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More than a decade after the Persian Gulf War, the United States went to war against Iraq for the second time. This conflict grew out of fear and distrust of Saddam Hussein’s suspected chemical and nuclear weapons of mass destruction. A highly inaccurate intelligence assessment contributed to the fear of Iraq’s phantom WMD. Except for Britain, the permanent members of the Security Council opposed the war. George W. Bush put together a coalition of the willing to invade Iraq in 2003. The opening, conventional phase of the intervention went well with Baghdad seized in three weeks. But soon the Pentagon faced a raging insurgency in much of the country, which was divided along sectarian lines of the Sunni and Shiite Islamic sects fighting each other while attacking the US and allied forces. The intractable insurgency proved complex and deeply rooted. By 2006, the Pentagon looked as it was headed toward defeat. Three factors contributed to a turnaround in its fortunes. Chief among these was the Awakening movement in which Sunni Arab tribal leaders crossed over to the American side because of their resentment of Salafi-jihadi militants. The Bush White House adopted a finely-tuned counterinsurgency strategy and surged 28,500 more troops into the fray. During the intense fighting, Bush pushed several elections and a constitution to entrench democracy. Gun battles and street bombings greatly subsided by the time Barack Obama withdrew all US combat troops in 2011.
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.
Conclusion:
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.