Bloodstream infection (BSI) in febrile patients is associated with high mortality. Clinical and laboratory variables, such as procalcitonin (PCT), may predict BSI and help decision-making concerning empirical treatment. This study compared two models for prediction of BSI, and evaluated the role of PCT vs. clinical variables, collected daily in 300 consecutive febrile inpatients, for 48 h after onset of fever. Multiple logistic regression (MLR) and classification and regression tree (CART) models were compared for discriminatory power and diagnostic performance. BSI was present in 17% of cases. MLR identified the presence of intravascular devices, nadir albumin and thrombocyte counts, and peak temperature, respiratory rate and leukocyte counts, but not PCT, as independent predictors of BSI. In contrast, a peak PCT level of >2.45 ng/mL was the principal discriminator in the decision tree based on CART. The latter was more accurate (94%) than the model based on MLR (72%; p <0.01). Hence, the presence of BSI in febrile patients is predicted more accurately and by different variables, e.g., PCT, in CART analysis, as compared with MLR models. This underlines the value of PCT plus CART analysis in the diagnosis of a febrile patient.