An all-inclusive model for predicting invasive bacterial infection in febrile infants age 7-60 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.PMID:38575694 | DOI:10.1038/s41390-024-03141-3

Source: Pediatric Research - April 4, 2024 Category: Pediatrics Authors: Dustin W Ballard Jie Huang Adam L Sharp Dustin G Mark Tran H P Nguyen Beverly R Young David R Vinson Patrick Van Winkle Mamata V Kene Adina S Rauchwerger Jennifer Y Zhang Stacy J Park Mary E Reed Tara L Greenhow Source Type: research