title: Predictive model for serious bacterial infections among infants younger than 3 months of age.
abstract: OBJECTIVE
  To develop a data-derived model for predicting serious bacterial infection (SBI) among febrile infants <3 months old.
  
  
  METHODS
  All infants </=90 days old with a temperature >/=38.0 degrees C seen in an urban emergency department (ED) were retrospectively identified. SBI was defined as a positive culture of urine, blood, or cerebrospinal fluid. Tree-structured analysis via recursive 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 cross-validation.
  
  
  RESULTS
  Of 5279 febrile infants studied, SBI was diagnosed in 373 patients (7%): 316 urinary tract infections (UTIs), 17 meningitis, and 59 bacteremia (8 with meningitis, 11 with UTIs). The model sequentially used 4 clinical parameters to define high-risk patients: positive UA, WBC count >/=20 000/mm(3) or </=4100/mm(3), temperature >/=39.6 degrees C, and age <13 days. The sensitivity of the model for SBI is 82% (95% confidence interval [CI]: 78%-86%) and the negative predictive value is 98.3% (95% CI: 97.8%-98.7%). The negative predictive 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: 9.3-15.6). Sixty-six SBI patients (18%) were misclassified into the lower risk group: 51 UTIs, 14 with bacteremia, and 1 with meningitis.
  
  
  CONCLUSIONS
  Decision-tree analysis using common clinical variables can reasonably 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.
authors.affiliation: Division of Emergency Medicine, Children's Hospital, Boston, Massachusetts 02115, USA. bachur@tch.harvard.edu
authors.forename: R G
authors.initials: RG
authors.lastname: Bachur
authors.forename: M B
authors.initials: MB
authors.lastname: Harper
mesh_terms: D000367:Age Factors; D016470:Bacteremia / Q000175:diagnosis / Q000382:microbiology; D001419:Bacteria / Q000254:growth & development / Q000302:isolation & purification; D001424:Bacterial Infections / Q000175:diagnosis* / Q000382:microbiology; D001431:Bacteriological Techniques / Q000706:statistics & numerical data; D001769:Blood / Q000382:microbiology; D001772:Blood Cell Count / Q000706:statistics & numerical data; D002555:Cerebrospinal Fluid / Q000382:microbiology; D003661:Decision Support Techniques; D003663:Decision Trees; D005334:Fever / Q000175:diagnosis* / Q000382:microbiology; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007958:Leukocyte Count / Q000706:statistics & numerical data; D008581:Meningitis / Q000175:diagnosis / Q000382:microbiology; D015233:Models, Statistical; D009504:Neutrophils / Q000166:cytology; D017410:Practice Guidelines as Topic; D011336:Probability; D012189:Retrospective Studies; D012307:Risk Factors; D016482:Urinalysis / Q000706:statistics & numerical data; D014552:Urinary Tract Infections / Q000175:diagnosis / Q000382:microbiology; D014556:Urine / Q000382:microbiology