Statistical Modeling of Combat Mortality Events by Using Subject Matter Expert Opinions and Operation Iraqi Freedom Empirical Results from the Navy-Marine Corps Combat Trauma Registry

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Advances in autonomous aircraft technology are spurring research into the different roles these aircraft could fill. The Office of Naval Research (ONR) is pursuing an Innovative Naval Prototype of an autonomous cargo aircraft in response to a United States Marine Corps Universal Needs Statement. Since the use of such a vehicle to evacuate casualties after delivering supplies is an obvious extension, ONR initiated research into how the functional characteristics of an aircraft such as speed, range, capacity, and number available affect how the aircraft performs as a patient movement platform. To evaluate aircraft functional characteristics we executed experiments with a patient movement simulation that explicitly models treatment, evacuation, and mortality as patients flow from the point of injury through definitive care. The experiments provide data, which was used to develop a response surface model of estimated patient mortality as a function of the casualty evacuation system characteristics. This response surface will be useful for comparing competing systems when currently unknown constraints such as total cost of ownership, volume, area, and weight are applied.

Section: Discrete Event Simulationmentioning

confidence: 95%

Determining How Functional Characteristics of a Dedicated Casualty Evacuation Aircraft Affect Patient Movement and Outcomes

Anderson

Konoske

Davis³

et al. 2010

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“…Blankeney et al 16 provided details of three operational research tools developed to view capability projections of Canadian Defence Forces (CDF) and compared the values of disparate CDF capabilities. Mitchell et al 17 used statistical analysis tests to model combat mortality events. Their analysis of two years of data from Operation Iraqi Freedom (OIF) revealed that the timing of a high-risk mortality event can be estimated with good accuracy using a Weibull distribution.…”

Section: Introductionmentioning

confidence: 99%

Historical operational data analysis for defence preparedness planning

Shafi

Debie

Oliver

2016

Preparedness is an important function of defence planning that involves developing defence capabilities to deal with emergent situations relating to national defence and security. Preparedness planning relies on a number of inputs, including requirement analysis, to identify critical capability gaps. Modern data analysis can play an important role in identifying such future requirements. To this end, this paper presents an analytical study, consisting of both descriptive as well as predictive analysis, of historical defence operational data. The descriptive analysis component of the methodology focuses on identifying useful features in the collected data for building a predictive model. The predictive analysis investigates existing patterns in the data, including spatial and temporal trends. An artificial neural network based time series forecasting model is developed to predict future operations based on the identified features. The proposed methodology is applied to a defence operational data set, built from a number of unclassified sources relating to the historical operational deployments of the Australian Defence Force between 1885 and 2012. Implications are also discussed.

“…Unfortunately, research in this domain has been limited. A literature review identified only a single paper, 2 which presented survival curves for combat trauma casualties with life-threatening injuries. Those curves were based on a small dataset ( n = 160, with 26 deaths and 134 survivors) of Role II casualties.…”

Section: Introductionmentioning

confidence: 99%

Development of empirically based time-to-death curves for combat casualty deaths in Iraq and Afghanistan

Souza

Vickers

Zouris

et al. 2014

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The United States Department of Defense medical planners need survival-time estimates for anticipated patient streams associated with projected combat scenarios. Survival-time estimates should be grounded in empirical observations. Unfortunately, research in this domain has been limited to a single paper describing the development of died-of-wounds curves for combat casualties with life-threatening injuries. The curves developed from that research were based on a small dataset (n = 160, with 26 deaths and 134 survivors) of forward surgical (Role II) casualties and subject matter experts' judgments. This paper reports the first empirically based time-to-death curves for combat casualties based on a large sample. The results indicate that survival time varied across roles of care at which casualties died but was at most weakly associated with injury severity. Time-to-death curves were, therefore, developed for the overall study population of valid times to death and for Role I, Role II and Role III care. The log-logistic probability distribution provided the best representation of the survival times for the overall study population, while the log-normal distribution was the best choice for Role I, Role II and Role III care. The proposed time-to-death curves should refine the survival-time estimates used in combat medical logistics planning.