Symptom-based, algorithmic approach for handling the initial encounter with victims of a potential terrorist attack.
OBJECTIVE
This study intended to create symptom-based triage algorithms for the initial encounter with terror-attack victims. The goals of the triage algorithms include: (1) early recognition; (2) avoiding contamination; (3) early use of antidotes; (4) appropriate handling of unstable, contaminated victims; and (5) provisions of force protection. The algorithms also address industrial accidents and emerging infections, which have similar clinical presentations and risks for contamination as weapons of mass destruction (WMD).


METHODS
The algorithms were developed using references from military and civilian sources. They were tested and adjusted using a series of theoretical patients from a CD-ROM chemical, biological, radiological/nuclear, and explosive victim simulator. Then, the algorithms were placed into a card format and sent to experts in relevant fields for academic review.


RESULTS
Six inter-connected algorithms were created, described, and presented in figure form. The "attack" algorithm, for example, begins by differentiating between overt and covert attack victims (A covert attack is defined by epidemiological criteria adapted from the Centers for Disease Control and Prevention (CDC) recommendations). The attack algorithm then categorizes patients either as stable or unstable. Unstable patients flow to the "Dirty Resuscitation" algorithm, whereas, stable patients flow to the "Chemical Agent" and "Biological Agent" algorithms. The two remaining algorithms include the "Suicide Bomb/Blast/Explosion" and the "Radiation Dispersal Device" algorithms, which are inter-connected through the overt pathway in the "Attack" algorithm.


CONCLUSIONS
A civilian, symptom-based, algorithmic approach to the initial encounter with victims of terrorist attacks, industrial accidents, or emerging infections was created. Future studies will address the usability of the algorithms with theoretical cases and utility in prospective, announced and unannounced, field drills. Additionally, future studies will assess the effectiveness of teaching modalities used to reinforce the algorithmic approach.
Subbarao
Italo
I
Department of Emergency Medicine, Lehigh Valley Hospital and Health Network, Allentown, Pennsylvania, USA. italo_subbarao@yahoo.com
Johnson
Christopher
C
Bond
William F
WF
Schwid
Howard A
HA
Wasser
Thomas E
TE
Deye
Greg A
GA
Burkhart
Keith K
KK
D000465:Algorithms; D023482:Bioterrorism / Q000517:prevention & control; D002985:Clinical Protocols; D003666:Decontamination / Q000379:methods / Q000592:standards; D004632:Emergency Medical Services / Q000379:methods* / Q000592:standards*; D005107:Explosions; D006801:Humans; D018788:Radioactive Hazard Release / Q000517:prevention & control; D012151:Resuscitation / Q000379:methods / Q000592:standards; D023481:Terrorism / Q000517:prevention & control*; D014218:Triage / Q000379:methods / Q000592:standards