 RLB, associated with the patient's clinical data could be useful for better targeted antimicrobial therapy in BSI.Ĭorrect and immediate antimicrobial treatment significantly reduces the mortality of patients with bloodstream infections (BSI), which affects about 30 million people, and are among the main causes of morbidity, causing approximately 6 million deaths per year worldwide 1, 2, 3, 4. These data confirm the discriminatory capacity of the new models for BSI-GN (p = 0.64). The new model presented values to predict BSI-GN of the area under the curve (AUC) of 0.72 and 0.69 for CM and RM, respectively with sensitivity of 0.62 and 0.61 (CM and RM) and specificity of 0.67 for both. The reproductivity of both models were applied to a test bank of 2019. In the RM, only platelets, creatinine, mean corpuscular hemoglobin and erythrocytes were used. After four filters applied total of 320 patients and 16 RLB remained in the Complete-Model-CM, and 4 RLB in the Reduced-Model-RM (RLB p > 0.05 excluded). The logistic regression model was built considering BSI-GP or BSI-GN as response variable and RLB as covariates. A total of 13,574 BC of 6787 patients (217 BSI-GP and 238 BSI-GN) and 68 different RLB from these were analyzed. This study evaluated routine laboratory biomarkers (RLB) to predict the infectious bacterial group, Gram-positive (GP) or Gram-negative (GN) associated with bloodstream infection (BSI) before the result of blood culture (BC). ![]()
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