AbstractWe aimed to derive the Febrile Infants Risk Score at Triage (FIRST) to quantify risk for serious bacterial infections (SBIs), defined as bacteremia, meningitis and urinary tract infections. We performed a prospective observational study on febrile infants < 3 months old at a tertiary hospital in Singapore between 2018 and 2021. We utilized machine learning and logistic regression to derive 2 models: FIRST, based on patient demographics, vital signs and history, and FIRST + , adding laboratory results to the same variables. SBIs were diagnosed in 224/1002 (22.4%) infants. Among 994 children with complete data, age (adjusted odds ratio [aOR] 1.01 95%CI 1.01–1.02, p < 0.001), high temperature (aOR 2.22 95%CI 1.69–2.91, p < 0.001), male sex (aOR 2.62 95%CI 1.86–3.70, p < 0.001) and fever of ≥ 2 days (aOR 1.79 95%CI 1.18–2.74, p = 0.007) were independently associated with SBIs. For FIRST + , abnormal urine leukocyte esterase (aOR 16.46 95%CI 10.00–27.11, p < 0.001) and procalcitonin (aOR 1.05 95%CI 1.01–1.09, p = 0.009) were further identified. A FIRST + threshold of ≥ 15% predicted risk had a sensitivity of 81.8% (95%CI 70.5–91.0%) and specificity of 65.6% (95%CI 57.8–72.7%). In the testing dataset, FIRST + had an area under receiver operating characteristic curve of 0.87 (95%CI 0.81–0.94). These scores can potentially guide triage and prioritization of febrile infants.