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

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.

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