A PREDICTIVE MODEL TO ESTIMATE THE RISK OF SERIOUS BACTERIAL-INFECTIONS IN FEBRILE INFANTS

Low risk criteria have been defined to identify febrile infants unlike ly to have serious bacterial infection (SBI). Using these criteria app roximately 40% of all febrile infants can be defined as being at low r isk. Of the remaining infants (60%) only 10%-20% have an SBI. No adequ ate criteria exists to identify these infants. All infants aged 2 week s-1 year, presenting during a 1-year-period with rectal temperature gr eater than or equal to 38.0 degrees C to the Sophia Children's Hospita l were included in a prospective study. Infants with a history of prem aturity, perinatal complications, known underlying disease, antibiotic treatment or vaccination during the preceding 48 h were excluded. Cli nical and laboratory variables at presentation were evaluated by a mul tivariate logistic regression model using SBI as the dependent variabl e. By using likelihood ratios a predictive model was derived, providin g a post test probability of SBI for every individual patient. Of the 138 infants included in the study, 33 (24%) had SBI. Logistic regressi on analysis defined C-reactive protein (CRP), duration of fever, a sta ndardized clinical impression score, a history of diarrhoea and focal signs of infection as independent predictors of SBI. Conclusion CRP, d uration of fever, the ''standardized clinical impression score'', a hi story of diarrhoea and focal signs of infection were the independent, most powerful predictors of SBI in febrile infants, identified by logi stic regression analysis. Although the predictive model is not validat ed for direct clinical use, it illustrates the clinical potential of t he used technique. This technique offers the advantage to assess the p robability of SBI in every individual infant. This probability will fo rm the best basis for well-founded decisions in the management of the individual febrile infant.