BACKGROUND: The aim of this modeling study was to examine how casualty load affects the level of trauma care in multiple casualty incidents and to define the surge capacity of the hospital trauma assets. METHODS: The disaster plan of a U.S. Level I trauma center was translated into a computer model and challenged with simulated casualties based on 223 patients from 22 bombing incidents treated at an Israeli hospital. The model assigns providers and facilities to casualties and computes the level of care for each critical casualty from six variables that reflect the composition of the trauma team and access to facilities. RESULTS: The model predicts a sigmoid-shaped relationship between casualty load and the level of care, with the upper flat portion of the curve corresponding to the surge capacity of the trauma assets of the hospital. This capacity is 4.6 critical patients per hour using immediately available assets. A fully deployed disaster plan shifts the curve to the right, increasing the surge capacity to 7.1. Overtriage rates of 50% and 75% shift the curve to the left, decreasing the surge capacity to 3.8 and 2.7, respectively. CONCLUSION: This model defines the quantitative relationship between an increasing casualty load and gradual degradation of the level of trauma care in multiple casualty incidents, and defines the surge capacity of the hospital trauma assets as a rate of casualty arrival rather than a number of beds. The study demonstrates the value of dynamic computer modeling as an important tool in disaster planning.
BACKGROUND: The aim of this modeling study was to examine how casualty load affects the level of trauma care in multiple casualty incidents and to define the surge capacity of the hospital trauma assets. METHODS: The disaster plan of a U.S. Level I trauma center was translated into a computer model and challenged with simulated casualties based on 223 patients from 22 bombing incidents treated at an Israeli hospital. The model assigns providers and facilities to casualties and computes the level of care for each critical casualty from six variables that reflect the composition of the trauma team and access to facilities. RESULTS: The model predicts a sigmoid-shaped relationship between casualty load and the level of care, with the upper flat portion of the curve corresponding to the surge capacity of the trauma assets of the hospital. This capacity is 4.6 critical patients per hour using immediately available assets. A fully deployed disaster plan shifts the curve to the right, increasing the surge capacity to 7.1. Overtriage rates of 50% and 75% shift the curve to the left, decreasing the surge capacity to 3.8 and 2.7, respectively. CONCLUSION: This model defines the quantitative relationship between an increasing casualty load and gradual degradation of the level of trauma care in multiple casualty incidents, and defines the surge capacity of the hospital trauma assets as a rate of casualty arrival rather than a number of beds. The study demonstrates the value of dynamic computer modeling as an important tool in disaster planning.
Authors: Craig Goolsby; Kandra Strauss-Riggs; Michael Rozenfeld; Nathan Charlton; Eric Goralnick; Kobi Peleg; Matthew J Levy; Tim Davis; Nicole Hurst Journal: Am J Public Health Date: 2018-12-20 Impact factor: 9.308
Authors: S Huber-Wagner; R Lefering; M V Kay; J Stegmaier; P N Khalil; A O Paul; P Biberthaler; W Mutschler; K-G Kanz Journal: Eur J Med Res Date: 2009 Impact factor: 2.175
Authors: Michel Debacker; Ives Hubloue; Erwin Dhondt; Gerald Rockenschaub; Anders Rüter; Tudor Codreanu; Kristi L Koenig; Carl Schultz; Kobi Peleg; Pinchas Halpern; Samuel Stratton; Francesco Della Corte; Herman Delooz; Pier Luigi Ingrassia; Davide Colombo; Maaret Castrèn Journal: PLoS Curr Date: 2012-03-23