Polytomous regression did not outperform dichotomous logistic regression in diagnosing serious bacterial infections in febrile children

J Clin Epidemiol. 2008 Feb;61(2):135-41. doi: 10.1016/j.jclinepi.2007.07.005.

Abstract

Objective: To compare polytomous and dichotomous logistic regression analyses in diagnosing serious bacterial infections (SBIs) in children with fever without apparent source (FWS).

Study design and setting: We analyzed data of 595 children aged 1-36 months, who attended the emergency department with fever without source. Outcome categories were SBI, subdivided in pneumonia and other-SBI (OSBI), and non-SBI. Potential predictors were selected based on previous studies and literature. Four models were developed: a polytomous model, estimating probabilities for three diagnostic categories simultaneously; two sequential dichotomous models, which differed in variable selection, discriminating SBI and non-SBI in step 1, and pneumonia and OSBI in step 2; and model 4, where each outcome category was opposed to the other two. The models were compared with respect to the area under the receiver-operating characteristic curve (AUC) for each of the three outcome categories and to the variable selection.

Results: Small differences were found in the variables that were selected in the polytomous and dichotomous models. The AUCs of the three outcome categories were similar for each modeling strategy.

Conclusion: A polytomous logistic regression analysis did not outperform sequential and single application of dichotomous logistic regression analyses in diagnosing SBIs in children with FWS.

Publication types

  • Comparative Study

MeSH terms

  • Bacterial Infections / complications
  • Bacterial Infections / diagnosis*
  • Child, Preschool
  • Data Interpretation, Statistical
  • Decision Support Techniques*
  • Emergency Service, Hospital
  • Female
  • Fever of Unknown Origin / microbiology*
  • Humans
  • Infant
  • Logistic Models
  • Male
  • Regression Analysis*
  • Risk Factors