Validating and updating a prediction rule for serious bacterial infection in patients with fever without source

Acta Paediatr. 2007 Jan;96(1):100-4. doi: 10.1111/j.1651-2227.2006.00033.x.

Abstract

Aim: To externally validate and update a previously developed rule for predicting the presence of serious bacterial infections in children with fever without apparent source.

Methods: Patients, 1-36 mo, presenting with fever without source, were prospectively enrolled. Serious bacterial infection included bacterial meningitis, sepsis, bacteraemia, pneumonia, urinary tract infection, bacterial gastroenteritis, osteomyelitis/ethmoiditis. The generalizability of the original rule was determined. Subsequently, the prediction rule was updated using all available data of the patients with fever without source (1996-1998 and 2000-2001, n = 381) using multivariable logistic regression.

Results: the generalizability of the rule appeared insufficient in the new patients (n = 150). In the updated rule, independent predictors from history and examination were duration of fever, vomiting, ill clinical appearance, chest-wall retractions and poor peripheral circulation (ROC area (95%CI): 0.69 (0.63-0.75)). Additional independent predictors from laboratory were serum white blood cell count and C-reactive protein, and in urinalysis > or = 70 white bloods (ROC area (95%CI): 0.83 (0.78-0.88).

Conclusions: A previously developed prediction rule for predicting the presence of serious bacterial infection in children with fever without apparent source was updated. Its clinical score can be used as a first screening tool. Additional laboratory testing may specify the individual risk estimate (range: 4-54%) further.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Bacterial Infections / complications
  • Bacterial Infections / diagnosis*
  • Child, Preschool
  • Clinical Laboratory Techniques
  • Decision Support Techniques
  • Emergency Service, Hospital
  • Fever of Unknown Origin / etiology*
  • Forecasting
  • Humans
  • Infant