ArticleJournal of translational medicine2022
Development of a risk prediction model for bloodstream infection in patients with fever of unknown origin.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Utility of shaking chills as a diagnostic sign for bacteremia in adults: a systematic review and meta-analysis.BMC medicine · 2024Pooled it
- A Risk Model Incorporating the Novel Inflammatory Biomarker CD64 for Predicting Bloodstream Infection in Suspected Cases.Antibiotics (Basel, Switzerland) · 2026Article
- Comparison of talaromycosis in HIV-infected patients with and without Talaromyces marneffei bloodstream infection.BMC infectious diseases · 2026Article
- Clinical and laboratory indicators predicting coagulase-negative staphylococci as a cause of bloodstream infection among children below five years of age admitted at a tertiary hospital in Dar es Salaam, Tanzania.Annals of medicine · 2025Article
- Prediction of bloodstream infection using machine learning based primarily on biochemical data.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBloodstream infection (BSI) is a significant cause of mortality among patients with fever of unknown origin (FUO). Inappropriate empiric antimicrobial therapy increases difficulty in BSI diagnosis and treatment. Knowing the risk of BSI at early stage may help improve clinical outcomes and reduce antibiotic overuse.
methodsWe constructed a multivariate prediction model based on clinical features and serum inflammatory markers using a cohort of FUO patients over a 5-year period by Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression.
resultsAmong 712 FUO patients, BSI was confirmed in 55 patients. Five independent predictors available within 24 h after admission for BSI were identified: presence of diabetes mellitus, chills, C-reactive protein level of 50-100 mg/L, procalcitonin > 0.3 ng/mL, neutrophil percentage > 75%. A predictive score incorporating these 5 variables has adequate concordance with an area under the curve of 0.85. The model showed low positive predictive value (22.6%), but excellent negative predictive value (97.4%) for predicting the risk of BSI. The risk of BSI reduced to 2.0% in FUO patients if score < 1.5.
conclusionsA simple tool based on 5 variables is useful for timely ruling out the individuals at low risk of BSI in FUO population.
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