Using Clinical History Factors to Identify Bacterial Infections in Young Febrile Infants.

OBJECTIVE: To develop a novel predictive model using primarily clinical history factors and compare performance to the widely used Rochester Low Risk (RLR) model.

STUDY DESIGN: In this cross-sectional study, we identified infants brought to one pediatric emergency department from January 2014 to December 2016. We included infants age 0-90 days, with temperature ≥38°C, and documented gestational age and illness duration. The primary outcome was bacterial infection. We used 10 predictors to develop regression and ensemble machine learning models, which we trained and tested using 10-fold cross-validation. We compared areas under the curve (AUCs), sensitivities, and specificities of the RLR, regression, and ensemble models.

RESULTS: Of 877 infants, 67 had a bacterial infection (7.6%). The AUCs of the RLR, regression, and ensemble models were 0.776 (95% CI 0.746, 0.807), 0.945 (0.913, 0.977), and 0.956 (0.935, 0.975), respectively. Using a bacterial infection risk threshold of .01, the sensitivity and specificity of the regression model was 94.6% (87.4%, 100%) and 74.5% (62.4%, 85.4%), compared with 95.5% (87.5%, 99.1%) and 59.6% (56.2%, 63.0%) using the RLR model.

CONCLUSIONS: Compared with the RLR model, sensitivities of the novel predictive models were similar whereas AUCs and specificities were significantly greater. If externally validated, these models, by producing an individualized bacterial infection risk estimate, may offer a targeted approach to young febrile infants that is noninvasive and inexpensive.