ArticleScientific reports2022
Routine laboratory biomarkers used to predict Gram-positive or Gram-negative bacteria involved in bloodstream infections.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 15 citations in OpenAlex.
- Surveillance of bloodstream infections in pediatric patients for 11 years: what was the COVID-19 impact?BMC pediatrics · 2025Article
- Clinical and microbiological characteristics of bacteremia in pediatric intensive care unit: a retrospective analytical observational study.BMC pediatrics · 2025Observational
- Leveraging innovative diagnostics as a tool to contain superbugs.Antonie van Leeuwenhoek · 2025Review
- Exploring the clinical value of procalcitonin, c-reactive protein, white blood cell count, and neutrophil-to-lymphocyte ratio in the early diagnosis of bloodstream infections in children.BMC pediatrics · 2025Article
- Epidemiology and drug resistance analysis of bloodstream infections in an intensive care unit from a children's medical center in Eastern China for six consecutive years.International microbiology : the official journal of the Spanish Society for Microbiology · 2024Article
- Procalcitonin and C-reactive protein as diagnostic biomarkers in COVID-19 and Non-COVID-19 sepsis patients: a comparative study.BMC infectious diseases · 2024Article
- Identifying key blood markers for bacteremia in elderly patients: insights into bacterial pathogens.Frontiers in cellular and infection microbiology · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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). A total of 13,574 BC of 6787 patients (217 BSI-GP and 238 BSI-GN) and 68 different RLB from these were analyzed. The logistic regression model was built considering BSI-GP or BSI-GN as response variable and RLB as covariates. 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). In the RM, only platelets, creatinine, mean corpuscular hemoglobin and erythrocytes were used. The reproductivity of both models were applied to a test bank of 2019. 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. These data confirm the discriminatory capacity of the new models for BSI-GN (p = 0.64). 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.
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