Predictive model for serious bacterial infections among infants younger than 3 months of age

Objective. To develop a data-derived model for predicting serious bacterial infection (SBI) among febrile infants <3 months old. Methods. All infants <less than or equal to>90 days old with a temperature greater than or equal to 38.0 degrees C seen in an urban emergency departme nt (ED) were retrospectively identified. SBI was defined as a positive cult ure of urine, blood, or cerebrospinal fluid. Tree-structured analysis via r ecursive partitioning was used to develop the model. SBI or No-SBI was the dichotomous outcome variable, and age, temperature, urinalysis (UA), white blood cell (WBC) count, absolute neutrophil count, and cerebrospinal fluid WBC were entered as potential predictors. The model was tested by V-fold cr oss-validation. Results. Of 5279 febrile infants studied, SBI was diagnosed in 373 patients (7%): 316 urinary tract infections (UTIs), 17 meningitis, and 59 bacteremi a (8 with meningitis, 11 with UTIs). The model sequentially used 4 clinical parameters to define high-risk patients: positive UA, WBC count greater th an or equal to 20 000/mm(3) or less than or equal to 4100/mm(3), temperatur e greater than or equal to 39.6 degreesC, and age <13 days. The sensitivity of the model for SBI is 82% (95% confidence interval [CI]: 78%-86%) and th e negative predictive value is 98.3% (95% CI: 97.8%-98.7%). The negative pr edictive value for bacteremia or meningitis is 99.6% (95% CI: 99.4%-99.8%). The relative risk between high- and low-risk groups is 12.1 (95% CI: 9.3-1 5.6). Sixty-six SBI patients (18%) were misclassified into the lower risk g roup: 51 UTIs, 14 with bacteremia, and 1 with meningitis. Conclusions. Decision-tree analysis using common clinical variables can rea sonably predict febrile infants at high- risk for SBI. Sequential use of UA , WBC count, temperature, and age can identify infants who are at high risk of SBI with a relative risk of 12.1 compared with lower-risk infants.