An all-inclusive model for predicting invasive bacterial infection in febrile infants age 7–60 days

Background

Invasive bacterial infections (IBIs) in febrile infants are rare but potentially devastating. We aimed to derive and validate a predictive model for IBI among febrile infants age 7–60 days.

Methods

Data were abstracted retrospectively from electronic records of 37 emergency departments (EDs) for infants with a measured temperature >=100.4 F who underwent an ED evaluation with blood and urine cultures. Models to predict IBI were developed and validated respectively using a random 80/20 dataset split, including 10-fold cross-validation. We used precision recall curves as the classification metric.

Results

Of 4411 eligible infants with a mean age of 37 days, 29% had characteristics that would likely have excluded them from existing risk stratification protocols. There were 196 patients with IBI (4.4%), including 43 (1.0%) with bacterial meningitis. Analytic approaches varied in performance characteristics (precision recall range 0.04–0.29, area under the curve range 0.5–0.84), with the XGBoost model demonstrating the best performance (0.29, 0.84). The five most important variables were serum white blood count, maximum temperature, absolute neutrophil count, absolute band count, and age in days.

Conclusion

A machine learning model (XGBoost) demonstrated the best performance in predicting a rare outcome among febrile infants, including those excluded from existing algorithms.

Impact

Several models for the risk stratification of febrile infants have been developed. There is a need for a preferred comprehensive model free from limitations and algorithm exclusions that accurately predicts IBIs.

This is the first study to derive an all-inclusive predictive model for febrile infants aged 7–60 days in a community ED sample with IBI as a primary outcome.

This machine learning model demonstrates potential for clinical utility in predicting IBI.