INCREASING THE RESOLUTION OF SIMULATED COMBAT TRAUMA INJURIES IN A HIGH LEVEL ARCHITECTURE (HLA) ENVIRONMENT

by

CPT GREGORY STUART CREECH B.S. Methodist College, 1988

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science

in the Department of Industrial Engineering and Management Systems in the College of Engineering

at the University of Central Florida Orlando, Florida

Spring Term 1999

DISTRIBUTION STATEMENT A Approved for Public Release

Distribution Unlimited

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ABSTRACT

This research develops a technique that leverages Operational Requirements-

based Casualty Assessment (ORCA) generated patient conditions for use in the Combat

Trauma Patient Simulation (CTPS). The research paves the way for enhancing the

resolution of combat trauma injuries in a High Level Architecture (HLA) environment.

ORCA is U.S. Government-owned software developed by the Crew Casualty

Working Group (CCWG). The ORCA methodology calls for the initial creation of a

"medical casualty" in order to determine the affects on the body's ability to perform

certain military tasks. The Alpha + version upon which this paper is based had working

modules for only five of seven planned injury-inducing insult types. These are

Penetrators (fragments), Blast overpressure, Thermal fluence, Toxic gases (military

agents), and Directed Energy.

The CTPS research team is prototyping and evaluating a training and analysis

HLA federation that realistically simulates the emergency medical treatment process

from the time of injury through initial treatment at a field hospital. The goals of the

project are to decrease the deaths due to combat wounds by having better trained medical

staffs and to provide a mechanism for analysis and for test and evaluation (T&E) of

issues in casualty medical treatment. In addition to ORCA, a central component of this

system is a physical simulation of a casualty (an instrumented mannequin), the Human

Patient Simulator (HPS).

Products of this research include a methodology and techniques for generating

ORCA injuries and storing the injury profiles offline in a database. This database can

later be read by CTPS to enhance the injury data available on a given casualty. Future

work can automate this process.

This research also discusses a prototype injury Simulation Object Model (SOM),

that objectifies the ORCA output data. Additionally, a prototype Federation Object

Model (FOM) is presented. Researchers plan to use this FOM in future phases of the

CTPS program. Finally, the research highlights the relevance of such object models to

the possible addition of future, more visually oriented, components to the CTPS.

ACKNOWLEDGEMENTS

The Combat Trauma Patient Simulation (CTPS) project is sponsored by the

Department of Defense (DOD), Live Fire Test and Evaluation Office, under Mr. Jim

O'Bryon, and managed by the U.S. Army Simulation, Training, and Instrumentation

Command, under the supervision of Ms. M. Beth H. Pettitt. The Institute for Simulation

and Training (1ST) CTPS project team is managed by Mr. Brian Goldiez. I gratefully

acknowledge their support.

IV

TABLE OF CONTENTS

1. CHAPTER ONE

1.1 INTRODUCTION 1

1.2 ISSUES IN COMBAT MEDICAL TRAINING 2

1.2.1 WARTIME CASUALTY FLOW 2

1.2.2 TRADITIONAL TRAINING METHODS 2

1.3 Do MEDICAL SIMULATIONS WORK? 5

1.3.1 BACKGROUND 5

1.3.2 SPECIFIC CASE STUDY 6

1.4 CURRENT USE OF MEDICAL SIMULATIONS 7

1.4.1 PHYSICAL SIMULATIONS 7

1.4.2 SOFTWARE MODELS (VIRTUAL AND CONSTRUCTIVE SIMULATIONS) 8

1.4.3 MILITARY APPLICATIONS 11

1.5 INTEROPERABILITY IN A HIGH LEVEL ARCHITECTURE (HLA) ENVIRONMENT 11

1.6 COMBAT TRAUMA PATIENT SIMULATION (CTPS) 13

1.7 Focus OF RESEARCH EFFORT 14

2. CHAPTER TWO

2.1 INJURY GENERATION AND CATEGORIZATION METHODS 16

2.1.1 DEPMEDS CODES 16

2.1.2 ABBREVIATED INJURY SCALE (AIS) CODES 17

2.2 ANALYSIS OF PHASE ICTPS 18

2.2.1 CTPS INFORMATION FLOW 19

2.2.2 INJURY GENERATION 20

2.2.3 PHASE I INJURY TYPES 21

2.2.4 ECC PHASE I MODIFICATIONS 22

2.3 ANALYSIS OF ORCA 22

2.3.1 PENETRATION INSULT OVERVIEW 24

2.3.1.1 PENETRATION INPUT PARAMETERS 24

2.3.1.2 PENETRATION INSULT OUTPUT 25

2.3.2 BLAST INSULT OVERVIEW 26

2.3.2.1 BLAST INPUT PARAMETERS 26

2.3.2.2 BLAST INSULT OUTPUT 27

2.3.3 THERMAL EXPOSURE INSULT 27

2.3.3.1 THERM AL EXPOSURE INPUT PARAMETERS.... 27

2.3.3.2 THERMAL EXPOSURE INSULT OUTPUT 28

2.3.4 Toxic GAS INSULT OVERVIEW 28

2.3.4.1 Toxic GAS INPUT PARAMETERS 28

2.3.4.2 Toxic GAS INSULT OUTPUT 28

VI

2.3.5 DIRECTED ENERGY INSULT OVERVIEW 29

2.3.5.1 DIRECTED ENERGY INPUT PARAMETERS 29

2.3.5.2 DIRECTED ENERGY INSULT OUTPUT 29

2.3.6 SUMMARY OF ORCA OUTPUT 30

2.3.6.1 DATA STRUCTURE 30

2.3.6.2 POSSIBLE ORCA ATTRIBUTES 31

2.4 MYSTECHWORK 32

2.4.1 MYSTECH PHYSIOLOGICAL SOM 32

2.4.2 MYSTECH HUMAN RESPONSE TO STIMULI SOM 33

2.4.3 MYSTECH FOM 35

2.5 CHAPTER SUMMARY 35

2.6 RESEARCH QUESTIONS 36

3. CHAPTER THREE

3.1 RESEARCH METHODOLOGY 37

3.2 INJURY GENERATION USING ORCA 38

3.3 THE INJURY PROFILE DATABASE 42

3.4 INJURY SOM DEVELOPMENT 45

3.5 TESTING THE MODELS 48

3.5.1 PRE-TEST PHASE 48

3.5.2 TEST PHASE 48

3.6 FOM DEVELOPMENT 50

Vll

4. CHAPTER FOUR

4.1 CTPS PHASE II TESTING WITH ORCA 52

4.1.1 THE TEST DATABASE 52

4.1.2 THE TEST 53

4.2 CTPS PHASE III VERSION 0 FOM 56

5. CHAPTER FIVE

5.1 CONCLUSION 59

5.2 RECOMMENDATIONS FOR FUTURE RESEARCH 61

6. CHAPTER SIX

6.1 VISUAL LIMITATIONS OF CTPS 62

6.2 VISUALLY-ORIENTED MEDICAL SIMULATIONS 64

6.3 THE NEED FOR FURTHER EXPANSION OF THE MYSTECH FOM 65

APPENDIXES 67-144

REFERENCES 145-147

Vlll

LIST OF APPENDIXES

APPENDIX A: ORCA PENETRATION INSULT OUTPUT 67-69

APPENDIXB: OBC/OBJECTNAME TABLE 70-81

APPENDIX C: INJURY SOM 82-96

APPENDIX D: PROTOTYPE INJURY PROFILE DATABASE 97-99

APPENDIXE: EXPANDED TEST INJURY PROFILE DATABASE 100-102

APPENDIX F: PATIENT INJURY DATA FROM CTPS TEST RUNS 103-118

APPENDIX G: CTPS PHASE III VERSION 0 FOM 119-144

IX

LIST OF TABLES

TABLE 1: PATIENT ATTRIBUTE DETAILS 21

TABLE 2: WOUND SCENARIOS AND TRAUMA TYPES 22

TABLE 3: PARTIAL PROTOTYPE INJURY DATABASE 41

TABLE 4.1: INJURY SOM - PARTIAL OBJECT CLASS STRUCTURE TABLE 46

TABLE 4.2: INJURY SOM - ATTRIBUTE/PARAMETER TABLE 47

TABLE 5: INJURY SOM INTEGRATION INTO MYSTECH FOM 57

LIST OF FIGURES

FIGURE 1: CTPS PHASE I ARCHITECTURE 19

FIGURE 2: ORCA TAXONOMY WITH AREA OF RESEARCH INTEREST 23

FIGURE 3: EXAMPLE IMPLEMENTATION OF ORCA HIERARCHICAL DESIGN 30

FIGURE 4: PROPOSED CTPS INJURY GENERATION MODEL USING ORCA 39

FIGURE 5: CTPS TEST CONFIGURATION 49

FIGURE 6: RTI C++ INTERFACE 50

FIGURE 7: EXAMPLE PATIENT INJURY PROFILE FROM CTPS TEST RUN 54

XI

LIST OF ACRONYMS

AAR AIS ARL

After Action Review Abbreviated Injury Scale Army Research Laboratory

BV Blood Vessel

CCWG CTPS

Crew Casualty Working Group Combat Trauma Patient Simulation

DEPMEDS DMSO DOD

Deployable Medical Systems Defense Modeling and Simulation 0 Department of Defense

ECC Electronic Casualty Card

FOM Federation Object Model

GUI Graphic User Interface

HOH HOS HLA HPS

Hollow Organ - Hard Hollow Organ - Soft High Level Architecture Human Patient Simulator

ISS IST

Injury Severity Score Institute for Simulation and Training

JMSL JTCG

Jackson Medical Scenario Library Joint Technical Coordinating Group

LC LCD LD

Line of Contact Liquid Crystal Display Line of Departure

Xll

MAIS MERLIN METI MILES

OBC OMDT ORCA

Maximum AIS Medical Readiness Learning Initiative Medical Education Technologies Inc. Multiple Integrated Laser Engagement System

ORCA Body Component Object Model Development Tool Operational Requirements-based Casualty Assessment

PATSIM Patient Simulator

RTI RTI

SO SOM STRICOM

Run Time Infrastructure (pertaining to HLA) Research Triangle Institute

Soft Organ Simulation Object Model Simulation Training and Instrumentation Command

T&E TMC

UCF USAARL

Test and Evaluation Troop Medical Clinic

University of Central Florida U.S. Army Aeromedical Research Laboratory

VMT VMET-TPS VR

Virtual Medical Trainer Virtual Medical Trainer Virtual Reality

Trauma Patient Simulator

xni

CHAPTER 1

1.1 Introduction

Current combat medic training scenarios lack realism, standardization, depth

(through entire battlefield evacuation process), and an accurate playback mechanism for

After Action Review (AAR) purposes. These shortcomings of the simulated medical

component result in medical personnel lacking in many areas of combat casualty care

(Rajput, Goldiez, & Petty, 1997). Also, the future of traditional medical training

methods, such as using traumatized animals as patients, is in jeopardy (Stansfield,

Shawver, & Sobel, 1998).

Although current medical training simulators may fall far behind their real world,

live-tissue alternatives, there is hope for the future. This is evident in the statement by

Satava and Jones (1997a), concerning medical simulations. Satava and Jones stated that

cartoon-like medical simulations are likely to follow the same evolutionary path as their

flight simulator counterparts. Flight simulators took approximately 40 years to reach

their ultra-realistic current state.

1.2 Issues in Combat Medical Training

1.2.1 Wartime Casualty Flow

In wartime, the number of casualties may grow so quickly that all levels of care

are either "pushed down" to lower levels, providing someone knows and can perform the

appropriate procedures, or they are not done in a timely manner. The ability to provide

continuous combat casualty care forward of the line of departure (LD)/line of contact

(LC) is a considerable challenge. The pre-hospital phase of caring for combat casualties

is critically important, since up to 90% of combat deaths occur before the casualty ever

reaches a medical treatment facility (Sobczak & Freshour, 1997). During intense periods

of fighting, it is easy to see how the patient to medic ratio may become so large that even

a seasoned medic would panic, resulting in a lower quality of combat medical care and

more deaths due to combat wounds.

1.2.2 Traditional Training Methods

Current training methodology focuses on live training exercises: medics either

practice stabilization and wound care on animals (e.g. shooting and treating a goat) or via

moulage, where live soldiers are given highly realistic "fake" wounds via special make

up (Stansfield et al., 1998). Traditionally, medical combat training scenarios have either

been scripted and given on paper or read aloud to a medic at the casualty site. In the case

of the wounded animal, live tissue serves to enhance realism, while the instructor keeps

the scenario on track with scripted inputs. In such cases, animal physiological and

pathophysiological processes are often very similar to those of a human. However,

varying degrees of difference in overall anatomy, coupled with the visually induced

psychological aspect of knowing that the patient is non-human, produce an overall affect

of decreased scenario realism. Also, over the years these methods have met with

increased scrutiny by animal-rights activists.

In the case where the "patient" is another perfectly healthy individual, the trainee

may be able to realistically go through the motions of patient care, such as checking for

breathing and pulse, and perhaps even performing a more difficult procedure such as

administering intravenous fluid therapy (starting an IV). Since the "patient" is healthy,

the instructor must interject any abnormal parameters into the scenario, in verbal or

written fashion. In some cases, a mannequin, with moulaged injuries, is used in a similar

manner. The non-instrumented mannequin is used primarily for injury identification,

with limited capability to perform minor procedures such as application of splints or

dressings. In either case (live soldier or mannequin), written or verbal input on vital

signs and other indications of internal injury must be interjected into the scenario and the

medic does not get to practice realistic wound care. This type of input into a scenario,

particularly verbal, has inherent problems. Variations in simulated battlefield sounds,

instructor's voice (pitch, volume, and accent), and instructor or trainee's ability to read all

impact on the overall realism and standardization of the training session. Additionally,

unless the training sessions are videotaped, there is no detailed, unbiased account for after

action purposes.

The military's use of the Electronic Casualty Card (ECC), which interacts with

the MILES II System (Tinawi & Escobedo, 1996), attempts to overcome some of the lack

of realism inherent to these training methods by allowing medic interaction with a live

simulation casualty via a medical treatment gun. When utilizing the MILES II System,

each soldier participating in a live simulation wears a sensor-laden harness. Weapons are

fitted with a laser device for transmitting weapon-specific information. The MILES II

sensors may be "hit" by these weapons, simulating a battlefield injury. Once a MILES

injury is sustained, the severity of the wound and the prognosis for the soldier's near term

condition are displayed on the LCD display on the soldier's chest. A medic is then able

to "shoot" the casualties harness with a Medical Control Device. "Shooting" enables the

ECC software to initialize a treatment routine, whereby the medic can select the most

appropriate treatment for the injury received. This technique trains the combat medic in

only one area, identification of proper treatment, with no opportunity for realistically

treating the patient.

1.3 Do Medical Simulations Work?

1.3.1 Background

Kubala and Warnick (1979) found that knowing exactly what to expect in combat

reduces fear and stress. Kubala and Warnick further stated, quite obviously, that training

for combat should be done as realistically as possible. Soldiers should be introduced into

fearful situations gradually, with proper coaching, to make sure that they make the

correct responses when frightened in a combat situation. The recommended combat

training program maximizes realism and thoroughly trains critical tasks to the extent that

they become almost automatic, regardless of how severe conditions may become. This

type of training brings about a strong sense of situational confidence. There are key

training conditions that form the basis for a confidence-strengthening training program.

Kubala and Warnick list these as high degrees of fidelity for job performance execution

and performance feedback, coupled with repeated execution of the tasks under realistic

conditions. These training conditions apply to all soldiers, from tank crewmen to medics.

There are a number of reasons why simulations can provide the conditions

necessary for successful, confidence-strengthening, combat training. First, current and

ongoing technological advances in the computer arena mean greater fidelity. In essence,

fidelity is constrained by budget and acceptability of the user interface (a technological

limitation). Second, computers make wonderful elephants. They truly never forget. The

standardization and after-action capabilities inherent in many simulations make them

perfect "repetitious trainers". Lastly, high-fidelity standardized training simulators are

very capable of replicating realistic conditions, without placing participants in harm's

way.

1.3.2 Specific Case Study

Use of a simulator to train fiber-optic intubation resulted in improved

performance versus traditional teaching methods (Ovassapian, Yelich, Dykes, & Golman,

1988). The study compared a graduated training program with that of a traditional

teaching method. Thirty-two anesthesia trainees were randomly assigned to two groups.

One group used a graduated program involving practice on a bronchoscopy teaching

model (a physical simulator), followed by visual exposure to live tissue (epiglottis and

vocal cords) in recovering patients, and final performance of fiberoptic nasotracheal

intubation in awake sedated patients. The other group used the program involving

demonstration (on a patient) of one intubation by the instructor, followed by performance

of intubation (by the trainee) in awake sedated patients. Successful nasotracheal

intubation was accomplished significantly more often by the trainees in the graduated

program (86 out of 96 (89.6%) v. 64 out of 96 (66.5%) (P less than 0.01).

1.4 Current Use of Medical Simulations

There are a number of potential medical training devices either currently on the

market or in various stages of development. Basically, these break down into two distinct

areas, hardware-based and software simulation products. Hardware products allow

realistic human interaction, such as by touch and feel. Software simulation products

allow simulation of a patient's physiology and pharmacological, as well as treatment

through a computer model, utilizing a graphical user interface.

1.4.1 Physical Simulations

There are many physical simulation products currently on the market, including the

Leiden simulator (Rajput & Fang, 1998). The Leiden Anesthesia Simulator creates a

training environment for anesthetists. It makes use of a standard anesthesia machine and

monitoring devices. A mannequin attached to an electro-mechanical lung machine

represents a simulated patient. A physiological and pharmacological model controls the

patient's simulated parameters.

The Eagle Patient Simulator (Rajput & Fang, 1998) consists of a full body

mannequin, operator station, interface cart housing the electronic and pneumatic drive

equipment, and software describing patient physiology. Its features include thirty

simulated cardiovascular, pulmonary, and metabolic events and simulated physiological

reactions to 85 drugs. It also allows for scenario generation by the instructor/operator.

Another full body mannequin, the Medical Education Technologies (METI),

Inc./University of Florida Human Patient Simulator (HPS) is a full scale, fully

interactive, life-like simulator (Rajput et al., 1997). Clinical features include palpable

pulses, self-regulating control of breathing, heart and breath sounds, electrocardiograms,

pulmonary artery pressure. Physiological and pharmacological models direct simulated

patient responses (both normal and pathophysiological) to drugs, mechanical ventilation,

and other medical therapies. It is being utilized as part of a larger scale simulation, the

Combat Trauma Patient Simulation (CTPS), discussed in greater detail later in this

chapter. More detailed discussion on the inner workings of the CTPS may be found in

Chapter Two.

The Special Operations Medical Training Facility at Fort Bragg, North Carolina,

recently acquired a HPS. This simulator is being evaluated as a replacement for some of

the live animal models, as well as an enhancement to the training program.

1.4.2 Software Models (Virtual and Constructive Simulations)

Some current medical simulations are attempting to take advantage of advances in

computer graphics and visualization, by creating virtual training environments. The

Virtual Medical Trainer - Trauma Patient Simulator (VMT-TPS) (Research Triangle

Institute [RTI], 1998) is such a system. The VMT-TPS, formerly known simply as VMT,

allows training of pre-hospital emergency care and is specially suited for multi-trauma

patient assessment. It provides a graphic, on screen portrayal of a synthetic casualty and

provides some limited ability to offer treatment. It serves as an excellent initial wound

assessment tool, due to its visual nature. The ability to display bleeding at critical points

on the body is a key feature for its use as a casualty assessment tool.

Another model useful in initial assessment is MediSim (Stansfield et al., 1998).

MediSim is a prototype virtual reality training system, targeted primarily at training the

combat medic, who has the responsibility to stabilize and sort multiple casualties for

evacuation to field hospitals. This focus differentiates MediSim from some other virtual

reality medical trainers, whose primary goal is to train a specific procedure or task. The

goal of the MediSim trainer is to train rapid situational assessment and decision-making

under highly stressful conditions. Realistic visual queues are key to MediSim's ability to

train initial assessment of combat trauma.

Operational Requirements-based Casualty Assessment (ORCA) is U.S.

Government-owned software developed by the Crew Casualty Working Group (CCWG)

(Klopcic, Neades, & Tauson, 1998). The CCWG is in the fifth year of a five-year joint

project between the Joint Technical Coordinating Group (JTCG) for Munitions

Effectiveness and the JTCG on Aircraft Survivability. The major product of the project

is a comprehensive methodology, with supporting data, for assessing the effects of

conventional munitions upon the ability of personnel to perform military tasks. The

tangible result of the project is the computer program called ORCA. In the ORCA

community, a threat environment that has actually reached a person is called an insult.

The insults included in the CCWG charter include:

Penetrators (fragments, bullets)

Blast overpressure

Thermal fluence

Toxic gases, both military agents and combustion products

Abrupt acceleration

Blunt trauma

Laser eye damage

The ORCA methodology tracks INSULT to INJURY to IMPAIRMENT to JOB

PERFORMANCE, using degradation of performance functions based on 24 Elemental

Capabilities. These capabilities describe those things that the normal human body is

generally capable of doing (e.g. Visual Acuity and Color Discrimination). Therefore, the

effects of various types of injuries are determined and their degradations of the body are

mapped over time.

ORCA provides very specific injuries. The human body is defined and

geometrically modeled by 473 ORCA Body Components (OBC's). Each one basically

relates to an organ of the body. Each OBC has associated with it a scale in which its

condition (extent of injury) is expressed. Insult to an OBC causes an injury, which is

quantified in a data structure called the "A" Vector. The term "vector" refers to a one-

dimensional array, or series of objects of the same size and type. Each OBC has

associated vulnerability categories, which are used to determine which damage

algorithms to use in the injury determination process. Additionally, various physiological

processes, called "B" Processes (i.e. bleeding) result and further quantify the injury.

As the body loses critical functions, its ability to perform certain tasks also

diminishes, resulting in an affect on job performance and, collectively, the unit's mission

10

performance. ORCA attempts to answer the question: How will these type injuries in this

quantity affect my unit's ability to perform its wartime mission?

More detailed discussion on ORCA, including description of insult types and

outputs, may be found in Chapter Two.

1.4.3 Military Applications

A lower limb trauma simulator is being evaluated for the Medical Combat

Casualty Care training course at Fort Bragg, NC Medical Training Center (Satava &

Jones, 1997a). As part of the Advanced Trauma and Life Saving part of the course, a

visual 3-D representation of the thigh from the Visible Human has a gunshot wound

created from accurate ballistic wound data. In a typical scenario, the medic uses virtual

instruments to perform wound debridement.

1.5 Interoperability in a High Level Architecture (HLA) Environment

Many of the currently used medical simulation models offer particular training in

a specialized area. There are currently no simulated medical training systems available

that allow for realistic simulation of total-body casualty care at various levels and in

various environments. Nor is it known if in the near future a comprehensive model will

be developed. A comprehensive model would appear to be very expensive.

Additionally, the specialized nature of modern medicine promotes specialized tools as

11

seen from the discussion above. The basic assumption is that simulation interoperability

is the preferred methodological approach. Interoperability offers flexibility, while

permitting powerful individual simulations to develop independently of the system.

Finally, and equally important, there is no medical training system which promotes High

Level Architecture (HLA) interoperability among various types of simulated medical

components.

The Department of Defense (DOD) Modeling and Simulation Master Plan (DOD,

1995) calls for the establishment of a HLA for modeling and simulation and mandates

that all future simulations will be HLA compliant.

Before proceeding further, it is important to define a few key HLA terms. The

formal definition of the DOD HLA comprises three main components. These

components are: 1) the HLA Rules (Defense Modeling and Simulation Office [DMSO],

1998), which describe the responsibilities of simulations and of the runtime infrastructure

(RTI) in HLA federations, 2) the HLA Interface Specification (DMSO, 1997a), which

defines the interface functions between the RTI and simulations participating in HLA

federations, and 3) the HLA Object Model Template (OMT) (DMSO, 1997b), which

represents a common presentation format for HLA Object Models. There are two types

of object models in HLA, the Federation Object Model (FOM) and the Simulation Object

Model (SOM). A FOM is a specification of the exchange of public data among the

participants in a HLA federation. A SOM is a specification of the information types

offered to federations by individual simulations.

12

1.6 Combat Trauma Patient Simulation (CTPS)

The Combat Trauma Patient Simulation (CTPS) research team is prototyping and

evaluating a training and analysis simulation system that realistically simulates the

emergency medical treatment process from the time of injury through initial treatment at

a field hospital (Rajput et al., 1997). The CTPS team is composed of members from the

DOD Live Fire Test and Evaluation Office, US Army Simulation Training and

Instrumentation Command (STRICOM), the University of Central Florida's Institute for

Simulation and Training (1ST), Lockheed Martin, Medical Education Technologies, Inc.

(METI), and the Training and Simulation Technology Consortium. The University of

Central Florida (UCF) is also providing technical assistance from members within the

departments of Industrial Engineering and Management Systems, Health and Public

Affairs, and Computer Science.

The premise of this research project is that "virtual casualties" can be simulated

by realistic physical simulations from the time of initial trauma throughout treatment and

transportation. The goals of the project are to decrease the deaths due to combat wounds

by having better trained medical staffs and to provide a mechanism for analysis and for

test and evaluation (T&E) of issues in casualty medical treatment.

The most visible component of this system is a physical simulation of a casualty

(an instrumented electro-mechanical mannequin), the Human Patient Simulator (HPS).

The HPS is a model-driven system. Sophisticated physiological and pharmacological

models determine virtually all simulator responses. Patient physiology is defined by

13

patient profiles and can be modified either "on-line" or through scripts. The HPS was

originally developed for training anesthesiologists and provides a dynamic,

physiologically accurate simulation of a patient whose condition must be diagnosed,

treated, and monitored.

Injuries are input to the CTPS via the MILES IIECC, stored as "patients" in an

IST-developed software patient simulator known as PATSIM, and transmitted for

manifestation on an HPS. The transfer of patients from one CTPS component to another

is controlled by the CTPS Executive (Rajput & Petty, 1999). HLA provides a flexible

infrastructure for connecting the disparate simulations to form a larger distributed

simulation. In HLA terms, the CTPS system is an HLA federation.

The current CTPS system does not provide multiple, high-definition combat

injuries for manifestation on the HPS. The combat medical training community needs a

more comprehensive simulated training system that generates realistic combat casualties

for a federation of simulation systems. Injury-induced symptoms need to be realistically

manifested in a manner that allows for high fidelity training and feedback on critical life-

saving tasks.

1.7 Focus of Research Effort

In order to successfully address and possibly overcome the above limitations, it is

necessary to focus the research effort. Since simulated medical training must start with a

casualty, the research will first focus on current simulated injury generation and

14

categorization methods, identifying strengths and weaknesses as applicable. Secondly,

the research will analyze the ORCA insults, looking closely at output and how it might be

pertinent to other current medical simulations (e.g. CTPS). Finally, since CTPS

components must operate in an HLA environment, the research will look at ongoing

efforts to develop medical SOMs and FOMs.

15

CHAPTER 2

2.1 Injury Generation and Categorization Methods

2.1.1 DEPMEDS Codes

The Deployable Medical Systems (DEPMEDS) is a DOD initiative which

projects and deploys medical material to various theaters of operation (Gauker & Reed,

1997). As part of the methodology for determining logistical requirements for different

situations and areas of operation, a series of patient condition codes was developed.

These approximately 350 codes (the number continues to grow) describe patient

conditions likely to be encountered during military operations. About two thirds of the

codes describe trauma cases, making them suitable for modeling in ORCA. The other

one third describe non-traumatic conditions, applicable to a Troop Medical Clinic (TMC)

or a longer term care facility.

The DEPMEDS codes are used by current simulations in a number of ways. The

Medical Readiness Learning Initiative (MERLIN), uses algorithms developed by subject

matter experts to represent patient condition, as described by the DEPMEDS codes

(DOD, 1998). Other simulations use the codes as a method of transmitting virtual

16

patients to distant medical treatment facilities. When generated in the proper mix and

quantity, the codes are capable of realistically representing patient conditions for various

scenarios.

DEPMEDS patient condition codes do not define specific physiological effects of

injuries. However, they do provide enough detail to drive medical logistical requirements

and would serve as an excellent reference database for anyone interested in limiting

training scenarios to specific types of injuries (e.g. head wounds).

2.1.2 Abbreviated Injury Scale (AIS) Codes

The AIS codes (American Association for Automotive Medicine, 1985) represent

a standard for assessing impact injury severity (widely accepted within the emergency

medical care community). Born from the idea that classification of road transport injuries

by types and severity is critical to their etiological study, the AIS was originally

published in 1971. Its developmental sponsors were the American Medical Association,

American Association for Automotive Medicine and Society of Automotive Engineers.

The AIS is a system that codes single injuries and it forms the foundation for

assessing patients with multiple injuries. The AIS 80 committee recommended and

subsequently indorsed the method of Maximum AIS (MAIS). The premise of MAIS is

that the most severe AIS code should be used as the surrogate for assessing overall injury

severity. Studies revealed that approximately 98 percent of patients would be properly

17

assessed using MAIS. This method eliminated subjective judgement and could be

assigned by a nonmedical coder.

Another AIS derived method of injury scaling is the Injury Severity Score (ISS).

The ISS is a mathematically derived code determined by adding the squares of the

highest AIS codes in each of the three most severely injured regions of the body.

Penetrating injury was incorporated into AIS 85. This step highlighted the fact

that although the AIS was originally developed for impact injury assessment, its global

recognition as a standard for injury severity data collection may warrant further

expansion to encompass all injury types.

There is no apparent relationship between the DEPMEDS and AIS codes, other

than they both are used to describe patient conditions. A single DEPMEDS provides an

excellent general description of the total patient condition, while the AIS codes must be

grouped, deciphered, and analyzed, in order to provide insight into the overall condition

of the body. AIS codes provide more detail for a specific injury.

2.2 Analysis of Phase I CTPS

The following section is based on the CTPS Phase I Final Report (Rajput et al,

1997). The data contained in the referenced document serves as the basis for any future

enhancements to the CTPS and provides excellent insight into the HLA-driven

architecture of the system. The figures and tables serve to provide a better understanding

of the system architecture and to highlight current methods of CTPS injury generation.

18

Figure 1 depicts the CTPS Phase I architecture. In addition to portraying the architecture,

this section will briefly discuss CTPS information flow and injury generation.

ECC 1

ECC Database Svstpm

HPS

E CC Patient HPS RTI Simulator RTI

Interface Interface iL i i ;

T ' ' *

Run Time Infrastructure (RTI)

Figure 1: CTPS Phase 1 Architecture (Reprinted from Rajput et al., p. 9)

2.2.1 CTPS Information Flow

CTPS information flow is as follows: 1) the ECC federate transfers ownership, via

the RTI, to PatSim; 2) the PatSim federate transfers ownership, via the RTI, to HPS; and

3) the HPS federate transfers ownership back to PatSim, via the RTL

19

2.2.2 Injury Generation

In the CTPS, patient simulation begins with the generation of an injured patient

by the ECC. Next, the information is transferred to PatSim, then to the HPS.

The CTPS Phase I design allows the physiological/pharmacological patient attributes to

be simulated by the HPS, since it is most suitable for simulating these attributes.

Administrative categorization of attributes concerned with wound type and severity are

best handled by the ECC.

It is important to highlight the patient flow during the injury generation process.

The CTPS patient attributes, as well as who owns them, is depicted in Table 1. In

response to a MILES/SAWE "hit", virtual patients are created at the ECC. When the

virtual patient receives a treatment selected by a medic, the ECC sends patient

information to ECC Database Translator. Once the ECC Database Translator captures

this information, it retransmits it to the ECC RTI Interface. The ECC RTI Interface

creates the patient in the CTPS federation through the RTI with a "Request ID" service

call, and informs the federation of the initial attribute values through the RTI "Update

Value" service calls. Note that the first nine attributes are produced by the ECC. Also

note that attributes 6-9, the physiological attributes of blood pressure, respiration rate,

and heart rate, are transferable attributes. This is a key point in that ECC produces a "one

shot" update of these attributes (represents patient state at time zero), then passes them on

to be monitored and updated by another federate component.

20

P = Permanent Ownership of Attribute T = Transference Among Simulators

Attribute Abbrev Static

Dynamic

Physiological

Non Physiological

FCC 1LPS PatSim

Trauma Type Static Non Physiological P

Weapon "l>pe Static Non Phwological I»

Body Area Static Non Physiological P

Wound Severity Static Non Physiological P

Recommended Action Static Non Physiological P

Location Dynamic Non Physiological P

Blood Pressure BP Dynamic Physiological T T T

Respiration Rate RR Dynamic Physiological T T T

Heart Rate HR Dynamic Physiological T T T

C02 Arterial Partial

Pressure

CO;APP Dynamic Physiological T r

02 Arterial Partial

Pressure

(KAPP Dynamic Physiological T T

Arterial 02 Saturation OSAl D\namic Physiological 1 1

Table 1: Patient Attribute Details (Reprinted from Rajput et al., p. 11)

2.2.3 Phase I Injury Types

For Phase 1, METI developed three combat trauma types: Anaphylaxis,

Pneumothorax, and Blood Loss. These trauma types are implemented as scenarios that

21

execute on the HPS in response to the corresponding values of the wound type. The

relationships between trauma and wound types are shown in Table 2.

Combat Wound Scenario Combat Trauma Type

Snake Bite scenario Anaphylaxis

Gunshot Wound To Chest scenario Pneumothorax

Land Mine Explosion scenario Blood loss

Table 2: Wound Scenarios and Trauma Types (Reprinted from Rajput et al., p. 14)

2.2.4 ECC Phase I Modifications

To meet the requirement of the CTPS design, ECC firmware was modified to be

triggered/hit by three specific types of weapons: 1) Ml 6/ M60 Machine Gun, 2) M2/

M82 Machine Gun , and 3) AT-3 SAGGER-NTC. The wounds caused by these three

weapon types are Blood loss, Pneumothorax, and Anaphylaxis, respectively. Currently,

all casualties are generated from a single ECC.

2.3 Analysis of ORCA

As previously stated, the ORCA charter calls for development of seven insults.

The ORCA Alpha + software version (Frew, Gray, Killion, & Streit, 1997), upon which

this research is based, had working modules for four of these insult types: Penetrators

22

(fragments), Blast overpressure, Thermal fluence, and Toxic gases (military agents).

Since the ORCA charter takes it beyond the scope of medical concerns, it was necessary

to focus the research to a specific area of the ORCA program. Figure 2 depicts the

ORCA taxonomy and the specific area of research concern. What follows is an overview

of each of the working insult types.

INDIVIDUAL CHARACTERISTICS

TASK REQUIREMENTS BY JOB SPECIALTY

ECV IMPAIRMENT

—W~ ECV

REQUIREMENTS

COMPARE REQUIREMENTS TO IMPAIRMENT

I OPERATIONAL CASUALTY

area of research interest , _

' ! = USER INPUT i

Figure 2: ORCA Taxonomy (Klopcic et al, 1998) With Area of Research Interest

23

2.3.1 Penetration Insult Overview

The penetration insult is the most robust of the ORCA insult types and is therefore

covered in more detail than other insult types. In addition, some of the same output

formats used by the penetration model are the same for other insults. ORCA uses the

ComputerMan model for evaluating an expected level of incapacitation due to fragments

(Frew et al.,1997). This methodology includes: a computerized human anatomical

model, the ability to trace a ray through the human starting at any point, algorithms for

penetrator retardation and resulting organ damage, and mapping to injury scales

(Abbreviated Injury Scale (AIS) Code). The model can process single fragment shots, a

grid of shots, or a point-burst spray of fragment shots.

2.3.1.1 Penetration Input Parameters

For penetration, ORCA uses the following insult parameters: penetrator

description, penetrator impact conditions and body orientation. The penetrator

description includes the mass, shape, material, and orientation upon impact. The impact

conditions include the fragment velocity and impact location. The body orientation refers

to the position of the body (e.g. standing). Currently, the user selects insult type and

parameters using the ORCA pull-down menus and keyboard. The ORCA GUI displays

the human anatomical model on the screen, allowing the user to select penetration

entrance and exit wounds via the mouse. Applied Research Associates (ARA) states that

a future ORCA version may have the ability to read batch files as input.

24

2.3.1.2 Penetration Insult Output

ORCA allows display of injury data to screen, file, or both. Data is output to a

Injury Summary screen (or file). The data displayed or written to a file is retrieved from

the current injury severity structure. The Injury Summary lists each OBC affected,

followed by total damage to the affected OBC. Total damage is listed as either a

percentage of damage or surface area damaged, depending on the OBC type.

Additionally, the Injury Summary lists any deleterious processes initiated by the injury.

A "view process" radio button allows the user to get a more detailed account of the

process. The detailed account (for bleeding process) includes the following:

• Number of wounds

• Organ type affected (OBC type)

• Size of the wound (sq cm)

• Initial blood loss rate (liters/min)

• Shock volume (liters)

• Time to onset of shock (min)

• Final stats (time to cessation of bleeding, total blood loss, and residual

capability factors at immediate, 30 sec, 5 min, 1 hr, 24 hrs, and 72 hrs)

For a penetration wound, the user also has access to a "view AIS" radio button,

which displays the AIS description from the Penetration insult. The AIS description

25

output includes the AIS code (5 digits, followed by a decimal and one last digit - e.g.

10503.2), AIS body region (e.g. Thorax), and AIS Severity (e.g. moderate).

2.3.2 Blast Insult Overview

ORCA uses two methodologies for calculating blast injury affects (Frew et al.,

1997). The first pertains to injuries of the lung and thoracic region and uses an injury

model developed by the Walter Reed Army Institute of Medicine. The second model

calculates injury to the ears, using one of two options. Either model can use a user-

defined data file or the simple Friedlander or Triangular waves to determine eardrum

rupture or hearing loss.

2.3.2.1 Blast Input Parameters

The blast input parameters are described by the blast/overpressure and the body

orientation. The user has three options (Friedlander, Triangular wave, or data file) for

defining the blast/overpressure data. For each option, the user may view the data on a

time history plot.

Body orientation is important, because blast waves cause different levels of injury

based on the amount and type of waves hitting the thorax. The three choices for body

orientation are: long axis of the body parallel to the blast winds, long axis of the body

perpendicular to the blast winds, and thorax near a reflecting surface and perpendicular to

the blast winds.

26

2.3.2.2 Blast Insult Output

The blast insult produces injury output in the same summary format as the

penetration injury, minus the AIS codes. An important consideration is that blast injuries

affect only the lungs and the ears, so the blast injury output will only reflect damage to

the OBC's pertaining to these regions. In addition, an internal respiration process will

occur and it will be output as "Percent of Lung Volume Lost" at various times

(immediate, 30 sees, etc.).

2.3.3 Thermal Exposure Insult

ORCA uses the BURNSIM program as the thermal insult model (Frew et al.,

1997). F.S. Knox, III, Ph.D. and the U.S. Army Aeromedical Research Laboratory

(USAARL) developed this model. BURNSIM represents the skin as 12 nodes, or skin

depths, and predicts the total damage and threshold depth at each node.

2.3.3.1 Thermal Exposure Input Parameters

The user must enter values for exposure time, skin diffusion data, and exposed

tissue surface areas. The ORCA GUI offers fill-in-the-blank menus for easy data entry.

Many of the blanks are pre-filled with default values. For surface areas affected, the user

can choose from among hand, elbow, knee, foot and ankle, sole of foot, external ear,

periorbital, scalp area, and other non-specified area. For areas with both a right and left

entity (e.g. ear), the user may choose either or both sides.

27

2.3.3.2 Thermal Exposure Insult Output

The model outputs the total skin damage and the threshold depth (skin depth) for

each of the 12 nodes. The model represents and outputs data on 12 nodes for each of the

exposed tissue areas.

2.3.4 Toxic Gas Insult Overview

ORCA utilizes the ChemicalMan model, developed by ARL, for computing the

affects of toxic gas (Frew et al., 1997). ChemicalMan was written in FORTRAN, but the

code was translated into C for inclusion in ORCA.

2.3.4.1 Toxic Gas Input Parameters

Input parameters include the exposure time, the vapor concentration, and the

agent (GA, GB, GD, GF, and VX). Future versions may allow alteration of the soldier

parameters (body weight and respiratory rate), currently set as default values of 70 kg and

15.01 breaths/min, respectively. Additionally, future version will allow the user to

choose between "combustion products" and "military agents". The current ORCA

version only works for "military agents".

2.3.4.2 Toxic Gas Insult Output

The toxic gas insult displays graphic information to the screen and text

information to file. The output formats differ considerably. The screen output displays a

28

color-coded graphic of severity for various physiological categories. These are mental,

visual, cardiovascular, visceral, and limbs. The file output lists severity levels at various

concentration times (2 sec intervals), as well as Toxic Vapor Symptoms at immediate (1

sec), 30 seconds, and 2 minutes.

2.3.5 Directed Energy Insult Overview

ORCA uses this insult to predict laser damage to the eyes (Frew et al., 1997). The

methodology for this insult was developed by Bob Miller, Ph.D. and Brad Carver of

Systems Research Laboratories, and implemented by ARA. Laser exposure causes eye

injuries such as flashblindness, retinal damage, and corneal swelling.

2.3.5.1 Directed Energy Insult Input Parameters

The required user inputs are laser type (continuous wave or pulse), laser

properties (wavelength, intensity at cornea, duration, pulse repetition frequency, pulse

width), and eyes affected (L, R, or both). As with other insult types, the fill-in-the blank

menus drive the input process.

2.3.5.2 Directed Energy Insult Output

The output (to screen and file) includes a summary of the input parameters, along

with a degree of injury for each injury type (flashblindness, retinal damage, and corneal

swelling). The degree of injury is expressed in the degrees of visual angle (arc) affected

29

and length of time to recovery. If a certain type of injury does not occur (e.g. corneal

swelling) then the returned injury value is "no".

2.3.6 Summary of ORCA Output

2.3.6.1 Data Structure

Although the ORCA data is not structured in a hierarchical manner, definitions

are in place to make the transition to hierarchical design a smooth one. Figure 3 shows

how the ORCA Vulnerability categories and OBCs may be placed in a hierarchical order

as classes and subclasses.

Blood Vessel

Artery (carotid), L

Vein

Bone

Skull

Femur

Muscle

Muscle, Face NFS

Deltoid

Nerve

Brain

Optic Nerve

Vulnerability Categories (Classes)

ORCA Body Components (Subclasses)

Figure 3. Example Implementation of ORCA Hierarchical Design

30

2.3.6.2 Possible ORCA Attributes

In examining output from each of the ORCA insult types, it is clear to see that the

penetration insult produces output most applicable to combat trauma simulation. A

number of useful attributes can be obtained from the penetration insult output. These

include:

• damage to the affected OBCs

• initial blood loss rate

• time to onset of shock

• percentage of lung volume lost

• AIS code (body region/organ affected/level of injury based on

anatomy/severity)

• survival probability (based on ISS)

All of the above attributes, with the exception of time to onset of shock and survival

probability, are considered static; they would be passed once through the RTI as part of

the patient profile at time zero. They would provide significant detail concerning the

patients specific level of injury and near term survival probability. Since ORCA provides

no dynamic physiological attributes, it is essential that the ORCA output be combined

with the ECC dynamic physiological attributes (blood pressure, respiratory rate, and heart

rate) in order to simulate a realistic patient.

31

Appendix A contains example output from an ORCA penetration insult. The

blast insult also provides data suitable for combat trauma patient simulation, but it is

limited to percentage of lung volume lost and ear damage.

2.4 MYSTECH Work

2.4.1 MYSTECH Physiological SOM

Mystech Associates, Inc. developed a Physiological SOM for stimulation of a

mannequin (Lyell, 1998), which incorporated the human respiratory and cardiovascular

systems, along with the corresponding nervous system components.

Initial research compared the Mystech SOM subclasses, listed in the Object Class

Structure Table with the ORCA OBCs (473 total). There were 68 direct subclass

name/OBC matches. It is possible that the 44 additional Mystech subclasses are present

due to greater specificity concerning certain nervous system components. The over 100

Mystech subclasses, when compared with the 473 OBCs, highlights ORCA's "total

body" representation of human physical anatomy. Since the cardiovascular, respiratory,

and nervous systems form the core of human functional physiology, the Mystech

Physiological SOM will serve as a fine anatomical foundation (both physical and

functional) for development of a "total body" Physiological SOM.

32

2.4.2 MYSTECH Human Response to Stimuli SOM

In developing the Stimuli SOM, Mystech concluded that the first order response

of the body is not determined by the Stimuli Federate (Lyell, 1998). This means that the

structure of the Stimuli SOM will not include physiological objects.

The Mystech Physiological SOM guided the development of the Mystech Stimuli

SOM. Since the Physiological SOM focused on the respiratory and cardiovascular

sytstems, the Stimuli SOM only included those stimuli that affect these systems. With

this approach, the size of Stimuli SOM increases proportionally with the size of the

Physiological SOM. For example, if we add eyes to the Physiological SOM, then we

would want to include all stimuli that affect the eyes in the Stimuli SOM.

Mystech also concluded that the structure of the Stimuli SOM is adequate for

representing non-visual simple stimuli (Lyell, 1998). Simple, in this context, is defined

as a stimulus which has one simple identity vis-a-vis the body. This conclusion was

reached through expert medical consultation, resulting in definition of the things that the

physical body must know about a particular stimulus: 1) how much of the stimulus is

present, 2) how much of the stimulus must be present to cause an affect, 3) how fast

acting is the stimulus, 4) what are the features (identity) of the stimulus, and 5) what is

the primary target system of the stimulus. These knowledge requirements were translated

into Informational Attributes, housed as complex datatypes within the structure of the

Stimuli SOM. They are: identity profile, potential level of injury, immediacy of effect,

and target system (of the stimulus). A more detailed description follows.

33

In the Response to Stimuli SOM, the "Identity Profile" uses the complex datatype

"Identity Profile Data". The fields are Boolean and describe the possible effects of the

stimulus (e.g.. Modify Ambient Air Properties? or Bind Hemoglobin?). The "Potential

Level of Injury" attribute utilizes the complex data type "Generic Level of Info",

containing two fields. The first is "Level" and the second is "Potential Injury". The third

informational attribute is "Immediacy of Effect". This attribute uses datatype

"Immediacy of Effect Data" to describe how fast-acting the stimulus is. Example entries

in "Immediacy of Effect Data" include "immediate" and "seconds". The final

informational attribute is "Target System". The system should be some physiological

component. The specificity of the component will depend on the specificity of the

stimulus. In other words, the "target system" for stimulus "1/2 inch needle" should not

be "liver", because it can't affect it. A more suitable "target system" in this case might

be "epidermis".

The Stimuli SOM is capable of expansion to include stimuli types beyond those

that deal solely with the respiratory and cardiovascular systems (Lyell, 1998). One

method, termed a "flat extension", is simply to add new fields to "Identity Profile Data".

This approach would work for addition of simple stimuli, but more effort is required for

complex stimuli. Complex stimuli possess the capability to simultaneously alter the state

of more than one target system. As stated by Lyell, "the increased complexity of the

recipient system feeds back into the representation of the stimuli and requires a more

complex representation of the stimuli, since this representation is done vis-a-vis the

recipient body" (p. 45).

34

2.4.3 MYSTECH FOM

Mystech developed two versions of the "Human Response to Stimuli" FOM

(Lyell, 1998). In version one, a sophisticated "Body Environ Matrix" object forms the

core. All other physiological objects must communicate with it via publish/subscribe

services. A possible limitation of this FOM is that physiological details are hidden. This

would make it more difficult to add members to the federation, without having to first

revisit the Physiological SOM and subsequently develop a totally new FOM.

Mystech FOM Version Two is simply a union of the Physiological and Response

to Stimuli SOMs and is an excellent FOM for expansion. This research focuses on

possible expansion of this FOM, through the addition of new objects and attributes from

ORCA.

2.5 Chapter Summary

Before proceeding, it is important to summarize the completed research. The

research has taken a look at some current methods of injury generation and categorization

(DEPMEDS and AIS) and discussed their impact or possibilities for use with medical

simulations. An overview of phase I of the CTPS program identified the ECC as a key

component, capable of producing time zero physiological patient attributes. The research

analyzed ORCA output, discussed possible ORCA attributes, and outlined a methodology

for placing ORCA output into a hierarchical design. Finally, the research discussed

35

efforts to create robust medical SOMs and FOMs that are capable of supporting a variety

of simulations.

2.6 Research Questions

This research addresses the general question: What is the potential to advance

medical simulation interoperability and fidelity, given leading simulation systems? This

research will take advantage of two advanced medical simulators, the Operational

Requirements-based Casualty Assessment (ORCA) program and the Combat Trauma

Patient Simulation (CTPS) to research the gap in the current simulated medical

component. The research will then progress into development of theoretical and working

models that answer the questions: 1) Can the CTPS manifest symptoms of virtual patients

afflicted with ORCA-generated injuries onto the CTPS FOM; 2) Which of the ORCA

insult types can the CTPS accommodate?; 3) What injury types can CTPS handle that

ORCA can not generate?; 4) What is the most effective way of integrating ORCA into

CTPS?

36

CHAPTER 3

3.1 Research Methodology

This research has identified different methods for generating and categorizing

injuries, and taken a look at their applicability to combat trauma simulation. Now the

effort will proceed to presentation of a prototype injury generation model using ORCA

and conduct of research into incorporating injuries into that model. To demonstrate the

viability of the injury generation model, initial research will use this model to develop an

injury database. The injury database, once developed, will provide a mechanism for

storing injury "profiles", which may later be used as initialization data for CTPS patients.

The research will highlight CTPS testing, conducted to determine the feasibility of

reading patient injury data from such a database. In order to decide which types of

injuries to include in the database, the research makes a determination as to the suitability

of ORCA output for use by CTPS. Further analysis of potential CTPS capabilities will

highlight injury scenarios that ORCA is not capable of supporting. The research attempts

to build on previous work by identifying relationships between ORCA objects and

attributes and those from the Mystech Physiological and Stimuli SOM. Research into the

development of an ORCA-derived Injury SOM, along with analysis of its relationship to

the current CTPS FOM and the Mystech-derived CTPS Phase III version 0 FOM, will

shed light on the possible future of CTPS.

37

As a recommendation for future research, this work explores the need to continue

building on Mystech's efforts in an attempt to ultimately produce a FOM robust enough

to handle even the most visually intense total-body simulations.

3.2 Injury Generation Using ORCA

A proposed model for injury generation is portrayed in Figure 4. A key feature of

this figure is that data concerning wound type and severity gets more specific as you

traverse the figure from left to right, top to bottom (from Electronic Casualty Card (ECC)

injury to ORCA induced injury).

The injury begins with an instrument of causation (weapon type). These types are

inherent in the MILES ECC database and are the same ones used in Phase I of the CTPS

project. The specified weapon causes a particular type of injury. This injury is selected

randomly from a group of injuries which would realistically be caused by the specified

weapon. As far as CTPS is concerned, this ECC injury consists of the nine attributes.

These are trauma type, weapon type, body area, wound severity, recommended action,

location, blood pressure, respiration rate, and heart rate. It is important to make a key

point at this time. The fact that the ECC provides the three physiological attributes

(blood pressure, respiration, and heart rate) for a particular injury is of the utmost

importance. Other methods of defining injury (ORCA and AIS codes) have focused on

physical anatomy and those changes to physical anatomy produced by an insult. ORCA

does not provide physiological attributes. It does describe respiratory and bleeding

processes, in response to insults but not to medical interventions. The fact that we can

38

relate the measurements of certain physiological attributes (e.g. heart rate) to a particular

injury type allows us to define a patient at state zero (immediately following injury).

ECC INJURY

TYPE

'A /relates '"•••.. to /

Ml

DEPMEDS PATIENT

CONDITION

automatic process

manual process

ORCA PENETRATION

INSULT

INJURY DATABASE

i RTI

INTERFACE

I CTPS

Figure 4. Proposed CTPS Injury Generation Model Using ORCA

39

Although the ECC provides a number of attributes, including the important

physiological ones, the attributes concerning wound type, body area, and severity are still

relatively non-specific. The data from these attributes may be used to select from more

specific DEPMEDS patient condition codes, which might result from the type of injury

described by the ECC. Once a specific DEPMEDS code is determined, the injury

information is specific enough to model using ORCA. ORCA further specifies the

injury, creating a comprehensive injury profile. Various injury profiles can correspond to

a specific DEPMEDS code. However, since the ORCA injury profiles are manually

modeled to closely resemble a given DEPMEDS patient condition, there is a subjective

range of injury profile data that reasonably relates to each DEPMEDS code. Venture

outside of this range and you possibly describe a different DEPMEDS patient condition.

Each ORCA injury profile can be saved to file. A collection of these ORCA injury

profile files can be used for constructing an injury database, partially depicted in Table 2

on the following page.

40

ECC

Wpn

Type

ECC

Area

ECC

Inj

Type

ECC

Sever

-ity

DEP-

MEDS

Codes

Inj

Prob

ORCA

Injury

Profile

Dam-

aged

OBC

Dam-

age

Done

Lung

Vol

Blood

Loss

Rate

27 11 4 18 124 0.33 10 1 1.58 0.15

2 1.50

306 <20

139 Frac

321 <.20

278 >.50

188 <.20

303 <.20

11**

12**

121* 0.33 20**

122* 0.33 30**

27 6 4 25 176 0.1 40 1 0.36 1.1

2 1.60

70 <.20

Table 3 - Partial Prototype Injury Database

* - DEPMEDS codes that are applicable for the given ECC injury, but have not yet been

modeled using ORCA

** - ORCA Injury Profiles that are applicable to the given DEPMEDS condition, but

have not yet been developed

41

3.3 The Injury Profile Database

The following are key points concerning the example data in the prototype database

shown in Table 3:

• The sample injuries are generated by ECC weapon types 27 (M16) and 28

(artillery).

• One injury occurs to body area 11 (upper leg), another to body area 6

(abdomen), and the final two injuries affect body area 3 (chest).

• Three injuries are type 4 (penetration), while one is type 7 (blunt).

• The upper leg injury is of severity 18 (open penetrating - moderate), the

severity of the abdominal wound is 25 (tissue loss - open - severe), the first

chest wound is of severity 14 (lung - closed - respiratory distress -

moderate), and the final chest wound is severity 36 (thorax - open - rib

fracture - severe).

The following information relates to the DEPMEDS patient condition codes used in the

database:

• 084, Injury lung closed (blast crush) with pneumohemothorax moderate -

one lung with pulmonary contusion and respiratory distress; hearing

impairment moderate.

42

• 087, Wound thorax (anterior or posterior) open penetrating with associated

rib fractures and pneumothorax - acute severe respiratory distress.

• 124, Wound thigh open lacerated penetrating perforating with fracture and

nerve and/or vascular injury - limb salvageable.

• 176, MIW abdomen and pelvis with penetrating perforating wound of liver

and kidney.

The following additional information relates to the database:

• The "key" for runtime retrieval from the database consists of three ECC

attributes (body area, injury type, and severity). All possible injuries are

given equal probability, although this could change if there were reason to

believe that one injury type was more common than another. Retrieval code

might appear like this: "If body area = 11 and injury type = 4 and severity =

18, then DEPMEDS = 124 (probability 0.33)"

• While Table 3 relates a single injury profile to a DEPMEDS code, more can

be done.

• For reference, the OBC descriptions are listed in Appendix B.

43

• For "damage done", the following applies: numbers represent square

centimeters of surface area; > or < followed by a number represents

percentage of total OBC damaged; "Frac" stands for "fracture"; blood loss

rate is in liters/min.

• + and - are used to describe "go-no go" injuries (+ = "any damage")

The prototype database model was built utilizing the injury generation model

discussed earlier. The database model uses the injury generation model to expand from

the two current CTPS ECC-based injuries to thirteen possible CTPS injuries, through the

extension available in DEPMEDS/ORCA object models. The database model is meant to

show possible expandability, although each injury is modeled fully (using ORCA) to only

one level. There are a number of important features of an injury database composed in the

manner described above. The first of these is that the database model retains CTPS

project continuity by linking to the ECC, a key player in the first phase of the CTPS

program. DEPMEDS and ORCA data models also inherit physiological attributes from

the ECC. Secondly, the database model includes the DEPMEDS codes, which will serve

not only as an excellent reference list for the various injury types stored in the database,

but also as a point of familiarity for users already familiar with DOD Medical

Simulations. Lastly, the database model will include ORCA, which brings with it a host

of specific attributes concerning damage to tissue, as well as data on the bleeding process

and respiratory degradation.

44

3.4 Injury SOM Development

An injury SOM built around the previously discussed injury generation and injury

database models must include classes and attributes from various CTPS components

(ECC, ORCA), since they each play a role in injury profile development.

Development of an injury SOM needs to consider the proposed hierarchical

design of ORCA data suggested earlier. A complete Injury SOM (in HLA Object Model

Development Tool (OMDT) format is depicted at Appendix C.

In the proposed design, the OBC's may be looked at as subclasses, falling under

their parent ORCA Vulnerability Category classes. The design depicts 12 human

anatomical classes (bone, blood vessel, ear, eye, other tissue, hollow organ -hard, hollow

organ -soft, lung, muscle, nerve, skin, and soft organ). It is important to note that some

of the original ORCA OBC's and Vulnerability Categories have been altered or deleted

in this design. Since the "go-no go" ORCA category did not fit well with other medical

terminology, this category is renamed "other tissue." The "blood" vulnerability category

(relates directly to OBC 469, the blood hemoglobin element) was deleted, due to the fact

that OBC 469 is not "damaged" in the normal ORCA sense. The "blood" vulnerability

category and OBC 469 are present merely to degrade the human elemental capabilities

discussed earlier. The 473 OBC's fall within these 12 classes, with the exception of the

aforementioned OBC 469 and OBC 229 (Peritoneum). ORCA lists the vulnerability

category for OBC 229 as "NA". We will disregard OBC's 469 and 229 in further design

schemes. All 12 classes fall directly under the CTPS base class "Casualty", as depicted

45

in the partial Injury SOM of Table 4.1. The partial Injury SOM presented is incomplete

in that all subclasses (OBC's) and attribute parameters are not represented.

Base Class 1st Subclass 2nd Subclass 3rd Subclass

Casualty (PS) Bone (PS) HeadOfFemurL

HeadOfFemurR

Bloodvessel (PS) Aorta Abdominal

Thoracic

Artery Jejunal

AnteriorTibialL

Vein Angular

AnteriorTibialR

Ear (PS) InternalL

InternalR

Eye(PS) RetinaL

RetinaR

Other (PS) Adrenals

BileDuct

Pituitary

HollowOrganHard (PS) Bronchus

Larynx

Pharynx

HollowOrganSoft (PS) Duodenum

Esophagus

GallBladder

Table 4.1 Injury SOM - Partial Object Class Structure Table

46

Class Attribute Datatype Update

Type

Casualty Location GPSLocation Static

WoundType WoundTypeEnum Static

WeaponType WeaponTypeEnum Static

BodyArea BodyAreaEnum Static

WoundSeverity WoundSeverityEnum Static

BloodPressure BloodPressureStruct Conditional

RespirationRate unsigned short Conditional

HeartRate unsigned short Conditional

BloodLossRate unsigned short Conditional

LungCapacity Unsigned short Conditional

Bone BoneDamage BoneDamageEnum Static

BV BVDamage BVDamageEnum Static

Ear EarDamage EarDamageEnum Static

Eye EyeDamage EyeDamageEnum Static

Other OtherDamage OtherDamageEnum Static

HOH HOHDamage HOHDamageEnum Static

HOS HOSDamage HOSDamageEnum Static

Lung LungDamgage LungDamageEnum Static

Muscle MuscleDamage MuscleDamageEnum Static

Nerve NerveDamage NerveDamageEnum Static

Skin SkinDamage SkinDamageEnum Static

SO SODamage SODamageEnum Static

Table 4.2 Injury SOM - Attribute/Parameter Table

47

3.5 Testing the Models

This section describes research to test the feasibility of using the injury generation

model, injury database model, and injury SOM. The research is divided into two distinct

phases: pre-test and test.

3.5.1 Pre-Test Phase

This phase involves building a limited injury database model, using our injury

generation model, to facilitate testing. An injury profile database will be built,

incorporating ORCA generated injuries that correspond, to varying degrees, to the CTPS

scenarios of blood loss and gunshot wound with pneumothorax. The third scenario

planned for Phase II CTPS is snakebite. ORCA will not support modeling of a snakebite

injury. It is important to note that if a FOM represented all of the objects and attributes

read from the database, then the published attribute values would be updated throughout

the federation. Since CTPS currently does not have such a FOM, the capability to fully

test an ORCA-based injury SOM is somewhat limited.

3.5.2 Test Phase

The limited test will be conducted in the 1ST Lab, using the CTPS Phase II

configuration depicted in figure 5. In this configuration, all federates operate in a

48

Windows NT environment, with the exception of ORCA (in dotted lines), which runs on

a UNIX platform. The focus, for this particular portion of the research, is on the ORCA

RTI Interface's ability to accurately read data from the injury profile database.

RTI Executive

CTPS Executive

RTI Interface z

HLA Network

RTI Interface RTI Interface

Serial

ECC Database Translator

Serial

Serial

Injury Profile Database

Offline

ECC ORCA

Figure 5: CTPS Test Configuration

Before leaving the test configuration, it is important to look at the structure of the

Run Time Infrastructure (RTI) interface in more detail. The RTI interface structure is

depicted in Figure 6. Two classes provide the interface between the federate (or

simulator) and the RTL These are the RTIambassador and FederateAmbassador classes.

The RTIambassador class represents definition and implementation of the interface used

by the federate to communicate with the RTL The FederateAmbassador class defines the

49

interface used by the RTI to talk with the federate. It is important to note that

FederateAmbassador is an abstract base class that the federate developer must implement

by defining appropriate sub-classes and methods, prior to compiling the federate with the

RTL

Sub-class of FederateAmbassador

Internal Federate Objects

FederateAmbassador RTIambassador

Internal RTI Objects

Figure 6: RTI C++ Interface

3.6 FOM Development

Unfortunately, the CTPS phase II FOM is scenario based. A scenario-based FOM

places patients in certain "states" or subsets of various injury scenarios. For instance, a

"blood loss" scenario might have several states in which the patient could be initially

placed or moved to, depending on the situation. These states may be depicted in a

manner such as "light", "moderate", or "heavy." In the blood loss case, the states would

be characterized by the amount of blood loss occurring, most probably determined by an

attribute like "blood loss rate."

Why does CTPS have a scenario-based FOM? If a FOM is not robust enough to

handle all of the anatomical and pathophysiological changes that occur as a result of

combat trauma type injuries and manifest them in such a way so that the medical

50

caregiver can make an assessment of the patient state, then the alternative is to track

patient state within specified scenarios. In other words, make the medical assessment

beforehand and manifest symptoms appropriately, as opposed to manifesting

physiological symptoms based on the pathology. Mention of the scenario-based CTPS

Phase IIFOM raises an important question. Exactly what type of FOM would be

necessary to fully utilize detailed anatomical and pathological injuries such as those

produced by ORCA? More discussion on this subject follows in Chapter 4.

51

CHAPTER 4

4.1 CTPS Phase II Testing With ORCA

Testing of the ORCA RTI interface was conducted at the Institute for Simulations

and Training, Orlando, Florida in January, 1999. The following is a synopsis of the

testing.

4.1.1 The Test Database

The injury database developed for testing was a text file, containing four injury

profiles; three bloodloss and one pneumothorax. Each entry in the database was

structured as follows:

#Bloodloss

27,176,Skin_NFS,SubcutaneousTissue,AbdomenNFS,Liver,KidneyR,UreterR,RenalR,Lo

ngissimus,.36,1.60,<.20,fracture>.50,fracture>.50,damage,>.50,<.20,-

1,1.10,61809.5,.48,heavy

Each database injury was initially created in ORCA, using the penetration insult.

The insult parameters (penetrator type, entry and exit point, etc.) were input manually

into ORCA and the insults were processed. The resulting ORCA injuries were then

52

analyzed to determine which category of the injury scenario they fell into (light, medium,

or heavy). State categories for the bloodloss scenario were determined based on the

blood loss rate. Although the database contains only one entry for the pneumothorax

scenario, similar methodology could be applied to it, using the values for lung capacity.

After the injuries were categorized, ORCA output data was translated into object names

and attribute values and appropriately stored in the database. The OBC/Object Name

Table at Appendix B served as the translation document.

The first database field corresponds to ECC Weapon Code. ECC Weapon Code

27 depicts the bloodloss scenario entries, while code 24 denotes gunshot wound

(pneumothorax) injuries. Appendix D contains a complete listing of the database entries.

The weapon code is followed by a corresponding DEPMEDS patient condition code, then

a series of damaged objects. The list of damaged objects (corresponding to damaged

body parts) is followed by the extent of damage done to each. The final five entries are

lung capacity (cc), blood loss rate (liters/min), max AIS code (MAIS), probability of

survival, and state (light, moderate, or heavy). A negative number for any field

represents no data for that field, as is the case for lung capacity in the above example.

4.1.2 The Test

It is important to note that the Jackson Medical Scenario Library (JMSL) breaks

down each scenario (bloodloss, pneumothorax, etc.) into eleven states. These states are

based on the length of time the scenario has been running. For the test, it was necessary

53

to artificially expand the injury database, using the data already present, to include an

entry for each of the 11 JMSL states (0-10). This expanded database, containing 22

states, is located at Appendix E. The ORCA RTI interface read from the database and

printed the resulting injury profile to the screen, as depicted in Figure 7.

state_number 8

CASUALTY CHARACTERISTICS:

Weapon Type: 27 Patient Condition code :19 Skin NFS: 2.40 FaceNFS: <.20 SubcutaneousTissue: 1.35 HeadNeckNFS: .20-.50 ExternalMaxillaryL: >.50 AnteriorFacialL: >.50 Internal JugularL: >.50 Lung Capacity: -1 Blood Loss Rate : .33 MAX AIS Code : 40505.4 Probability of Survival: .90 JMSL STATE : 7

Figure 7: Example Patient Injury Profile from CTPS Test Run

Note that the last entry in Figure 7 (JMSL STATE) corresponds to the one of

eleven states. The ORCA RTI interface code randomly selects from the available states

for a particular scenario, once the scenario is chosen. For the test, sufficient scenarios

were not ran to ensure that all 22 JMSL states were selected (11 for each scenario).

Redundant state selection based on the inherent randomness made that a tedious and

54

unnecessary option. Instead, enough scenarios were run to ensure that each of the

original injury profile database data sets was represented (three variations for bloodloss

and one for gunshot wound/pneumothorax). Also, the number of scenarios was sufficient

to ensure that the output accurately represented the ORCA RTI interfaces ability to

randomly select from a large database of injury types. This is evidenced through

examination of the "JMSL STATE" number. Due to the artificial nature of the expanded

test injury database, the state number is the key that differentiates otherwise identical data

sets. Appendix F contains output data for patients injured through reading from the

injury database.

The research carried out the test only this far, based on the aforementioned FOM

immaturity. The definition of test success was demonstration of an ORCA RTI interface

that could read values from the injury database, keep them matched to their respective

objects, and display the objects with their corresponding values. If the ORCA RTI

interface can read, store, and print the data from the injury database, then it logically

follows that further manipulation is possible.

The ORCA RTI read, stored, and printed data from the database without error.

The test was a success, proving that the concept of enhancing CTPS injuries through

database retrieval is sound. A major finding of the test was that the current CTPS FOM

is not robust enough to manifest symptoms of virtual patients afflicted with ORCA-

generated injuries.

55

4.2 CTPS Phase III Version 0 FOM

As previously mentioned the MYSTECH FOM offers an excellent base for

expansion. What follows is a description of research to integrate the previously

developed Injury SOM into the MYSTECH FOM. On the following page, Table 5

depicts the methodology for integrating components of the Injury SOM into the Mystech

FOM.

It is important to highlight a few things, in order to explain the rationale behind

building the FOM in the manner described in Table 5. "Skin" and "Bone" are treated

differently than "Striated Muscle." The former two were added as subclasses under

"Organ", while the latter was added to the tissue groups for each body region. This was

done mainly because of the way Mystech defined the various tissue groups. The tissue

groups were defined by the main artery that supplies oxygenated blood to the specified

region of the body. When muscles are traumatically damaged, there is often a significant

amount of dead (due to lack of oxygen) muscle tissue that must be cut away through a

process known as wound debridement. Discolored muscle tissue is cut away, exposing

blood red, freshly oxygenated tissue.

Bones and skin also receive and rely on an oxygenated blood supply. However,

the ORCA definition of skin did not allow for breaking it down into the various regions

and the bone's reliance on oxygenated blood was not seen as significant enough to

warrant the structural break out.

56

Blood vessel Bone Lung lobe

• added to existing Mystech • added as subclass to • added as subclass to

structure, under Mystech's "organs" class Mystech's "lungs" class

"cardiovascular

ductwork"

Hollow organ - hard Hollow organ - soft Skin

• added as subclass to • added as subclass to • added as subclass to

Mystech's "organs" class Mystech's "organs" class Mystech's "organs" class

Striated Muscle Nerve

• added as subclasses (by • added to existing Mystech

body region) to structure, under "nervous

Mystech's "tissue groups" ductwork"

class

Solid Organ Go- no go

• added as subclass to • added as subclass

Mystech's "organs" class (OtherMiscOrg) under

Mystech's "organs" class

Table 5: Injury SOM Integration Into Mystech FOM

The resulting FOM is located at Appendix G. This FOM accounts for all

components of the Injury SOM, while leaving in tact most of the superb structure

originally conceived by Mystech. This FOM, if used by the CTPS federation, would

57

allow for detailed total body injury representation. This detailed anatomical account of

injuries, made possible by ORCA, would be of great significance when considering the

addition of visually oriented simulations to the CTPS federation. Current plans call for

use of this FOM in Phase III of the CTPS program.

58

CHAPTER 5

5.1 Conclusion

ORCA is capable of producing injury profiles in conjunction with the ECC.

ORCA injury profiles add more anatomical specificity to DEPMEDS codes, as well as

increasing the variety of available injury types. Associating DEPMEDS codes with an

ECC injury type provides universally recognized patient conditions. Creating an ORCA

injury profile database increases the number of injuries in the available injury pool, while

adding more anatomical specificity to the injuries. The successful testing, conducted as

part of this research, demonstrated the capability of transferring data, through the RTI,

from a prototype ORCA injury database. A major finding of the test was that the current

CTPS FOM is inadequate for handling the enhanced injury profiles and a situation-based

FOM will not suit long term use. Automation of the proposed injury generation

methodology will provide quick, on-line access to this pool of realistic combat trauma

injuries.

The current ORCA Alpha + version lacks the robustness to handle all injury types

that the CTPS can handle. ORCA is fine for modeling injuries that require damage to

tissue. ORCA does not do well modeling injury scenarios where the injury manifests

itself solely as a change to human physiological parameters such as respiratory rate, heart

59

rate, and blood pressure. Examples of these injuries include those depicted in the Phase I

and IICTPS anaphylactic shock scenarios, as well as theoretical chemical/biological

scenarios. Although ORCA does include a limited toxic substance insult, conversion of

the output data into something useful by HPS would require significant work and medical

expertise.

Conversely, the CTPS is not currently designed to sufficiently handle all of the

possible ORCA output. Conversion functions may be necessary to convert the ORCA

attributes of blood loss rate and lung capacity to something useable by the HPS. Also,

the HPS lacks the capability to display the visual enhancements offered by ORCA's

detail on injury location and size. Future research is needed to identify suitable visual

enhancement technology, if the CTPS is ever to be used as a trainer for initial assessment

of battlefield injuries.

Of the available insult types in the current ORCA version, the ORCA penetration

insult model shows the most promise for providing CTPS injury generation, because it is

capable of producing combat trauma type injuries to any portion of the human body.

ORCA body components may be objectified and placed in an Injury SOM, along with

potential ORCA-generated attributes. Potential attributes output from a penetration

injury are: 1) damage to the affected OBCs; 2) initial blood loss rate; 3) blood loss to

onset of shock; 4) percentage of lung volume lost and; 4) AIS code (body region/organ

affected/level of injury based on anatomy/severity). The blast insult produces similar

output, but it is limited to lung and ear injuries.

60

Work should continue on enhancing the CTPS Phase III Version 0 FOM.

Mystech provided an excellent base FOM, however, realistic simulation of damage to

human tissue, as with ORCA, must include the stimuli necessary to do the damage. Such

an effort must also include the anatomical and physiological structure necessary for "total

body" representation. Incorporation of the ORCA-derived Injury SOM into the Mystech

FOM provided the additional necessary anatomy, as well as damage mechanisms

required to identify the various "tissue damage" stimuli. However, tissue damage will

trigger complex systemic interactions, the modeling of which are beyond the scope of this

research and will require significant medical expertise, primarily in the areas of human

anatomy and pathophysiological response.

5.2 Recommendations for Future Research

ORCA is capable of producing injury profiles that add more anatomical

specificity to DEPMEDS codes, as well as increasing the variety of available injury

types. Creating an ORCA injury profile database increases the number of injuries in the

available injury pool, while adding more anatomical specificity to the injuries.

Automation will provide quick, on-line access to this pool of realistic combat trauma

injuries. However, when it comes to manifestation of these realistic injuries onto the

HPS, it is important to note a few shortfalls in the current CTPS FOM. Discussion of

possible CTPS FOM improvements follows.

61

CHAPTER 6

6.1 Visual Limitations of CTPS

The current CTPS has some limitations. One limitation is the inability to visually

manifest the physical damage that occurs with a physical injury. According to Satava and

Jones (1997b), " "Realism" is the prime determinant of believability in any virtual

environment, with the medical arena having a high priority on realism" (p. 141). With

this in mind, an ideal virtual patient, from a medical caregiver standpoint, should look

and function much like a real patient on the outside and inside.

The current HPS can be "made-up" with moulage to add an element of realism

through visual representation of external injuries. The drawback to this method is that it

requires defiling the mannequin. It also means that someone must clean up the mess and

change it before beginning a new scenario. Suggestions for CTPS enhancement have

included the addition of an on screen virtual patient in close proximity to the HPS, as

well as projection (from an external source) of injuries directly onto the mannequin.

The first CTPS enhancement suggestion would probably utilize some existing

simulation that possesses the capability to take a set of injury parameters and display

them appropriately (and realistically) onto a screen. The unfortunate thing about

computer graphic representations is that they are usually easily identifiable as computer

62

graphic representations. Unlike digital pictures or video, computer renderings often take

on the appearance of a cartoon, limiting the ability of an application to portray a realistic

virtual scene.

Many recent technological advances have brought us closer to the creation of the

ideal virtual patient:

• the Visible Human Project has made it possible to view a realistic digital 3D

representation of the human body, inside and out

• algorithms for stitching together digital images into panoramic scenes are now

faster and more efficient

• visualization of virtual worlds through head mounted displays is better than

ever

• tactile devices, such as the Phantom, have made it possible to realistically

touch and manipulate objects in a virtual world

The projector-based CTPS enhancement suggestion, although simple and cheap,

would most probably suffer from lack of quality, due to environmental factors such as

lighting and patient movement.

63

6.2 Visually-Oriented Medical Simulations

There are a number of simulations currently available that are attempting to take

advantage of the above technological trends. None, however, have attempted to join an

HLA Federation such as the CTPS Federation. This research has already mentioned two

simulators, MediSim (Stansfield et al., 1998) (a prototype) and the Virtual Medical

Trainer - Trauma Patient Simulator (VMET-TPS) (RTI, 1998).

MediSim, developed by Sandia National Laboratories, focuses on situational

training of first-line battlefield medical personnel whose responsibility it is to triage and

stabilize multiple casualties, prior to their evacuation to field hospitals. This focus

differentiates MediSim from some other VR-based medical trainers, whose primary goal

is to train a specific procedure or task. MediSim's goal is to train rapid situational

assessment and decision-making under battlefield conditions. MediSim's initial training

procedure is the primary assessment. This first stage of triage is carried out for all wound

types. Primary assessment involves evaluating each of the following in this order:

airway, breathing, circulation, disability and expose. The procedure for each of these

contains a number of steps used to diagnose and stabilize the casualty. It is important to

note that "expose" is a visual check for wounds on the body; a procedure requiring a

highly visible and realistically portrayed casualty.

VMET-TPS (formerly called VMT) is an interactive, multimedia, virtual- reality-

based simulator that presents the user with a three-dimensional (3D) trauma scenario,

complete with realistic visual and aural effects. The casualty is a 3D virtual model with

64

realistic visual injuries and internal trauma. Injury mechanisms currently available

include falls, gunshot wounds, vehicle collisions, explosions, and blunt injury. Similar to

MediSim, VMET-TPS is an excellent initial assessment tool, allowing the user to enter

and "size-up" the scene, determine level of consciousness, and attend to major life-

threatening conditions. Many of these conditions present themselves visually (e.g. major

hemorrhage).

6.3 The Need for Further Expansion of the MYSTECH FOM

Mystech's decision to limit the scope of the Physiological SOM to the

cardiovascular and respiratory systems is based on these systems' key role in the intake

and distribution of oxygen, a function key to survival of the physiological system. There

is no denying the validity of this decision. However, not all simulations will rely so

heavily on the underlying physiology. For instance, MediSim or the VMT-TPS are

heavily dependent on visual information pertaining to the state of the human body.

Information which might be derived from a baseball-sized hole being blown through the

human chest region. This information can only be realistically rendered and updated with

precise physical anatomical information.

The tissue damage provided by ORCA-generated injuries certainly calls for an

additional stimulus to the Mystech Stimuli SOM and subsequent addition to the FOM. A

"Damage to Tissue" stimulus seems in order. This particular stimulus type could be

represented, for instance, one per ORCA Vulnerability Category, with a corresponding

65

array of targeted objects (e.g. bone, muscle, blood vessels). Addition of the Injury SOM

classes and attributes to the Mystech FOM is a good start, but the complex systemic

interactions to the pathologic ORCA stimuli will also have to be dealt with and this will

require a great deal of medical expertise.

Concerning the virtual patient, there should remain a healthy balance between

anatomy, physiology, and pathophysiology, just as there is in the real human patient. The

inherent synergism can not be ignored in the endeavor to create the perfect virtual patient

model. Future research is needed to flush out the details of a CTPS FOM to support the

virtual patient.

66

Appendix A

ORCA Penetration Insult Output

67

Appendix A - ORCA Output File Formats

A. 1 - Penetration Injury to Thigh With Bleeding

JimBliiljüBi

Muscle (abductor magnus), L

neous

=Sufci|||lll|l!lllllllil|511111111l:l

'Klllli|lIro|||ilIi;;..:.:

■fiüi fflgK.;:::iiiioiHiliysiäiii:B:i Wound 1; Organ type; Peripheral Artery Size; 0,25

A.2 - AIS and Survival Probability

B wmterm Raw fiiS AIS Body Region

Fwternal Lower E:-.„. „.... Lower- EKtromi

Muderatt; Moderate Moderate "■-■■.■■.■: . .I--

■sing ? = a.9

Appendix B

OBC/Object Name Table

70

OBC# ORCA Common Name Object Name 88 headoffemur,L HeadOfFemurL 361 headoffemur,R HeadOfFemurR 99 ilium,L IliumL 372 ilium,R IliumR 86 Bone (ischium), L IschiumL 360 Bone (ischium), R IschiumR 84 Bone (pubis) Pubis 82 Bone (wing of ilium), L WingOflliumL 359 Bone (wing of ilium), R WingOflliumR 127 Carpal Bones, L CarpalBonesL 396 Carpal Bones, R CarpalBonesR 37 Clavicle, L ClavicleL 348 Clavicle, R ClavicleR 80 Coccyx Coccyx 330 Facial bone (mandibular ramus), R MandibularRamusR 81 Facial Bone (mandible), L MandibleL 358 Facial Bone (mandible), R MandibleR 90 Facial Bone (mandibular body) MandibularBody 8 Facial Bone (mandibular ramus), L MandibularRamusL 66 Facial Bone (maxilla), L MaxillaL 357 Facial Bone (maxilla), R MaxillaR 42 Facial Bone (nasal) Nasal 18 Facial Bone (zygoma), L ZygomaL 334 Facial Bone (2ygoma), R ZygomaR 139 Femur, L FemurL 408 Femur, R FemurR 153 Fibula, L FibulaL 419 Fibula, R FibulaR 43 Humerus (proximal shaft), L HumerusProxShaftL 351 Humerus (proximal shaft), R HumerusProxShaftR 103 Humerus, L HumerusL 377 Humerus, R HumerusR 179 Hyloid Bone Hyoid 161 Lateral and Medial Malleolus, L LatMedMalleolusL 425 Lateral and Medial Malleolus, R LatMedMalleolusR 133 Metacarpal Bones, L MetacarpalBonesL 398 Metacarpal Bones, R MetacarpalBonesR 171 Metatarsal Bones, L MetatarsalBonesL 430 Metatarsal Bones, R MetatarsalBonesR 141 Patella, L PatellaL 409 Patella, R PatellaR 172 Phalanges (foot), L PhalangesFootL 431 Phalanges (foot), R PhalangesFootR 135 Phalanges (hand), L PhalangesHandL 400 Phalanges (hand), R PhalangesHandR

115 Radius, L RadiusL 390 Radius, R RadiusR 45 Rib Rib 78 Sacrum Sacrum 180 Sacrum (near cauda equina) SacrumCaudaEquina 39 Scapula, L ScapulaL 349 Scapula, R ScapulaR 41 Shoulder Joint, L ShoulderJointL 350 Shoulder Joint, R ShoulderJointR 4 Skull (base) SkullBase 5 Skull (vault) SkullVault 53 Sternum Sternum 165 Tarsal Bones, L TarsalBonesL 428 Tarsal Bones, R TarsalBonesR 34 Thyroid (cartilage) ThyroidCartilage 151 Tibia, L TibiaL 418 Tibia, R TibiaR 116 Ulna, L UlnaL 391 Ulna, R UlnaR 182 Vertebra (cl-c4 body) VertebraC 1 C4Body 190 Vertebra (c5-c7 body) VertebraC5_C7Body 30 Vertebra (cervical odontoid) VertCervicalOdontoid 131 Vertebra (cervical pedicle) VertCervicalPedicle 174 Vertebra (cervical transverse) VertCervicalTransverse 15 Vertebra (cervical) VertebraCervical 67 Vertebra (lumbar body) VertebraLumbarBody 146 Vertebra (lumbar lamina) VertLumbarLamina 150 Vertebra (lumbar pedicle) VertLumbarPedicle 65 Vertebra (lumbar spinal process) VertLumbarSpinalProc 68 Vertebra (lumbar transverse) VertLumbarTransverse 17 Vertebra (thoracic body) VertThoracicBody 142 Vertebra (thoracic lamina) VertThoracicLamina 32 Vertebra (thoracic pedicle) VertThoracicPedicle 29 Vertebra (thoracic spinal process) VertThoracicSpinProc 31 Vertebra (thoracic transverse) VertThoracicTrans 71 Aorta (abdominal) Abdominal 97 Aorta (thoracic) Thoracic 173 Artery (jejunal) Jejunal 220 Artery (anterior tibial), L AnteriorTibialArtL 449 Artery (anterior tibial), R AnteriorTibialArtR 239 Artery (arciformis) Arciformis 216 Artery (axillary), L AxillaryArtL 445 Artery (axillary), R AxillaryArtR 114 Artery (basilar) Basilar 290 Artery (brachial) BrachialArt 124 Artery (circumflex coronary) CircumflexCoronary

279 Artery (circumflex femoral), L CircumflexFemoralL 458 Artery (circumflex femoral), R CircumflexFemoralR 248 Artery (colic) ColicArt 211 Artery (common carotid), L CommonCarotidL 441 Artery (common carotid), R CommonCarotidR 254 Artery (common iliac), L CommonlliacL 454 Artery (common iliac), R CommonlliacR 237 Artery (coronary) Coronary 291 Artery (deep brachial) DeepBrachial 429 Artery (deep cervical), R DeepCervicalR 167 Artery (deep cervical), L DeepCervicalL 145 Artery (deep femoral), L DeepFemoralL 412 Artery (deep femoral), R DeepFemoralR 134 Artery (digital), L DigitalL 399 Artery (digital), R DigitalR 184 Artery (external carotid), L ExternalCarotidL 432 Artery (external carotid), R ExternalCarotidR 262 Artery (external iliac) ExternallliacArt 285 Artery (external maxillary), L ExternalMaxillaryL 460 Artery (external maxillary), R ExternalMaxillaryR 143 Artery (femoral), L FemoralArtL 410 Artery (femoral), R FemoralArtR 228 Artery (gastric) Gastric 242 Artery (gastroduodenal) Gastroduodenal 274 Artery (gluteal), L GlutealArtL 456 Artery (gluteal), R GlutealArtR 263 Artery (hypogastric) HypogastricArt 168 Artery ( ileocolic) IleocolicArt 96 Artery ( iliac), L IliacL 370 Artery ( iliac), R IliacR 255 Artery ( iliolumbalis) Iliolumbalis 264 Artery ( inferior epigastric) InferiorEpigastric 251 Artery (i inferior mesenteric) InferiorMesenteric 73 Artery (i inferior phrenic) InferiorPhrenic 205 Artery (i inferior thyroid) InferiorThyroid 275 Artery (i inferior vesicular) InferiorVesicular 217 Artery (i innominate), L InnominateL 446 Artery (i innominate), R InnominateR 223 Artery ( intercostal) Intercostal 21 Artery (] internal carotid), L InternalCarotidL 335 Artery (i internal carotid), R InternalCarotidR 209 Artery (i internal mammary) InternalMammary 112 Artery (i interosseus), L InterosseusL 382 Artery (interosseus), R InterosseusR 119 Artery (left ant. des. coronary) LeftAntDesCoronary 295 Artery (i medial collateral), L MedialCollateralL

466 Artery (medial collateral), R MedialCollateralR 281 Artery (middle cerebral), L MiddleCerebralL 459 Artery (middle cerebral), R MiddleCerebralR 282 Artery (middle meningeal) MiddleMeningeal 176 Artery (obturator) ObturatorArt 25 Artery (occipital), L OccipitalL 338 Artery (occipital), R OccipitalR 245 Artery (pancreatic duodenal) PancreaticDuodenal 178 Artery (peroneal), L Peroneal ArtL 473 Artery (peroneal), R PeronealArtR 148 Artery (popliteal), L PoplitealArtL 414 Artery (popliteal), R PoplitealArtR 200 Artery (posterior communicating), L PostCommunicatingL 438 Artery (posterior communicating), R PostCommunicatingR 214 Artery (posterior tibial), L PosteriorTibialArtL 443 Artery (posterior tibial), R PosteriorTibialArtR 278 Artery (primary perforating) PrimaryPerforatingArt 276 Artery (pudendal) Pudendal 222 Artery (pulmonary), L PulmonaryL 451 Artery (pulmonary), R PulmonaryR 293 Artery (radial collateral), L RadialCollateralL 464 Artery (radial collateral), R RadialCollateralR 117 Artery (radial), L RadialArtL 392 Artery (radial), R RadialArtR 109 Artery (recurrent radial), L RecurrentRadialL 381 Artery (recurrent radial), R RecurrentRadialR 106 Artery (recurrent ulnar), L RecurrentUlnarL 379 Artery (recurrent ulnar), R RecurrentUlnarR 236 Artery (renal), L RenalArtL 453 Artery (renal), R RenalArtR 125 Artery (right coronary) RightCoronary 260 Artery (spermatic) SpermaticArt 232 Artery (splenic) SplenicArt 210 Artery (subclavian), L SubclavianArtL 440 Artery (subclavian), R SubclavianArtR 22 Artery (superficial temporal), L SuperficialTemporalL 336 Artery (superficial temporal), R SuperficialTemporalR 199 Artery (superior cerebral), L SuperiorCerebralArtL 437 Artery (superior cerebral), R SuperiorCerebralArtR 243 Artery (superior mesenteric) SuperiorMesentericArt 294 Artery (ulnar collateral), L UlnarCollateralL 465 Artery (ulnar collateral), R UlnarCollateralR 121 Artery (ulnar), L Ulnar ArtL 394 Artery (ulnar), R UlnarArtR 203 Artery (vertebral) VertebralArt 224 Thoracic Duct ThoracicDuct

177 Vein( angular) Angular 420 Vein (anterior tibial), R AnteriorTibialVeinR 286 Vein (anterior facial), L AnteriorFacialL 461 Vein (anterior facial), R AnteriorFacialR 201 Vein (anterior jugular), L AnteriorJugularL 439 Vein (anterior jugular), R AnteriorJugularR 155 Vein (anterior tibial), L AnteriorTibialVeinL 54 Vein (axillary), L AxillaryVeinL 354 Vein (axillary), R AxillaryVeinR 204 Vein (azygos) AzygosVein 108 Vein (basilic) Basilic 213 Vein (brachial), L BrachialVeinL 442 Vein (brachial), R BrachialVeinR 107 Vein (cephalic), L CephalicL 380 Vein (cephalic), R CephalicR 163 Vein (colic) ColicVein 289 Vein (common facial), L CommonFacialL 462 Vein (common facial), R CommonFacialR 164 Vein (common iliac), L CommonlliacL 427 Vein (common iliac), R CommonlliacR 288 Vein (communicans) Communicans 284 Vein (deep cervical) Deepcervical 159 Vein (deep femoral), L DeepFemoralL 423 Vein (deep femoral), R DeepFemoralR 269 Vein (external iliac) ExternallliacVein 51 Vein (external jugular), L ExternalJugularL 353 Vein (external jugular), R ExternalJugularR 144 Vein (femoral), L FemoralVeinL 411 Vein (femoral), R FemoralVeinR 170 Vein (gluteal) GlutealVein 234 Vein (hemizygos) Hemizygos 72 Vein (hepatic) Hepatic 265 Vein (hypogastric) HypogastricVein 256 Vein( ileocolic) IleocolicVein 169 Vein( inferior epigastric) InferiorEpigastric 249 Vein( inferior mesenteric) InferiorMesenteric 74 Vein (i inferior vena cava) InferiorVenaCava 218 Vein (i nnominate), L InnominateL 447 Vein (i innominate), R InnominateR 185 Vein (i internal jugular), L InternalJugularL 433 Vein (i nternal jugular), R InternalJugularR 208 Vein (i ntervertebral) Intervertebral 252 Vein (intestinal) Intestinal 175 Vein (middle cerebral) MiddleCerebral 273 Vein (obturator) ObturatorVein 156 Vein( jeroneal), L PeronealVeinL

472 Vein (peroneal), R PeronealVeinR 158 Vein (popliteal), L PoplitealVeinL 422 Vein (popliteal), R PoplitealVeinR 195 Vein (portal) Portal 287 Vein (posterior facial) PosteriorFacial 421 Vein (posterior tibial), R PosteriorTibialVeinR 181 Vein (primary perforating) PrimaryPerforatingVein 221 Vein (pulmonary), L PulmonaryVeinsL 450 Vein (pulmonary), R PulmonaryVeinsR 120 Vein (radial), L RadialVeinL 393 Vein (radial), R RadialVeinR 233 Vein (renal), L RenalVeinL 452 Vein (renal), R RenalVeinR 147 Vein (saphenous), L SaphenousVeinL 413 Vein (saphenous), R SaphenousVeinR 250 Vein (spermatic) SpermaticVein 231 Vein (splenic) SplenicVein 448 Vein (subclavian), R SubclavianVeinR 219 Vein (subclavian),L SubclavianVeinL 198 Vein (superior cerebral), L SuperiorCerebralVeinL 436 Vein (superior cerebral), R SuperiorCerebralVeinR 247 Vein (superior mesenteric) SuperiorMesentericVein 52 Vein (superior vena cava) SuperiorVenaCava 122 Vein (ulnar), L UlnarVeinL 395 Vein (ulnar), R UlnarVeinR 162 Vein (variant), L VariantL 426 Vein (variant), R VariantR 240 Vein (vertebral venus plexus) VertebralVenusPlexus 207 Vein (vertebral) VertebralVein 157 Vein posterior tibial), L PosteriorTibialVeinL 186 Ear (internal), L InternalL 434 Ear (internal), R InternalR 24 Retina, L RetinaL 337 Retina, R RetinaR 62 Adrenals Adrenals 244 Bile Duct BileDuct 10 Eye, L (except eyeball & sclera) EyeLNoEyeballSclera 332 Eye, R (except eyeball and sclera) EyeRNoEyeballSclera 241 Gall Bladder (c and h duct) GallBladderCHDuct 50 Heart (chambers & valves) HeartChambers Valves 55 Heart Valves HeartValves 94 Penis Penis 118 Pituitary Pituitary 277 Seminal Vesicle (testicle), L SeminalVesicleL 457 Seminal Vesicle (testicle), R SeminalVesicleR 20 Superior Sagittal Sinus SuperiorSagittalSinus

16 Thyroid Gland Thyroid 64 Ureter, L UreterL 356 Ureter, R UreterR 197 Urethra Urethra 206 Bronchus Bronchus 14 Larynx Larynx 13 Pharynx Pharynx 35 Trachea Trachea 189 Duodenum Duodenum 36 Esophagus Esophagus 59 Gall Bladder GallBladder 126 Heart (left atrium) AtriaLeft 130 Heart (left ventricle) VentricleLeft 128 Heart (right atrium) AtriaRight 129 Heart (right ventricle) VentricleRight 57 Large Intestine Largelntestine 253 Mesentery Mesentery 61 Pancreas Pancreas 225 Pericardium Pericardium 111 Pleura Pleura 191 Rectum Rectum 238 Small Intestine Smalllntestine 56 Stomach Stomach 93 Urinary Bladder Urinary Bladder 91 Lung (inferior lobe), L InferiorLobeL 362 Lung (inferior lobe), R InferiorLobeR 95 Lung (middle lobe) MiddleLobe 47 Lung (superior lobe), L LungSuperiorLobeL 352 Lung (superior lobe), R LungSuperiorLobeR 166 Adductor Brevis, L AdductorBrevisL 366 Adductor Brevis, R AdductorBrevisR 321 Adductor Longus, L AdductorLongusL 367 Adductor Longus, R AdductorLongusR 188 Adductor Magnus, L AdductorMagnusL 368 Adductor Magnus, R AdductorMagnusR 85 Biceps Brachii, L BicepsBrachiiL 374 Biceps Brachii, R BicepsBrachiiR 303 Biceps Femoris, L BicepsFemorisL 402 Biceps Femoris, R BicepsFemorisR 152 Buccinator Buccinator 314 Deltoid, L DeltoidL 342 Deltoid, R DeltoidR 48 Diaphragm L&R DiaphragmLR 301 Extensor Carpi Radialis Longus, L ExtCarpiRadLongusL 388 Extensor Carpi Radialis Longus, R ExtCarpiRadLongusR 320 Extensor Digitorum, L ExtensorDigitorumL

387 Extensor Digitorum, R 280 Flexor Carpi Ulnaris, L 386 Flexor Carpi Ulnaris, R 230 Flexor Digitorum Superficialus, L 385 Flexor Digitorum Superficialus, R 307 Gastrocnemius, L 416 Gastrocnemius, R 227 Gluteus Maximus, L 364 Gluteus Maximus, R 318 Gluteus Medius, L 363 Gluteus Medius, R 193 Gracilus, L 369 Gracilus, R 49 Heart (epicardium & myocardium) 299 Iliocostalis 300 Iliopsoas 315 Infraspinatus, L 344 Infraspinatus, R 311 Latissimus Dorsi, L 339 Latissimus Dorsi, R 298 Longissimus 154 Masseter 160 Muscle (foot), L 424 Muscle (foot), R 310 Muscle (sartorious), L 468 Muscle (sartorious), R 309 Muscle (semispinalis group) 123 Muscle (wrist) 470 Muscle (wrist), R 70 Muscle, abdomen, NFS 113 Muscle, Forearm NFS, L 383 Muscle, Forearm, NFS, R 11 Muscle, head & neck, NFS 149 Muscle, Lower Leg, NFS, L 415 Muscle, Lower Leg, NFS, R 92 Muscle, Pelvis, NFS 26 Muscle, thorax, NFS 373 Muscle, Upper Arm, NFS, R 101 Muscle, Upper Arm, NFS,L 136 Muscle, Upper Leg, NFS, L 401 Muscle, Upper Leg, NFS, R 7 Muscle,face, NFS 104 Obicularis oris 319 Pectinius, L 365 Pectinius, R 312 Pectoralis Maj or, L

ExtensorDigitorumR FlexorCarpiUlnarisL FlexorCarpiUlnarisR FlexorDigitSuperficL FlexorDigitSuperficR GastrocnemiusL GastrocnemiusR GluteusMaximusL GluteusMaximusR GluteusMediusL GluteusMediusR GracilusL GracilusR HeartEpicardMyocard Iliocostalis Iliopsoas InfraspinatusL InfraspinatusR LatissimusDorsiL LatissimusDorsiR Longissimus Masseter FootL FootR SartoriousL SartoriousR SemispinalisGroup WristL WristR AbdomenNFS ForearmNFS_L ForearmNFS_R HeadNeckNFS LowerLegNFS_L LowerLegNFS_R PelvisNFS ThoraxNFS UpperArmNFS_R UpperArmNFS_L UpperLegNFS_L UpperLegNFS_R FaceNFS ObicularisOris PectiniusL PectiniusR PectoralisMajorL

340 Pectoralis Major, R PectoralisMaj orR 313 Pectoralis Minor, L PectoralisMinorL 341 Pectoralis Minor, R PectoralisMinorR 196 Pronator Quadratus, L PronatorQuadratusL 384 Pronator Quadratus, R PronatorQuadratusR 306 Quadriceps Femoris, L QuadricepsFemorisL 405 Quadriceps Femoris, R QuadricepsFemorisR 77 Rectus Abdominus RectusAbdominus 304 Semimembranosus, L SemimembranosusL 403 Semimembranosus, R SemimembranosusR 305 Semitendinosus, L SemitendinosusL 404 Semitendinosus, R SemitendinosusR 132 Soft Tissue (hand), L SoftTissueHandL 397 Soft Tissue (hand), R SoftTissueHandR 297 Spinalis Spinalis 235 Splenius Splenius 246 Sternocleidomastoid Sternocleidomastoid 317 Subscapularis, L SubscapularisL 346 Subscapularis, R SubscapularisR 271 Supraspinatus, L SupraspinatusL 343 Supraspinatus, R SupraspinatusR 226 Temporalis Temporalis 316 Teres Minor, L TeresMinorL 345 Teres Minor, R TeresMinorR 302 Thenar Eminence, L ThenarEminenceL 389 Thenar Eminence, R ThenarEminenceR 308 Tibialis Anterior, L TibialisAnteriorL 417 Tibialis Anterior, R TibialisAnteriorR 283 Tongue Tongue 257 Trapezius, L TrapeziusL 333 Trapezius, R TrapeziusR 87 Triceps Brachii, L TricepsBrachiiL 375 Triceps Brachii, R TricepsBrachiiR 23 Brain (frontal lobe) BrainFrontalLobe 6 Brain (minus frontal lobe) BrainNoFrontalLobe 183 Brain Stem BrainStem 69 Cauda Equina CaudaEquina 202 Nerve (acoustic) Acoustic 28 Nerve (brachialplexus), L BrachialplexusL 347 Nerve (brachialplexus), R BrachialplexusR 212 Nerve (cranial) Cranial 138 Nerve (femoral), L FemoralNVL 407 Nerve (femoral), R FemoralNVR 267 Nerve (First Sacral) FirstSacral 259 Nerve (lumbar) Lumbar 266 Nerve (lumbosacral trunk) LumbosacralTrunk

292 Nerve (median), L 463 Nerve (median), R 102 Nerve (musculocutaneous), L 376 Nerve (musculocutaneous), R 258 Nerve (obturator) 194 Nerve (peroneal), L 471 Nerve (peroneal), R 27 Nerve (phrenic) 296 Nerve (radial), L 467 Nerve (radial), R 261 Nerve (sacral) 98 Nerve (saphenous), L 371 Nerve (saphenous), R 137 Nerve (sciatic), L 406 Nerve (sciatic), R 268 Nerve (Second sacral) 270 Nerve (Third sacral) 192 Nerve (tibial), L 435 Nerve (tibial), R 215 Nerve (ulnar), L 444 Nerve (ulnar), R 187 Nerve (vagus) 12 Nerve, cervical 9 Optic Nerve, L 331 Optic nerve, R 19 Spinal Cord (C1-C3) 110 Spinal Cord (C4-C7) 76 Spinal Cord (L1-L2) 33 Spinal Cord (T1-T12) 1 Skin, NFS 378 Ear (external), R 105 Ear external, L 3 Scalp 329 Skin - sole of foot-R 38 Skin-Elbow, antecubital area-L 322 Skin-Elbow, antecubital area-R 40 Skin-Elbow, olecranon area-L 323 Skin-Elbow, olecranon area-R 140 Skin-Foot and ankle (not sole)-L 328 Skin-Foot and ankle (not sole)-R 79 Skin-Hand, dorsal surface, L 326 Skin-Hand, dorsal surface-R 83 Skin-Hand, volar surface-L 327 Skin-hand, volar surface-R 44 Skin-Knee, patellar area-L 324 Skin-Knee, patellar area-R

MedianL MedianR MusculocutaneousL MusculocutaneousR ObturatorNV PeronealNVL PeronealNVR PhrenicNV RadialNVL RadialNVR Sacral SaphenousNVL SaphenousNVR SciaticL SciaticR SecondSacral ThirdSacral TibialL TibialR UlnarNVL UlnarNVR VagusNV Cervical OpticNerveL OpticNerveR SpinalCordCl_C3 SpinalCordC4_C7 SpinalCordLl_L2 SpinalCordTl_T12 Skin_NFS EarExternalR EarExternalL Scalp SoleOfFootR ElbowAntecubitalL ElbowAntecubitalR ElbowOlecranonL ElbowOlecranonR FootAnkleNoSoleL FootAnkleNoSoleR HandDorsalSurfaceL HandDorsalSurfaceR HandVolarSurfaceL HandVolarSurfaceR KneePatellarL KneePatellarR

46 Skin-Knee, popliteal area-L KneePoplitealL 325 Skin-Knee, popliteal area-R KneePoplitealR 100 Skin-Periorbital area Periorbital 89 Skin-Sole of foot-L SoleOfFootL 2 Subcutaneous Tissue SubcutaneousTissue 63 Kidney, L KidneyL 355 Kidney, R KidneyR 58 Liver Liver 272 Meniscus (tibial), L MeniscusTibialL 455 Meniscus (tibial), R MeniscusTibialR 60 Spleen Spleen 75 Vertebra (cervical lamina) CervicalLamina

Appendix C

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Appendix D

Prototype Injury Profile Database

97

Appendix D - Prototype Injury Profile Database

CTPS - Prototype Injury Profile Database - Version 0 - Created by CPT Greg Creech -16 Nov98

Narrative Description. This database was created using injury data from ORCA. The intent was to create a database using ORCA injury data as a baseline for mapping the injury to other injury categorization and generation mechanisms or scenarios (e.g. ECC, DEPMEDS, JMSL). Two CTPS Phase I injury types were used for ECC mapping. These were Bloodloss and Gunshot Wound Causing Pneumothorax. ORCA to ECC mapping portrays three variations of Bloodloss and one Gunshot Wound Causing Pneumothorax, although many more variations are possible.

Database Structure. The injury profiles are listed in linear format, with values for each field separated by a comma. Each injury profile is separated by a space. For each injury profile database line read: ECC Weapon Type, DEPMEDS Patient Condition Code, Damaged Objects*, Damage Done**, Lung Capacity, Blood Loss Rate, Max AIS Code (MAIS), Probability of Survival, and JMSL State. Numbers greater than 1 but less than 1000 are in square centimeters, with the exception of blood loss rate, which is in liters per minute and lung damage, which is in cc and is labeled as such. The AIS Code consists of five digits, followed by a decimal point and one digit. Numbers less than one but greater than zero represent a percentage. Some percentage values are represented as greater than a certain percentage, some as less than, and some have a range (e.g. >.30, <.50, or .20- .40). Decreasing ranges for lung capacity represent a decrease in lung capacity from time zero to 5 minutes. A negative number entry for any field represents no data for that field.

*Note: When ORCA generates injuries, the output comes in the form of common names for the specific ORCA Body Components (OBC) damaged. These were translated into object names for each OBC. The database contains the object names. This field (and the corresponding damage done field, are the only text fields in the database. Also, they are the only fields with multiple entries. The number of entries (injured objects) will vary by injury type. For future reference, I will provide a separate Excel file which lists OBC #, common name, and object name. **Note: These entries relate one-to-one to the previously listed object names.

END OF NARRATIVE

#Bloodloss 27,176,Skin_NFS,SubcutaneousTissue,AbdomenNFS,Liver,KidneyR,UreterR,RenalR,Lo ngissimus,.36,1.60,<.20,fracture>.50,fracture>.50,damage,>.50,<.20,- 1,1.10,61809.5,.48,heavy

27,19,Skin_NFS,FaceNFS,SubcutaneousTissue,HeadNeckNFS,ExtemalMaxillaryL,Ante riorFacialL,IntemalJugularL,2.40,<.20,1.35,.20-.50,>.50,>.50,>.50,- l,.33,40505.4,.90,moderate

27,131 ,Skin_NFS,SubcutaneousTissue,MetatarsalBonesL,TibialisAnteriorL,LowerLegN FS_L,PeronealL,GastrocnemiusL, 1.69,2.37,comminuted fracture,>.80,.20- .50,>.501aceration,<.20,-l,.12,90605.2,.99,light

#Gunshot Wound Pneumothorax 24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc,>.50,.8-.4rl,50104.3,.97

Appendix E

Expanded Test Injury Profile Database

100

Appendix E - Expanded Test Injury Profile Database

#Bloodloss 27,176,Skin_NFS,SubcutaneousTissue,AbdomenNFS,Liver,KidneyR,UreterR,RenalR,Lo ngissimus,.36,1.60,<.20,fracture>.50,fi-acture>.50,damage,>.50,<.20,- 1,1.10,61809.5,.48,0

2749,Skin_NFS,FaceNFS,SubcutaneousTissue,HeadNeckNFS,ExternalMaxillaryL,Ante riorFacialL,InternalJugularL,2.40,<.20,1.35,.20-.50,>.505>.50,>.50,-l,.33,40505.4,.90,l

27,131 ,Skin_NFS,SubcutaneousTissue,MetatarsalBonesL,TibialisAnteriorL,LowerLegN FS_L,PeronealL,GastrocnemiusL, 1.69,2.37,comminuted fracture,>.80,.20- .50,>.501aceration,<.20,-l,.12,90605.2,.99,2

27,176,Skin_NFS,SubcutaneousTissue,AbdomenNFS,Liver,KidneyR,UreterR,RenalR,Lo ngissimus,.36,1.60,<.20,fracture>.50,fracture>.50,damage,>.50,<.20,- 1,1.10,61809.5,.48,3

27,19,Skin_NFS,FaceNFS,SubcutaneousTissue,HeadNeckNFS,ExternalMaxillaryL,Ante riorFacialL,InternalJugularL,2.40,<.20,1.35,.20-.50,>.50,>.50,>.50,-l,.33,40505.4,.90,4

27,131 ,SkinJSlFS,SubcutaneousTissue,MetatarsalBonesL,TibialisAnteriorL,LowerLegN FS_L,PeronealL,GastrocnemiusL, 1.69,2.37,comminuted fracture,>.80,.20- .50,>.501aceration,<.20,-l,.12,90605.2,.99,5

27,176,Skin_NFS,SubcutaneousTissue,AbdomenNFS,Liver,KidneyR,UreterR,RenalR,Lo ngissimus,.36,1.60,<.20,fracture>.50,fracrure>.50,damage,>.50,<.20,- 1,1.10,61809.5,-48,6

27,19,Skin_NFS,FaceNFS,SubcutaneousTissue,HeadNeckNFS,ExternalMaxillaryL,Ante riorFacialL,InternalJugularL,2.40,<.20,1.35,.20-.50,>.50,>.50,>.50,-l,.33,40505.4,.90,7

27,131 ,Skin_NFS,SubcutaneousTissue,MetatarsalBonesL,TibialisAnteriorL,LowerLegN FS_L,PeronealL,GastrocnemiusL, 1.69,2.37,comminuted fracture,>.80,.20- .505>.501aceration,<.20,-l,.12,90605.2,.99,8

27,176,Skin_NFS,SubcutaneousTissue,AbdomenNFS,Liver,KidneyR,UreterR,RenalR,Lo ngissimus,.36,1.60,<.20,fracture>.50,fi-acture>.50,damage,>.50,<.20,- 1,1.10,61809.5,-48,9

27,19,Skin_NFS,FaceNFS,SubcutaneousTissue,HeadNeclcNFS,ExtemalMaxillaryL,Ante riorFacialL,InternalJugularL,2.40,<.20,1.35,.20-.50,>.50,>.50,>.50,-l,.33,40505.4,.90,10

#Gunshot Wound Pneumothorax 24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc,>.50,.8-.4,-l,50104.3,.97,0

24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4-19,.20-.50,<.20,punchout,9.35,63.80cc,>.50,.8-.4,-l,50104.3,.97,l

24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc,>.50,.8-.4,-l,50104.3,.97,2

24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc,>.50,.8-.4,-l,50104.3,.97,3

24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc,>.50v8-.4,-l,50104.35.97,4

24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc,>.50v8-.4,-l,50104.35.97,5

24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc5>.50,.8-.4,-l,50104.3,.97,6

24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc5>.50,.8-.4,-l,50104.3,.97,7

24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc5>.50,.8-.4,-l,50104.3,.97,8

24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc,>.50,.8-.4,-l,50104.3,.97,9

24,88,Skin_NFS,PectoralisMajorR,ThoraxNFS,Rib,SubcutaneousTissue,MiddleLobe,Ple ura,4.19,.20-.50,<.20,punchout,9.35,63.80cc,>.50,.8-.4,-l,50104.3,.97,10

Appendix F

Patient Injury Data from CTPS Test Runs

103

Appendix F - Patient Injury Data from CTPS Test Runs

state number 7

CASUALTY CHARACTERISTICS:

Weapon Type: 27 Patient Condition code :176 SkinNFS: .36 SubcutaneousTissue: 1.60 AbdomenNFS: <.20 Liver: fracture>.50 KidneyR: fracture>.50 UreterR: damage RenalR: >.50 Longissimus: <.20 Lung Capacity : - 1 Blood Loss Rate : 1.10 MAX AIS Code: 61809.5 Probability of Survival: .48 JMSL STATE : 6

state number 10

CASUALTY CHARACTERISTICS:

Weapon Type: 27 Patient Condition code :176 Skin_NFS: .36 SubcutaneousTissue: 1.60 AbdomenNFS: <.20 Liver: fracture>.50 KidneyR: fracture>.50 UreterR: damage RenalR: >.50 Longissimus: <.20 Lung Capacity : - 1 Blood Loss Rate : 1.10 MAX AIS Code : 61809.5 Probability of Survival: .48 JMSL STATE : 9

State number 1

CASUALTY CHARACTERISTICS:

Weapon Type : 27 Patient Condition code :176 Skin_NFS: .36 SubcutaneousTissue: 1.60 AbdomenNFS: <.20 Liver: fracture>.50 KidneyR: fracture>.50 UreterR: damage RenalR: >.50 Longissimus: <.20 Lung Capacity : - 1 Blood Loss Rate : 1.10 MAX AIS Code: 61809.5 Probability of Survival: .48 JMSL STATE : 0

state number 11

CASUALTY CHARACTERISTICS:

Weapon Type : 27 Patient Condition code :19 Skin_NFS: 2.40 FaceNFS: <.20 SubcutaneousTissue: 1.35 HeadNeckNFS: .20-.50 ExternalMaxillaryL: >.50 AnteriorFacialL: >.50 Internal JugularL: >.50 Lung Capacity: -1 Blood Loss Rate : .33 MAX AIS Code : 40505.4 Probability of Survival: .90 JMSL STATE : 10

State number 8

CASUALTY CHARACTERISTICS:

Weapon Type: 27 Patient Condition code :19 Skin_NFS: 2.40 FaceNFS: <.20 SubcutaneousTissue: 1.35 HeadNeckNFS: .20-.50 ExtemalMaxillaryL: >.50 AnteriorFacialL: >.50 Internal JugularL: >.50 Lung Capacity: -1 Blood Loss Rate : .33 MAX AIS Code : 40505.4 Probability of Survival: .90 JMSL STATE : 7

state number 10

CASUALTY CHARACTERISTICS:

Weapon Type : 27 Patient Condition code :176 SkinNFS: .36 SubcutaneousTissue: 1.60 AbdomenNFS: <.20 Liver: fracture>.50 KidneyR: fracture>.50 UreterR: damage RenalR: >.50 Longissimus: <.20 Lung Capacity : - 1 Blood Loss Rate : 1.10 MAX AIS Code : 61809.5 Probability of Survival: .48 JMSL STATE : 9

State number 3

CASUALTY CHARACTERISTICS:

Weapon Type: 27 Patient Condition code : 131 Skin_NFS: 1.69 SubcutaneousTissue: 2.37 MetatarsalBonesL: comminuted fracture TibialisAnteriorL: >.80 LowerLegNFS_L: .20-.50 PeronealL: >.501aceration GastrocnemiusL: <.20 Lung Capacity: -1 Blood Loss Rate : .12 MAX AIS Code : 90605.2 Probability of Survival: .99 JMSL STATE : 2

state number 10

CASUALTY CHARACTERISTICS:

Weapon Type : 27 Patient Condition code :176 Skin_NFS: .36 SubcutaneousTissue: 1.60 AbdomenNFS: <.20 Liver: fracture>.50 KidneyR: fracture>.50 UreterR: damage RenalR: >.50 Longissimus: <.20 Lung Capacity : - 1 Blood Loss Rate : 1.10 MAX AIS Code : 61809.5 Probability of Survival: .48 JMSL STATE : 9

State number 9

CASUALTY CHARACTERISTICS:

Weapon Type : 27 Patient Condition code :131 Skin_NFS: 1.69 SubcutaneousTissue: 2.37 MetatarsalBonesL: comminuted fracture TibialisAnteriorL: > .80 LowerLegNFS_L: .20- .50 PeronealL: >.501aceration GastrocnemiusL: <.20 Lung Capacity: -1 Blood Loss Rate : .12 MAX AIS Code : 90605.2 Probability of Survival: .99 JMSL STATE : 8

state number 6

CASUALTY CHARACTERISTICS:

Weapon Type : 27 Patient Condition code :131 Skin_NFS: 1.69 SubcutaneousTissue: 2.37 MetatarsalBonesL: comminuted fracture TibialisAnteriorL: >.80 Lo werLegNF SJL: PeronealL:

.20-.50 >.501aceration

GastrocnemiusL: <.20 Lung Capacity: Blood Loss Rate:

-1 .12

MAX AIS Code : 90605.2 Probability of Survival: .99 JMSL STATE : 5

State number 10

CASUALTY CHARACTERISTICS:

Weapon Type: 27 Patient Condition code :176 SkinNFS: .36 SubcutaneousTissue: 1.60 AbdomenNFS: <.20 Liver: fracture>.50 KidneyR: fracture>.50 UreterR: damage RenalR: >.50 Longissimus: <.20 Lung Capacity : - 1 Blood Loss Rate : 1.10 MAX AIS Code: 61809.5 Probability of Survival: .48 JMSL STATE : 9

state number 2

CASUALTY CHARACTERISTICS:

Weapon Type : 27 Patient Condition code :19 SkinjNTS: 2.40 FaceNFS: <.20 SubcutaneousTissue: 1.35 HeadNeckNFS: .20-.50 ExternalMaxillaryL: >.50 AnteriorFacialL: >.50 Internal JugularL: >.50 Lung Capacity: -1 Blood Loss Rate : .33 MAX AIS Code : 40505.4 Probability of Survival: .90 JMSL STATE : 1

State number 5

CASUALTY CHARACTERISTICS:

Weapon Type : 27 Patient Condition code :19 Skin_NFS: 2.40 FaceNFS: <.20 SubcutaneousTissue: 1.35 HeadNeckNFS: .20-.50 ExternalMaxillaryL: >.50 AnteriorFacialL: >.50 Internal JugularL: >.50 Lung Capacity: -1 Blood Loss Rate : .33 MAX AIS Code : 40505.4 Probability of Survival: .90 JMSL STATE : 4

state number 1

CASUALTY CHARACTERISTICS:

Weapon Type: 27 Patient Condition code : 176 Skin_NFS: .36 SubcutaneousTissue: 1.60 AbdomenNFS: <.20 Liver: fracture>.50 KidneyR: fracture>.50 UreterR: damage RenalR: >.50 Longissimus: <.20 Lung Capacity : - 1 Blood Loss Rate : 1.10 MAX AIS Code: 61809.5 Probability of Survival: .48 JMSL STATE : 0

State number 1

CASUALTY CHARACTERISTICS:

Weapon Type: 27 Patient Condition code :176 SkinNFS: .36 SubcutaneousTissue: 1.60 AbdomenNFS: <.20 Liver: fracture>.50 KidneyR: fracture>.50 UreterR: damage RenalR: >.50 Longissimus: <.20 Lung Capacity : - 1 Blood Loss Rate : 1.10 MAX AIS Code: 61809.5 Probability of Survival: .48 JMSL STATE : 0

state number 7

CASUALTY CHARACTERISTICS:

Weapon Type : 27 Patient Condition code :176 Skin_NFS: .36 SubcutaneousTissue: 1.60 AbdomenNFS: <.20 Liver: fracture>.50 KidneyR: fracture>.50 UreterR: damage RenalR: >.50 Longissimus: <.20 Lung Capacity : - 1 Blood Loss Rate : 1.10 MAX AIS Code: 61809.5 Probability of Survival: .48 JMSL STATE : 6

State number 6

CASUALTY CHARACTERISTICS:

Weapon Type: 27 Patient Condition code : 131 Skin_NFS: 1.69 SubcutaneousTissue: 2.37 MetatarsalBonesL: comminuted fracture TibialisAnteriorL: > .80 LowerLegNFS_L: PeronealL:

.20-.50 >.501aceration

GastrocnemiusL: <.20 Lung Capacity: Blood Loss Rate :

-1 .12

MAX AIS Code : 90605.2 Probability of Survival: .99 JMSL STATE : 5

state number 1

CASUALTY CHARACTERISTICS:

Weapon Type: 27 Patient Condition code :176 Skin_NFS: .36 SubcutaneousTissue: 1.60 AbdomenNFS: <.20 Liver: fracture>.50 KidneyR: fracture>.50 UreterR: damage RenalR: >.50 Longissimus: <.20 Lung Capacity : - 1 Blood Loss Rate : 1.10 MAX AIS Code : 61809.5 Probability of Survival: .48 JMSL STATE : 0

State number 9

CASUALTY CHARACTERISTICS:

Weapon Type: 24 Patient Condition code :88 Skin_NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue: 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate: -1 MAX AIS Code: 50104.3 Probability of Survival: .97 JMSL STATE : 8

state number 5

CASUALTY CHARACTERISTICS:

Weapon Type : 24 Patient Condition code :88 Skin NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue : 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate: -1 MAX AIS Code: 50104.3 Probability of Survival: .97 JMSL STATE : 4

State number 8

CASUALTY CHARACTERISTICS:

Weapon Type: 24 Patient Condition code :88 Skin_NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue: 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate : -1 MAX AIS Code : 50104.3 Probability of Survival: .97 JMSL STATE : 7

state number 6

CASUALTY CHARACTERISTICS:

Weapon Type : 24 Patient Condition code :88 Skin_NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue: 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate : -1 MAX AIS Code: 50104.3 Probability of Survival: .97 JMSL STATE : 5

State number 7

CASUALTY CHARACTERISTICS:

Weapon Type: 24 Patient Condition code :88 Skin_NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue: 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate: -1 MAX AIS Code: 50104.3 Probability of Survival: .97 JMSL STATE : 6

state number 2

CASUALTY CHARACTERISTICS:

Weapon Type : 24 Patient Condition code :88 Skin_NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue: 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate : -1 MAX AIS Code: 50104.3 Probability of Survival: .97 JMSL STATE : 1

State number 1

CASUALTY CHARACTERISTICS:

Weapon Type: 24 Patient Condition code :88 Skin NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue : 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate: -1 MAX AIS Code : 50104.3 Probability of Survival: .97 JMSL STATE : 0

state number 3

CASUALTY CHARACTERISTICS:

Weapon Type : 24 Patient Condition code :88 Skin_NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue: 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate : -1 MAX AIS Code: 50104.3 Probability of Survival: .97 JMSL STATE : 2

State number 3

CASUALTY CHARACTERISTICS:

Weapon Type: 24 Patient Condition code :88 Skin_NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue: 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate: -1 MAX AIS Code : 50104.3 Probability of Survival: .97 JMSL STATE : 2

state number 8

CASUALTY CHARACTERISTICS:

Weapon Type : 24 Patient Condition code :88 Skin_NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue: 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate: -1 MAX AIS Code: 50104.3 Probability of Survival: .97 JMSL STATE : 7

State number 11

CASUALTY CHARACTERISTICS:

Weapon Type : 24 Patient Condition code :88 Skin_NFS: 4.19 PectoralisMajorR: .20-.50 ThoraxNFS: <.20 Rib: punchout SubcutaneousTissue: 9.35 MiddleLobe: 63.80cc Pleura: >.50 Lung Capacity: .8-.4 Blood Loss Rate : -1 MAX AIS Code: 50104.3 Probability of Survival: .97 JMSL STATE : 10

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