(A47) Pediatric Mass-Casualty Triage: The New York City Approach

Abstract

IntroductionA Mass-Casualty Event (MCE) involving pediatric victims could overwhelm existing pediatric resources. Therefore, early recognition of critically ill infants and children is essential for proper distribution among pediatric capable hospitals. However, emergency medical services (EMS) personnel have limited experience with pediatric assessments, and less with pediatric mass-casualty triage (MCT). To address these gaps, the New York City (NYC) Pediatric Disaster Coalition (PDC) in collaboration with the Fire Department (FDNY) and Office of Emergency Management, made simple alterations to the START-based NYC-MCT Algorithm that can be rapidly and accurately applied by EMS personnel in the field with minimal additional education and preparation, obviating the requirement for extensive and expensive retraining.MethodsThe PDC includes experts in pediatric emergency preparedness, emergency medicine, critical care, and trauma surgery in NYC, as well as DOHMH, FDNY-OMA, and OEM. Its Triage Subcommittee determined the minimum essential pediatric alterations to the Algorithm, which then was tested by FDNY-EMS.ResultsAfter focused literature review and multiple draft revisions aimed to maximize pediatric benefit yet minimize unnecessary complexity, the Algorithm was modified to ensure that: (1) five rescue breaths will be provided to infants or children prior to being categorized as Dead or Expectant; (2) infants under 12 months old will be categorized as Critical and receive priority transport, and (3) children initially categorized as Delayed or Minor will be uptriaged to a new Urgent (Orange) category to receive such care in a rapid manner. To date, > 3,000 FDNY personnel have been trained in its use, and tested its accuracy using tabletop scenarios. Mean accuracy is 80–90%.ConclusionsThe model is an effective, multidisciplinary approach to planning. Minimum alterations to the Algorithm were adopted by the regional EMS system. The Modified Algorithm improves identification of critically ill infants and children. This approach could be adopted by other large urban centers.