Clinical prediction models for young febrile infants at the emergency department: an international validation study

Evelien de Vos-Kerkhof1, Borja Gomez2,3, Karen Milcent4

  • 1Department of General Paediatrics, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands.

Insights

Four clinical prediction models (CPMs) effectively identify serious bacterial infections in febrile infants. These models, incorporating clinical signs, symptoms, and lab markers, serve as valuable adjunctive tools for risk assessment in young children.

Area of Science:

  • Pediatrics
  • Infectious Diseases
  • Clinical Diagnostics

Background:

  • Febrile young infants are at risk for serious bacterial infections (SBIs).
  • Clinical prediction models (CPMs) are statistically derived tools to aid diagnosis.
  • Assessing the diagnostic value of existing CPMs is crucial for timely intervention.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of established CPMs for identifying SBIs in febrile infants.
  • To determine the performance of CPMs across different age groups and clinical settings.

Main Methods:

  • Systematic literature review to identify eight relevant CPMs.
  • Validation of selected CPMs using four independent cohorts of febrile infants (<3 months and 1-12 months).
  • Performance assessment using sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and calibration.

Main Results:

  • Four CPMs demonstrated moderate to excellent performance (AUC 0.68-0.94) in validation cohorts.
  • Models incorporating clinical signs, symptoms, urine dipstick, and laboratory markers showed best results.
  • Specificities generally exceeded sensitivities in validation cohorts, particularly for infants <3 months.

Conclusions:

  • Four CPMs, utilizing clinical and laboratory data, can assist clinicians in diagnosing SBIs in febrile infants.
  • CPMs should be employed as adjunctive tools for risk stratification in this vulnerable population.
Abstract

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