How well do clinical prediction rules perform in identifying serious infections in acutely ill children across an international network of ambulatory care datasets?

Jan Y. Verbakel*, Ann Van den Bruel, Matthew Thompson, Richard Stevens, Bert Aertgeerts, Rianne Oostenbrink, Henriette A. Moll, Marjolein Y. Berger, Monica Lakhanpaul, David Mant, Frank Buntinx

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

38 Citations (Web of Science)

Abstract

Background: Diagnosing serious infections in children is challenging, because of the low incidence of such infections and their non-specific presentation early in the course of illness. Prediction rules are promoted as a means to improve recognition of serious infections. A recent systematic review identified seven clinical prediction rules, of which only one had been prospectively validated, calling into question their appropriateness for clinical practice. We aimed to examine the diagnostic accuracy of these rules in multiple ambulatory care populations in Europe. Methods: Four clinical prediction rules and two national guidelines, based on signs and symptoms, were validated retrospectively in seven individual patient datasets from primary care and emergency departments, comprising 11,023 children from the UK, the Netherlands, and Belgium. The accuracy of each rule was tested, with pre-test and post-test probabilities displayed using dumbbell plots, with serious infection settings stratified as low prevalence (LP; 20%). In LP and IP settings, sensitivity should be >90% for effective ruling out infection. Results: In LP settings, a five-stage decision tree and a pneumonia rule had sensitivities of >90% (at a negative likelihood ratio (NLR) of
Original languageEnglish
Article number10
JournalBMC Medicine
Volume11
DOIs
Publication statusPublished - 15 Jan 2013

Keywords

  • clinical prediction rules
  • serious infection in children
  • external validation
  • NICE guidelines feverish illness
  • Yale Observation Scale
  • diagnostic accuracy

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