ArticleThe Journal of pediatrics2021
Using Clinical History Factors to Identify Bacterial Infections in Young Febrile Infants.
Article in The Journal of pediatrics, 2021. 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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Who cites it
8 citing papers in PubMed.
- A 13-year cohort study using clinical machine learning to differentiate bacterial and viral infections in young infants in a dengue hyperendemic region.Journal of tropical pediatrics · 2026Article
- Invasive and urinary tract infections in febrile neonates in the emergency department: a retrospective analysis.European journal of pediatrics · 2025Article
- Development of a machine learning-based prediction model for serious bacterial infections in febrile young infants.BMJ paediatrics open · 2025Article
- Scoping review of clinical decision aids in the assessment and management of febrile infants under 90 days of age.BMC pediatrics · 2025Article
- Performance of AAP Clinical Practice Guideline for Febrile Infants at One Pediatric Hospital.Hospital pediatrics · 2023Article
- Derivation of a clinical-based model to detect invasive bacterial infections in febrile infants.Journal of hospital medicine · 2022Article
- Refinement and Validation of a Clinical-Based Approach to Evaluate Young Febrile Infants.Hospital pediatrics · 2022Article
- Derivation of a natural language processing algorithm to identify febrile infants.Journal of hospital medicine · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
objectiveTo develop a novel predictive model using primarily clinical history factors and compare performance to the widely used Rochester Low Risk (RLR) model. STUDY
designIn this cross-sectional study, we identified infants brought to one pediatric emergency department from January 2014 to December 2016. We included infants age 0-90 days, with temperature ≥38°C, and documented gestational age and illness duration. The primary outcome was bacterial infection. We used 10 predictors to develop regression and ensemble machine learning models, which we trained and tested using 10-fold cross-validation. We compared areas under the curve (AUCs), sensitivities, and specificities of the RLR, regression, and ensemble models.
resultsOf 877 infants, 67 had a bacterial infection (7.6%). The AUCs of the RLR, regression, and ensemble models were 0.776 (95% CI 0.746, 0.807), 0.945 (0.913, 0.977), and 0.956 (0.935, 0.975), respectively. Using a bacterial infection risk threshold of .01, the sensitivity and specificity of the regression model was 94.6% (87.4%, 100%) and 74.5% (62.4%, 85.4%), compared with 95.5% (87.5%, 99.1%) and 59.6% (56.2%, 63.0%) using the RLR model.
conclusionsCompared with the RLR model, sensitivities of the novel predictive models were similar whereas AUCs and specificities were significantly greater. If externally validated, these models, by producing an individualized bacterial infection risk estimate, may offer a targeted approach to young febrile infants that is noninvasive and inexpensive.
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