Predictive model for serious bacterial infections in children with fever presenting to emergency department

Creation and validation of clinical prediction model (CPM) for serious bacterial infections (SBI) in children presenting to emergency department (ED) with febrile illness, based on clinical variables, clinician’s “gut feeling”, and “sense of reassurance”. Febrile children presenting to the ED of Children’s Clinical University Hospital (CCUH) between 1st of April 2017 and 31st of December 2018 were enrolled in a prospective observational study. Data on clinical signs and symptoms were recorded in a standardized case report form. Clinician’s “gut feeling” of something wrong and “sense of reassurance” were assessed via questionnaire completed after examination. Variable selection for the CPM was performed using stepwise logistic regression (forward, backward, and bidirectional), AIC criteria was used to penalize for too many parameters. Bootstrapping was used for assessment of the model’s internal validity and correction for overoptimism. For external validation, the model was tested for prediction of SBI in a separate dataset of patients presenting to six regional hospitals between 1st of January and 31st of March 2019. 517 children were included in CCUH, 54% (n=279) were boys, the median age was 58 months. 26.7% (n=138) developed SBI. 188 patients were enrolled in validation cohort, the median age was 28 months, 26.6% (n=50) developed SBI. Two CPMs were created, CPM1 consisting of eight clinical variables, and CPM2 with four clinical variables: “Refusal to drink”, “Tachypnoea”, “Reduced breath sounds”, “Poor peripheral circulation”; plus “gut feeling” and “sense of reassurance”. The area under curve (AUC) for ROC curve of CPM1 was 0.738 in CCUH cohort and 0.677 in validation cohort. The AUC for CPM2 was 0.783 in CCUH cohort and 0.752 in validation cohort. Both CPMs had moderate ability to predict SBI and had acceptable performance in validation cohort. Adding variables “gut feeling” and “sense of reassurance” in the CPM improved its ability to predict SBI.