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BACKGROUND: Improving the diagnosis of serious bacterial infections (SBIs) in the children's emergency department is a clinical priority. Early recognition reduces morbidity and mortality, and supporting clinicians in ruling out SBIs may limit unnecessary admissions and antibiotic use. METHODS: A prospective, diagnostic accuracy study of clinical and biomarker variables in the diagnosis of SBIs (pneumonia or other SBI) in febrile children <16 years old. A diagnostic model was derived by using multinomial logistic regression and internally validated. External validation of a published model was undertaken, followed by model updating and extension by the inclusion of procalcitonin and resistin. RESULTS: There were 1101 children studied, of whom 264 had an SBI. A diagnostic model discriminated well between pneumonia and no SBI (concordance statistic 0.84, 95% confidence interval 0.78-0.90) and between other SBIs and no SBI (0.77, 95% confidence interval 0.71-0.83) on internal validation. A published model discriminated well on external validation. Model updating yielded good calibration with good performance at both high-risk (positive likelihood ratios: 6.46 and 5.13 for pneumonia and other SBI, respectively) and low-risk (negative likelihood ratios: 0.16 and 0.13, respectively) thresholds. Extending the model with procalcitonin and resistin yielded improvements in discrimination. CONCLUSIONS: Diagnostic models discriminated well between pneumonia, other SBIs, and no SBI in febrile children in the emergency department. Improvements in the classification of nonevents have the potential to reduce unnecessary hospital admissions and improve antibiotic prescribing. The benefits of this improved risk prediction should be further evaluated in robust impact studies.

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Anti-Bacterial Agents, Bacterial Infections, Child, Preschool, Diagnosis, Differential, Early Diagnosis, Emergency Service, Hospital, Female, Fever of Unknown Origin, Humans, Infant, Likelihood Functions, Male, Models, Statistical, Multivariate Analysis, Pneumonia, Bacterial, Prognosis, Prospective Studies, Quality Improvement, Risk Assessment