Share:


Simulating victim health state evolution from physical and chemical injuries in mass casualty incidents

    Mehdi Benhassine   Affiliation
    ; Ruben De Rouck Affiliation
    ; Michel Debacker Affiliation
    ; Ives Hubloue Affiliation
    ; Erwin Dhondt Affiliation
    ; Filip Van Utterbeeck Affiliation

Abstract

The field of discrete-event simulation for medical disaster management is relatively new. In such simulations, human victims are generated using pre-determined transitions from one health state to the next, based on a set of triggers that correspond to treatment or the clinical progression of untreated injuries or diseases. However, this approach does not account for subtle differences in clinical progression. We propose a parameter-based model to characterize the evolution of symptoms at first for physical and nerve agent chemical injuries. We used a Gompertz function to predict the time of death in trauma based on forensic data. Then we separately considered the effects of the chemical warfare agent sarin (GB) being the origin of the chemical injuries for the purpose of modelling a GB attack in a metro station. We emphasize that our approach can be extended to other CBRN threats pending knowledge of clinical progressions available in the literature for the purpose of casualty estimations. The intent is to provide an estimate of time to death without any treatment and overlay this model with a treatment model, improving the evolution of the health state. A modification for non-life-threatening injuries is included without losing generality. Improvement functions modelling medical treatment are proposed. We argue that the availability of injury scores vs mortality can greatly enhance the validity of the model.

Keyword : disaster medicine, discrete-event simulation, victim health state model, mass-casualty incidents, combined injuries

How to Cite
Benhassine, M., De Rouck, R., Debacker, M., Hubloue, I., Dhondt, E., & Van Utterbeeck, F. (2023). Simulating victim health state evolution from physical and chemical injuries in mass casualty incidents. New Trends in Computer Sciences, 1(2), 113–125. https://doi.org/10.3846/ntcs.2023.19458
Published in Issue
Dec 28, 2023
Abstract Views
137
PDF Downloads
81
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Ahuja, J., & Nash, S. (1967). The generalized Gompertz-Verhulst family of DIstributions. Sankhya, 29(2), 141–161.

Bellamy, R. (1984). The causes of death in conventional land warfare: Implications for combat casualty care research. Military Medicine, 149(2), 55–62. https://doi.org/10.1093/milmed/149.2.55

Benhassine, M., De Rouck, R., Debacker, M., Hubloue, I., Dhondt, E., & Van Utterbeeck, F. (2022a). Continuous Victim model for use in mass casualty incident simulations. In Proceedings of the 20th Industrial Simulation Conference (pp. 10–15). Eurosis-ETI, Ostend.

Benhassine, M., De Rouck, R., Debacker, M., Hubloue, I., Dhondt, E., & Van Utterbeeck, F. (2022b). Simulating the evacuation of a subway station after a sarin release. In Proceedings of the 36th European Simulation Conference (pp. 271–277). Porto, Portugal, EUROSIS-ETI.

Casagrande, R., Wills, N., Kramer, E., Sumner, L., Mussante, M., Kurinsky, R., McGhee, P., Katz, L., Weinstock, D. M., & Coleman, C. N. (2011). Using the model of resource and time-based triage (MORTT) to guide scarce resource allocation in the aftermath of a nuclear detonation. Disaster Medicine and Public Health Preparedness, 5 (S1), S98–S110. https://doi.org/10.1001/dmp.2011.16

Champion, H., Sacco, W., Copes, W., Gann, D., Gennarelli, T., & Flanagan, M. (1989). A Revision of the Traume Score. The Journal of Trauma: Injury, Infection and Critical Care, 29(5), 623–629. https://doi.org/10.1097/00005373-198905000-00017

Clark, D., Doolittle, P., Winchell, R., & Betensky, R. (2014). The effect of hospital care on early survival after penetrating trauma. Injury Epidemiology, 1(1), 24. https://doi.org/10.1186/s40621-014-0024-1

Cros, J., Alvarez, J., Sbidian, E., Charlie, P., & de la Grandmaison, G. (2013). Survival time estimation using Injury Severity Score (ISS) in homicide cases. Forensinc Science International, 233(1–3), 99–103. https://doi.org/10.1016/j.forsciint.2013.08.026

Curling, C., Burr, J., Danakian, L., Disraelly, D., Laviolet, L., Walsh, T., & Zirkle, R. (2010). Technical reference manual: Allied Medical Publication 8(c), NATO Planning Guide for the Estimation of Chemical, Biological, Radiological and Nuclear (CBRN), Casualties from Exposure to Specified Biological Agents (IDA Document D-4082). Institute for Defense Analyses.

De Rouck, R., Benhassine, M., Debacker, M. D., Van Utterbeeck, F., & Hubloue, I. (2023). Creating realistic nerve agent victim profiles for computer simulation of medical CBRN disaster response. Frontiers in Public Health: Disaster and Emergency Medicine, 11. https://doi.org/10.3389/fpubh.2023.1167706

De Rouck, R., Koghee, S., Debacker, M., Van Utterbeeck, F., Ullrich, C., Dhondt, E., & Hubloue, I. (2018). Simedis 2.0: On the road toward a comprehensive mass casualty incident medical management simulator. In Proceedings of the 2018 Winter Simulation Conference (pp. 2713–2724). https://doi.org/10.1109/WSC.2018.8632369

Debacker, M., Van Utterbeeck, F., Ullrich, C., Dhondt, E., & Hubloue, I. (2016). SIMEDIS: a Discrete-Event simulation model for testing responses to mass casualty incidents. Journal of Medical Systems, 40, Article 273. https://doi.org/10.1007/s10916-016-0633-z

Dempsey, W., & McCullagh, P. (2018). Survival models and health sequences. Lifetime Data Analysis, 24(4), 550–584. https://doi.org/10.1007/s10985-018-9424-9

El-Gohary, A., Alshamrani, A., & Al-Otaibi, A. (2013). The generalized Gomperts distribution. Applied Mathematical Modelling, 37, 13–24. https://doi.org/10.1016/j.apm.2011.05.017

Gompertz, B. (1825). On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philosophical Transactions of the Royal Society of London, 115, 513–585. https://doi.org/10.1098/rstl.1825.0026

Holcomb, J., Stansbury, L., Champion, H., Wade, C., & Bellamy, R. (2006). Understanding combat casualty care statistics. Journal of Trauma, 60(2), 397–401. https://doi.org/10.1097/01.ta.0000203581.75241.f1

Hussain, B., Vyawahare, M., & Pate, R. (2020). Correlation of injury severity score with survival time in fatal road traffic accidents in Central Indian population. Medico Legal Update, 20(2), 37–41.

McDaniel, M., Keller, J., White, S., & Baird, A. (2019). A whole-body mathematical model of sepsis progression and treatment designed in the biogears physiology engine. Frontiers in Physiology, 10, Article 1321. https://doi.org/10.3389/fphys.2019.01321

North Atlantic Treaty Organization. (2018). AMedP-7.5-1, Edition A, Version 1, Technical Reference Manual NATO Planning Guige for the Estimation of CBRN Casualties. NATO Standardization Office, Brussels, Belgium.

O’Reilly, G. M., Cameron, P. A., & Joshipura, M. (2012). Global trauma registry mapping: A scoping review. Injury, 43(7), 1148–1153. https://doi.org/10.1016/j.injury.2012.03.003

Petridou, E., & Antonopoulos, C. (2017). Injury epidemiology. In S. R. Quah (Ed.), International encyclopedia of public health. Elsevier. https://doi.org/10.1016/B978-0-12-803678-5.00233-2

Raoof, A., Meera Devi, T., Neha, S., & Chetri, D. (2019). Pattern and injury severity scors in thoraco-abdominal trauma: A cross-sectional study in medicolegal autopsy cases. Indian Journal of Forensic and Community Medicine, 6(1), 18–23. https://doi.org/10.18231/2394-6776.2019.0006

Raux, M., Thicoïpé, M., Wiel, E., Rancurel, E., Savary, D., David, J. S., Berthier, F., Ricard-Hibon, A., Birgel, F., & Riou, B. (2006). Comparison of respiratory rate and peripheral oxygen saturation to assess severity in trauma patients. Intensive Care Medicine, 32(3), 405–412. https://doi.org/10.1007/s00134-005-0063-8

Sacco, W., Navin, M., Fiedler, K., Waddell, R., Long, W., & Buckman, R. (2008). Precise formulation and evidence-based application of resource-constrained triage. Academic Emergency Medicine, 12(8), 759–770. https://doi.org/10.1197/j.aem.2005.04.003

Sahu, M. R., Mohaty, M. K., Sasmal, P. K., Radhakirshnan, R. V., Mohanty, C. R., Shaji, I. M., Naveen, I., & Parida, M. (2021). Epidemiology and patterns of road traffic fatalities in India pre- and post-motor vehicle (Amendment) act 2019: An autopsy-based study. International Journal of Critical Illness and Injury Science, 11(4), 198–203. https://doi.org/10.4103/ijciis.ijciis_51_21