Modeling Dynamic Casualty Mortality Curves in the Tactical Medical Logistics (TML+) Planning Tool

Mitchell, Ray; Galarneau, Michael R.; Hancock, Bill; Lowe, Douglas

doi:10.1037/e451652006-001

Cited by 7 publications

(13 citation statements)

References 2 publications

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“…5,14 The panel was conducted in a Delphi-similar manner, where a presumed number of battlefield casualties with various PCs developed by the Defense Medical Standardization Board and used in NHRC's M&S efforts were assumed to be initially injured. 15 For each PC and treatment assumption, the panel was asked to estimate the fraction of casualties that would be expected to survive after specified time epochs, such as 10 minutes, 30 minutes, 1 hour, 3 hours, etc., in the various interventions or MTFs.…”

Section: Initial Modeling Results Inferred From Subject Matter Expertmentioning

confidence: 99%

“…Figure 1 shows subjective observations and estimated results from a 2003 medical doctor SME panel 5,7 for a patient with a high risk of mortality injury. In the figure, the casualty is presumed to receive a series of medical interventions for a LT injury: 1. by self or a buddy at the point of injury (labeled 'no treatment'); 2. by a field-level corpsman (first responder) after a 10-minute delay; 3. by the Battalion Aid Station (BAS) MTF after a 30-minute delay; and, finally, 4. by a Shock Trauma Platoon (STP)/Forward Resuscitative Surgical System (FRSS) MTF after 30 minutes of delay.…”

Section: Introductionmentioning

confidence: 99%

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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

Mitchell

Parker

Galarneau

et al. 2010

Journal of Defense Modeling & Simulation

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In 12 Navy-Marine Corps medical logistics studies and analyses conducted by the Naval Health Research Center (NHRC) and Teledyne Brown Engineering (TBE) over the past 5 years, estimates of battlefield mortality have been a major metric of interest to medical planners. An ongoing concern is how mortality is related to delays in treatment for medical logistics reasons. In this paper, we describe how NHRC and TBE have been developing a statistically based model for mortality since 2003, first starting with panel results from a group of military medical doctors and continuing here with an analysis of empirical injury data from Operation Iraqi Freedom (OIF). The panel results and statistical analysis of life-threatening injury data in OIF from early 2004 to mid-2006 indicate that the Weibull distribution describes the timing of high risk of mortality events in a reasonable manner within surgical medical treatment facilities. The quest for a best-fitting probability distribution with parameters dependent on the casualty flow chain of treatment and evacuation in the theater is ongoing. In reality, combining analytical and subject matter expert (SME) results to model mortality is necessary given the breadth of theater medical delivery systems and general paucity of adequate empirical data in many of the segments of patient flow.

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Section: Initial Modeling Results Inferred From Subject Matter Expertmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mitchell

Parker

Galarneau

et al. 2010

Journal of Defense Modeling & Simulation

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“…HIT will also produce a report summarizing the billets/rates of lost personnel due to incapacitation (Figure 6). With respect to medical response, HIT produces output that TML+ users are accustomed to seeing (Mitchell, 2004). At the highest level, statistics are reported on the number of patients that are Killed in Action (KIA), Returned to Duty (RTD), or Evacuated from the ship for further treatment (Figure 7).…”

Section: Resultsmentioning

confidence: 99%

“…TML+ provides the capability to determine the status of patients as a function of time. As these personnel are returned to duty they can be assigned roles in the ongoing damage control response within the manning module (Mitchell, 2004). TML+ has the capability to provide patient outcome for up to 72 hours.…”

Section: Practice Innovationmentioning

confidence: 99%

Applying Modeling and Simulation to Predict Human Injury due to a Blast Attack on a Shipboard Environment

Jacobs¹,

Young

Champion

et al. 2012

Proceedings of the Human Factors and Ergonomics Society Annual

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Computer models simulating blast effects on ship personnel are needed, but thus far, development of models has focused on simulating blast effects on ship structure and equipment. Thus, capability gaps exist in predicting the type and severity of injuries from surface or underwater weapon impact, estimating medical response requirements, and determining outcomes of patients. The Human Injury & Treatment (HIT) model addresses these gaps. Algorithms are utilized for scoring the type and severity of injuries predicted, using a variety of existing and developing injury models. Additional algorithms determine the post-injury level of incapacitation by evaluating how a physical impairment can impact performance of a task. A manning model simulates movement of personnel aboard the ship (Young, Allen, & Minks, 2011). It functions iteratively with the Tactical Medical Logistics (TML+) code, a medical response model predicting resource utilization and patient outcomes (Mitchell, 2004). Impact: HIT will help the Navy and commercial maritime interests anticipate medical response requirements resulting from blast attacks to a ship, and understand the impact of personnel loss on the crew’s ability to perform damage control.

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“…Nevertheless, the survival curves in the paper reflected the empirical data of early combat operations in Iraq as well as the experiences of military medical doctors. 3 This paper reports the development of empirically based time-to-death curves for combat trauma casualties based on a large sample. The curves are based on deaths in Iraq and Afghanistan combat operations between 2002 and 2011.…”

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

Journal of Defense Modeling & Simulation

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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.

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Modeling Dynamic Casualty Mortality Curves in the Tactical Medical Logistics (TML+) Planning Tool

Cited by 7 publications

References 2 publications

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

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

Applying Modeling and Simulation to Predict Human Injury due to a Blast Attack on a Shipboard Environment

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

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