Predictors of bacteremia in febrile children 3 to 36 months of age

Purpose. To develop an improved model for the prediction of bacteremia in y oung febrile children. Methods. A retrospective review was performed on patients 3 to 36 months of age seen in a children's hospital emergency department between December 19 95 and September 1997 who had a complete blood count and blood culture orde red as part of their regular care. Exclusion criteria included current use of antibiotics or any immunodeficient state. Clinical and laboratory parame ters reviewed included age, gender, race, weight, temperature, presence of focal bacterial infection, white blood cell count (WBC), polymorphonuclear cell count (PMN), band count, and absolute neutrophil count (ANC). Logistic regression analyses were used to identify factors associated with bacterem ia, defined as growth of a pathogen in a blood culture. The model that was developed was then validated on a second dataset consisting of febrile pati ents 3 to 36 months of age collected from a second children's hospital (val idation set). Results. There were 633 patients in the derivation set (46 bacteremic) and 9465 patients in the validation set (149 bacteremic). The mean age of patie nts in the derivation and validation sets were 15.8 months (95% confidence interval [CI]: 15.2-16.5) and 16.6 months (95% CI: 16.5-16.8), respectively ; the mean temperatures were 39.1 degreesC (95% CI: 39.0-39.2) and 39.8 deg reesC (95% CI: 39.7-39.8); 56% were male in the derivation set and 55% male in the validation set predictors of bacteremia identified by logistic regr ession included ANC, WBC, PMN, temperature, and gender. Receiver operator c haracteristic (ROC) analysis showed similar performance of ANC and WBC as p redictors of bacteremia. When placed into a multivariate logistic regressio n model, band count was not significantly associated with bacteremia. Infor mation regarding focal infection was available for 572 patients in the deri vation set. The percentage of patients diagnosed with bacteremia with a foc al bacterial infection was not significantly different from the percentage who had bacteremia without a focal bacterial infection (16/200 vs 30/ 372). Based on this dataset, a logistic regression formula was developed that co uld be used to develop a unique risk value for each patient based on temper ature, gender, and ANC. When the final model was applied to the validation set, the area under the ROC curve (AUC) constructed from these data indicat ed that the model retained good predictive value (AUC for the derivation vs validation data = .8348 vs 0.8221, respectively). Conclusions. Use of the formulas derived here allows the clinician to estim ate a child's risk for bacteremia based on temperature, ANC, and gender. Th is approach offers a useful alternative to predictions based on fever and W BC alone.