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Original Investigation
February 18, 2019

A Clinical Prediction Rule to Identify Febrile Infants 60 Days and Younger at Low Risk for Serious Bacterial Infections

Author Affiliations
  • 1Departments of Emergency Medicine and Pediatrics, University of California, Davis School of Medicine, Sacramento
  • 2Division of Emergency Medicine, Department of Pediatrics, Columbia University College of Physicians and Surgeons, New York, New York
  • 3Department of Pediatrics, Bellevue Hospital, New York University Langone Medical Center, New York, New York
  • 4Division of Emergency Medicine, Department of Pediatrics, Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  • 5Department of Emergency Medicine, University of California, Davis School of Medicine, Sacramento
  • 6Department of Pediatrics, Children’s Hospital of Wisconsin, Medical College of Wisconsin, Milwaukee
  • 7Children’s Hospital of Colorado, University of Colorado School of Medicine, Aurora
  • 8Division of Emergency Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
  • 9Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio
  • 10Department of Pediatrics, The Colorado Children’s Hospital, University of Colorado, Denver
  • 11Departments of Emergency Medicine and Pediatrics, University of Michigan School of Medicine, Ann Arbor
  • 12Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children’s Hospital, Northwestern University Feinberg School of Medicine, Chicago, Illinois
  • 13Division of Emergency Medicine, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
  • 14Division of Emergency Medicine, Department of Pediatrics, St Louis Children’s Hospital, Washington University, St Louis, Missouri
  • 15Division of Emergency Medicine, Phoenix Children’s Hospital, Phoenix, Arizona
  • 16Department of Emergency Medicine and Pediatrics, Hasbro Children’s Hospital, Providence, Rhode Island
  • 17Brown University School of Medicine, Providence, Rhode Island
  • 18Department of Pediatrics, Women and Children’s Hospital of Buffalo, State University of New York at Buffalo School of Medicine
  • 19Department of Emergency Medicine, Helen DeVos Children’s Hospital of Spectrum Health, Grand Rapids, Michigan
  • 20Departments of Emergency Medicine and Pediatrics, Western Michigan University Homer Stryker MD School of Medicine, Kalamazoo
  • 21Division of Emergency Medicine, Department of Pediatrics, Primary Children’s Medical Center, University of Utah, Salt Lake City
  • 22Division of Emergency Medicine, Department of Pediatrics, University of Maryland Medical Center, Baltimore
  • 23Sections of Emergency Medicine and Infectious Diseases, Department of Pediatrics, Texas Children’s Hospital, Baylor College of Medicine, Houston
  • 24Department of Pediatrics, Jacobi Medical Center, Albert Einstein College of Medicine, Bronx, New York
  • 25Division of Emergency Medicine, Department of Pediatrics, Nationwide Children’s Hospital, Columbus, Ohio
  • 26The Ohio State University School of Medicine, Columbus
  • 27Departments of Emergency Medicine and Pediatrics, University of Rochester Medical Center, Rochester, New York
  • 28Department of Emergency Medicine, Hurley Medical Center, Flint, Michigan
  • 29University of Michigan School of Medicine, Ann Arbor
  • 30Departments of Pediatrics and Emergency Medicine, Children’s Hospital of Wisconsin, Medical College of Wisconsin, Milwaukee
  • 31Division of Emergency Medicine, Alfred I. duPont Hospital for Children, Nemours Children’s Health System, Thomas Jefferson School of Medicine, Wilmington, Delaware
  • 32Division of Emergency Medicine, Department of Pediatrics, Children’s National Medical Center, The George Washington School of Medicine and Health Sciences, Washington, DC
  • 33Department of Pediatrics, Johns Hopkins University, Baltimore, Maryland
  • 34Division of Emergency Medicine, Department of Pediatrics, Children’s Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
  • 35Ann and Robert H. Lurie Children’s Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois
  • 36Department of Pediatrics, University of Utah School of Medicine, Salt Lake City
  • 37Division of Pediatric Infectious Diseases and Center for Vaccines and Immunity, Nationwide Children’s Hospital, Columbus, Ohio
  • 38Division of Emergency Medicine, Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit, Michigan
  • 39Department of Emergency Medicine, University of Michigan School of Medicine, Ann Arbor
JAMA Pediatr. 2019;173(4):342-351. doi:10.1001/jamapediatrics.2018.5501
Key Points

Question  Can clinical features and laboratory tests identify febrile infants 60 days and younger at low risk for serious bacterial infections?

Findings  In a cohort of 1821 febrile infants 60 days and younger, 170 (9.3%) had serious bacterial infections, and using recursive partitioning analysis, we derived a low-risk prediction rule involving 3 variables: normal urinalysis, absolute neutrophil count ≤4090/μL, and serum procalcitonin ≤1.71 ng/mL. The rule sensitivity was 97.7%, specificity was 60.0%, and negative predictive value was 99.6%; no infant with bacterial meningitis was missed.

Meaning  The urinalysis, absolute neutrophil count, and serum procalcitonin levels may accurately identify febrile infants 60 days and younger at low risk for serious bacterial infections.

Abstract

Importance  In 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.

Objective  To derive and validate a prediction rule to identify febrile infants 60 days and younger at low risk for SBIs.

Design, Setting, and Participants  Prospective, 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.

Exposures  Clinical 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 Measures  Serious bacterial infection, defined as urinary tract infection, bacteremia, or bacterial meningitis.

Results  We 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 Relevance  We 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.

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