Symptom-based, algorithmic approach for handling the initial encounter with victims of a potential terrorist attack

Area of Science:

• Emergency Medicine
• Public Health
• Disaster Preparedness

Background:

• Terrorist attacks, industrial accidents, and emerging infections pose significant public health risks.
• Effective initial triage is crucial for managing mass casualty incidents and preventing secondary contamination.
• Existing triage methods may not adequately address the unique challenges of chemical, biological, radiological/nuclear, and explosive (CBRNE) events.

Purpose of the Study:

• To develop and present symptom-based triage algorithms for the initial encounter with victims of terror attacks.
• To ensure early recognition, prevent contamination, facilitate early antidote administration, and manage unstable/contaminated victims.
• To provide force protection and address similar risks posed by industrial accidents and emerging infections.

Main Methods:

• Algorithms were developed using military and civilian references.
• Theoretical patients from a CBRNE victim simulator were used for testing and adjustment.
• Algorithms were formatted into cards and underwent expert academic review.

Main Results:

• Six interconnected, symptom-based algorithms were created and presented.
• The 'attack' algorithm differentiates overt/covert attacks and categorizes patients as stable/unstable.
• Algorithms guide patients to specific pathways (e.g., 'Dirty Resuscitation,' 'Chemical Agent,' 'Biological Agent,' 'Suicide Bomb/Blast/Explosion,' 'Radiation Dispersal Device').

Conclusions:

• A civilian, symptom-based algorithmic approach for initial victim encounters was successfully created.
• Future studies will evaluate algorithm usability with theoretical cases and field drills.
• Further research will assess the effectiveness of teaching modalities for these algorithms.