CA FIRST (California Febrile Infant Risk Stratification Tool) Algorithm Development in a Learning Health System

Abstract

Introduction

There is considerable variation in the approach to infants presenting to the emergency department (ED) with fever. The authors’ primary aim was to develop a robust set of algorithms using community ED data to inform modifications of broader clinical guidance.

Methods

The authors report the development of California Febrile Infant Risk Stratification Tool (CA FIRST) using key components of the Roseville Protocol (ROS) and American Academy of Pediatrics (AAP) Clinical Practice Guideline (CPG). Expanded guidance was derived using a retrospective analysis of a cohort of 3527 febrile infants aged 7–90 days presenting to any Kaiser Permanente Northern California ED between 2010 and 2019 who underwent a core febrile infant evaluation.

Results

Melding ROS and AAP CPG algorithms in infants 7–60 days old, CA FIRST Algorithms had comparable performance characteristics to ROS and AAP CPG. CA FIRST enhancements included guidance on febrile infants 61–90 days old, high-risk infants, infants with bronchiolitis, and infants who received immunizations within the prior 48 hours. This retrospective analysis revealed that of 235 febrile infants 22–90 days old with respiratory syncytial virus and 221 who had fever in the 48 hours following vaccination, there were no cases of invasive bacterial infection.

Discussion

CA FIRST is a set of 13 algorithms providing a thoughtful and flexible approach to the febrile infant while minimizing unnecessary interventions.

Conclusions

CA FIRST Algorithms empower clinicians to manage most febrile infants. Algorithms are being modified as new data become available, imparting useful and ever-current educational information within a learning health care system.