A predictive model to estimate the risk of serious bacterial infections in febrile infants

Low risk criteria have been defined to identify febrile infants unlikely to have serious bacterial infection (SBI). Using these criteria approximately 40% of all febrile infants can be defined as being at low risk. Of the remaining infants (60%) only 10%–20% have an SBI. No adequate criteria exist to identify these infants. All infants aged 2 weeks-1 year, presenting during a 1-year-period with rectal temperature 38.0°C to the Sophia Children's Hospital were included in a prospective study. Infants with a history of prematurity, perinatal complications, known underlying disease, antibiotic treatment or vaccination during the preceding 48 h were excluded. Clinical and laboratory variables at presentation were evaluated by a multivariate logistic regression model using SBI as the dependent variable. By using likelihood ratios a predictive model was derived, providing a post test probability of SBI for every individual patient. Of the 138 infants included in the study, 33 (24%) had SBI. Logistic regression analysis defined C-reactive protein (CRP), duration of fever, a standardized clinical impression score, a history of diarrhoea and focal signs of infection as independent predictors of SBI.Conclusion CRP, duration of fever, the standardized clinical impression score, a history of diarrhoea and focal signs of infection were the independent, most powerful predictors of SBI in febrile infants, identified by logistic regression analysis. Although the predictive model is not validated for direct clinical use, it illustrates the clinical potential of the used technique. This technique offers the advantage to assess the probability of SBI in every individual infant. This probability will form the best basis for well-founded decisions in the management of the individual febrile infant.