• Nathan Kuppermann, Peter S Dayan, Deborah A Levine, Melissa Vitale, Leah Tzimenatos, Michael G Tunik, Mary Saunders, Richard M Ruddy, Genie Roosevelt, Alexander J Rogers, Elizabeth C Powell, Lise E Nigrovic, Jared Muenzer, James G Linakis, Kathleen Grisanti, David M Jaffe, John D Hoyle, Richard Greenberg, Rajender Gattu, Andrea T Cruz, Ellen F Crain, Daniel M Cohen, Anne Brayer, Dominic Borgialli, Bema Bonsu, Lorin Browne, Stephen Blumberg, Jonathan E Bennett, Shireen M Atabaki, Jennifer Anders, Elizabeth R Alpern, Benjamin Miller, T Charles Casper, J Michael Dean, Octavio Ramilo, Prashant Mahajan, and Febrile Infant Working Group of the Pediatric Emergency Care Applied Research Network (PECARN).
• Departments of Emergency Medicine and Pediatrics, University of California, Davis School of Medicine, Sacramento.
• JAMA Pediatr. 2019 Apr 1; 173 (4): 342-351.

ImportanceIn young febrile infants, serious bacterial infections (SBIs), including urinary tract infections, bacteremia, and meningitis, may lead to dangerous complications. However, lumbar punctures and hospitalizations involve risks and costs. Clinical prediction rules using biomarkers beyond the white blood cell count (WBC) may accurately identify febrile infants at low risk for SBIs.ObjectiveTo derive and validate a prediction rule to identify febrile infants 60 days and younger at low risk for SBIs.Design, Setting, And ParticipantsProspective, observational study between March 2011 and May 2013 at 26 emergency departments. Convenience sample of previously healthy febrile infants 60 days and younger who were evaluated for SBIs. Data were analyzed between April 2014 and April 2018.ExposuresClinical and laboratory data (blood and urine) including patient demographics, fever height and duration, clinical appearance, WBC, absolute neutrophil count (ANC), serum procalcitonin, and urinalysis. We derived and validated a prediction rule based on these variables using binary recursive partitioning analysis.Main Outcomes And MeasuresSerious bacterial infection, defined as urinary tract infection, bacteremia, or bacterial meningitis.ResultsWe derived the prediction rule on a random sample of 908 infants and validated it on 913 infants (mean age was 36 days, 765 were girls [42%], 781 were white and non-Hispanic [43%], 366 were black [20%], and 535 were Hispanic [29%]). Serious bacterial infections were present in 170 of 1821 infants (9.3%), including 26 (1.4%) with bacteremia, 151 (8.3%) with urinary tract infections, and 10 (0.5%) with bacterial meningitis; 16 (0.9%) had concurrent SBIs. The prediction rule identified infants at low risk of SBI using a negative urinalysis result, an ANC of 4090/µL or less (to convert to ×109 per liter, multiply by 0.001), and serum procalcitonin of 1.71 ng/mL or less. In the validation cohort, the rule sensitivity was 97.7% (95% CI, 91.3-99.6), specificity was 60.0% (95% CI, 56.6-63.3), negative predictive value was 99.6% (95% CI, 98.4-99.9), and negative likelihood ratio was 0.04 (95% CI, 0.01-0.15). One infant with bacteremia and 2 infants with urinary tract infections were misclassified. No patients with bacterial meningitis were missed by the rule. The rule performance was nearly identical when the outcome was restricted to bacteremia and/or bacterial meningitis, missing the same infant with bacteremia.Conclusions And RelevanceWe derived and validated an accurate prediction rule to identify febrile infants 60 days and younger at low risk for SBIs using the urinalysis, ANC, and procalcitonin levels. Once further validated on an independent cohort, clinical application of the rule has the potential to decrease unnecessary lumbar punctures, antibiotic administration, and hospitalizations.

31 keywords...

hide…