Technological Advancement in the Surgical Treatment of War Wounds

Eric Elster MD FACSCAPT MC USN

Professor and ChairmanNorman M. Rich Department of Surgery

Uniformed Services University Naval Medical Research Center

Walter Reed National Military Medical Center

Multiple Injuries in Combat Wounded

“Following massive injury, physiological responses that were appropriate when

applied locally become inappropriate and beyondregulation when systemically activated.”

Injury Cycle: Target areas for personalized treatment and

improved medical decision-making

Timing of regenerative medicine

Heterotopic ossification (HO) prophylaxis

Assess systemic responsePersonalized treatment of

systemic response and bioburden

Assess bioburdenVTE prophylaxis and therapy

Maximize return to duty

Assess tissue viability

Immediate Responseto injury

Debridement and Critical CareDebridement and Critical Care

Acut

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susc

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LLELLE RLERLE LLELLE RLERLE

LLELLE RLERLELLELLE RLERLE

LLELLE RLERLE

Bilateral lower-extremity amputationsBilateral lower-extremity amputations

Adaptive vs. Maladaptive Response to Injury

Tools to Understand Inflammatory Response

Real-time PCRReal-time PCR

Multiplex Protein AssayMultiplex Protein Assay

Raman SpectroscopyRaman Spectroscopy

FTIR ImagingFTIR Imaging

ThermographyThermography

Visible Reflectance Imaging

Visible Reflectance Imaging

Bayesian Belief Network modelingBayesian Belief

Network modeling

WRNMMC/NMRC Clinical Trials

• Clinical Trials– Biomarkers pilot completed (n = 75)– Orthopedic predictors (n = 200)– Wound imaging (n = 60)– FDA HO RCT COX-2 (n = 10)– FDA Prospective biomarker (start fall)

• Collaborative Efforts– Emory/Washington University – biomarker– Cleveland Clinic – HO

• Research Labs– NMRC – Central Player– WRAIR – Microbiology support – USUHS – Nexus Surgical Research

Serum: •Cytokines•Chemokines•Proteases•2D Gels (UC-Davis)

Tissue biopsy: •Wound healing associated genes•Osteogenesis•Pathogen specific PCR•Quantitative bacteriology•Pathogen Sequencing (LLNL/WRAIR)

Wound effluent: •Cytokines•Chemokines•Proteases•2D Gels (UC-Davis)

Sample Collection

Timing of Wound Closure (WounDXTM)

Serum MCP-1 Debridement 1

Serum MCP-1 Debridement 3

Serum IP-10 Debridement 2

Effluent MCP-1 Debridement 3

Serum IP-10 Debridement 3

Serum MCP-1 Debridement 2

Serum IP-10 Debridement 1

Serum MCP-1 Closure

Effluent IL-5 Debridement 1

Wound Outcome Normal Healing 85%

Impaired Healing 14%

Serum IL-6

Effluent RANTES

Effluent IL-5

Effluent RANTES

Decreased Expression Increased Expression

Probabilistic (Bayesian) Model

Wound Vac Tissue biopsy Serum

Wound Status

Systemic Response

• Systems biology analysis has demonstrated that biochemical markers predict wound outcome

• Predictive biomarkers of wounds may reduce the number of required surgical procedures (washouts in the OR)

• Key to correct timing of Regenerative Medicine strategies

Prospective WounDX to start this fall in military and civilian sites

Plast Reconstr Surg. 2011 Jan;127 Suppl 1:21S-26S.

Biomarker Assessment of Combat Wounded

Inflammatory Biomarkers in Combat Wound Healing

Annals Surg. 2009 Apr;197(4):515-24

WounDXTM

Prospective Biomarker Study

• Internal AUCs0.82 ± 0.015

• Cross-Validated AUCs0.71 ± 0.04

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195201199200198202599599102106137416811771186602607329332333763138154833678714878008699768728761392330341343337344345347375389108058243697817961797980981814794

0-0.5 0.5

Models:

Models Accuracy

KNN12 C V (average) 91% ± 8.8

C V 1 80.00%

C V 2 87.50%

C V 3 100%

C V 4 100%

C V 5 87.50%

Dehisced

Healed

No class

Error

Dehisced Healed

21 2

2 18

0 0

2 2

C onfusion matrix for: KNN12 C V (average)

True classes

Predicted

classes

PC2

1.2

1

0.8

0.6

0.4

0.2

0

-0.2

-0.4

-0.6

-0.8

-1

-1.2

PC110-1

Spot Maps (Score Plot)

PC2

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

-0.05

-0.1

-0.15

-0.2

-0.25

-0.3

-0.35

PC10.350.30.250.20.150.10.050-0.05-0.1-0.15

Proteins (Loading Plot)

DehiscedHealed

Discriminant Analysis

(using 5 folds for cross validation)

PC2

1.2

1

0.8

0.6

0.4

0.2

0

-0.2

-0.4

-0.6

-0.8

-1

-1.2

PC110-1-2

Spot Maps (Score Plot)

PC2

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

-0.05

-0.1

-0.15

-0.2

-0.25

-0.3

-0.35

PC10.40.30.20.10-0.1

Proteins (Loading Plot)

DehiscedHealedDehiscedHealedDehiscedHealed

2D Gel Analysis – UC Davis

Protein Biomarker Discovery

Comparison(left vs. right)

Differential Proteins

Markers selected

Accuracy (%)

Healed Dehisced 52 9 83.83 ± 2.8

Healed vs. Dehisced Discriminate Markers

Spot No. Gene Name Protein Name

95 PCH17 Protocadherin 17

99 STK36 Serine/threonine protein kinase 3

168 PTPRJ Receptor type tyrosine protein phosphotase precursor

195 CP Ceruloplasmin

198 CP Ceruloplasmin

341 C3 Complement C3

343 C3 Complement C3

375 XPNPEP1 Xaa-Pro aminopeptidase 1

872 SERPINA3 Alpha-1-antichymotrypsin

A

B

Systemic Response to Combat Injury and Wound Colonization

• Characterize the systemic and local wound environment

• Correlate objective measures with clinical outcome

• Develop predictive models of critical colonization

• Direct treatment approaches

Am J Surg. 2010 Oct;200(4):489-95.

Wound Colonization and Inflammatory Response

<103 CFU/g - Undetectable

103 CFU/g - Colonized

104 CFU/g - Critically Colonized

>105 CFU/g - Infection

*p<0.05 compared to <103 CFU/g

*

*

*

*

*

**** *

*

*

*

*

** *

**

IL-6, IL10 IL-8, IP-10, MIP-1a MMP-3, -7, -13

IL-1b, IL-6, IL10 IL-8, MIP-1a

Eff

luen

tS

erum

Surg Infect (Larchmt). 2011 Oct;12(5):351-7. Surg Infect (Larchmt). 2011 Oct;12(5):351-7.

Response to Emerging Patterns: Predicting IFI in Complex Dismounted Blast Injuries

Cytokine

Mean IFI SD IFI Mean

ControlSD

Control

Difference in Means

P Value Cytokine

Mean IFI SD IFI Mean

ControlSD

Control

Difference in Means

P Value

IL-1B 155.46 56.93 70.89 18.51 84.57 0.03 IL-7 104.02 37.55 76.36 22.44 27.65 0.253EGF 60.85 19.45 117.37 43.75 -56.52 0.056 IL-15 95.81 39.36 63.91 32.09 31.89 0.256

TNF-a 128.43 48.27 60.64 31.89 67.79 0.058 MIP-1B 107.86 49.73 74.88 28.31 32.99 0.293IL-17 98.46 3.08 66.29 30.23 32.17 0.079 MCP-1 123.08 46.08 79.94 59.1 43.14 0.293G-CSF 100.47 14.67 75.97 18.8 24.5 0.086 IL-3 100 0 99 2 1 0.356IL-RA 111.46 16.53 80.3 28.13 31.16 0.105 IL-12 103.59 16.29 93.25 12.88 10.34 0.358IFN-y 136.86 30.32 106.89 9.08 29.97 0.107 IL-10 143.67 114.41 92.02 9.09 51.65 0.403IL-4 97.05 15.46 77.25 18.4 19.81 0.15 MIG 118.59 49.53 92.23 31.28 26.35 0.403IL-2 150.95 110.89 63.75 21.22 87.2 0.173 IL-13 153.72 158.59 95.9 19.23 57.82 0.496

MIP-1a 106.02 31.28 77.6 20.76 28.43 0.181 IL-1a 135.66 138.44 89.43 12.71 46.23 0.531IFN-a 109.18 37.52 81.38 8.1 27.79 0.198 IL-5 92.22 9.03 85.66 17.64 6.56 0.532

IL-8 108.24 41.98 56.55 58.68 51.68 0.202 GM-CSF 100 0 91.72 29.13 8.28 0.59

FGF-Basic 108.2 43.77 69.42 35.46 38.78 0.218 HGF 111.15 65.52 91.89 54.39 19.26 0.667

VEGF 108.04 40.75 73.22 34.69 34.82 0.241 RANTES 83.1 10.29 76.31 38.2 6.79 0.743

IP-10 109.05 53.91 72.22 17.63 36.83 0.242 IL-2R 95.07 59.98 90.38 52.62 4.7 0.91Eotaxin 121.79 38.29 93.82 21.75 27.97 0.251 IL-6 105.02 23.93 103.59 171.89 1.43 0.987

Viral taxIDs with mapped sequence data (0.02% of all reads) for sample KS702EBON

Whole-genome approach allows for ID of viral sequence

OIF/OEF Injuries and HO:Risk Factors

1. Potter BK et al. J Bone Joint Surg Am. 2007;89:476-86.2. Forsberg JA et al. J Bone Joint Surg Am 2009; 91: 1084-1091

• 63% of all combat-related amputations1

– Amputation in zone of injury– Blast mechanism of injury

• 65% of all major extremity injuries2

Heterotopic Ossification

•More prevalent in OIF/OEF casualties than in similar civilian trauma (60% vs. 20%)

•An ongoing problem for rehabilitation/prosthetics

Wound effluent promotes bone growth in culture

Basic Science Meets Clinical Care

Clinical Observation

Basic ResearchStem Cell

Differentiation

Blast Effects On HO (Animal

Model)

6.1 Clinic

Laboratory

Assessment of Novel Treatments

to prevent HOSmall animal

model

6.2

Biomarkers Predictive of HO

in Casualties

6.3

A basic/applied Research Program

Randomized trial underway to assess efficacy of COX-2 inhibitors and biomarkers

Inflammatory Biomarkers and HO

*IP-10 predictive of not developing HO Evans, Brown et al, J Orthop Trauma. 2012 May 14.

Tissue Biomarkers

Osteogenic Progenitor Cells Are Present in Patients with HO

J Bone Joint Surg Am. 2011 Jun 15;93(12):1122-31. J Bone Joint Surg Am. 2011 Jun 15;93(12):1122-31.

Bedside Bench

Celecoxib-HO Prophylaxis PRT• 100 patients

– Major combat-related penetrating extremity injury(s)

– LRMC WRNMCC• Primary endpoints

• HO incidence• HO severity

• Secondary endpoints:• Rate of wound failure• Time to fracture union• Rate of nonunions• Rate of drug–related

complications

HO Polytrauma Model

• Small animal mode• Blast tube (systemic)• Amputation or fracture (local)• Biobuden

Spectroscopic analysis of injury

6.1

Image Analysis of Tissue Integrity – Real Time Feedback

Clinic

Laboratory

S&T Gap/Warfighting Requirement: Improved wound diagnostics

Current State-of-the-Art:Visual inspection of wounds by surgeons

Anticipated Impact: Save tissue that would have been surgically otherwise removedDecreased costsImproved patient outcomeImproved function from preservation of tissueDirect regenerative medicine approaches

Product/Deliverable: Enhanced diagnosticsOptic markers of tissue integrity

Image Enhancement and

Integration6.2

Preclinical assessment of

diagnostic imaging of

wounds6.3

Raman Fiber Probe Data Collection

Approximately 1 cm2 tissue biopsy is excised from the center of the wound bed.

Tissue is fixed in 10% neutral buffered formalin for storage.

Prior to spectral acquisition, samples are rinsed in 0.9% NaCl saline solution.

1 2

1800 1600 1400 1200 1000 800 600 1800 1600 1400 1200 1000 800 600

Raman Shift (cm-1)

Examine multiple spots across the tissue.

40 accumulations, 5s spectrum

1

2

Peakfitting for Spectral Deconvolution

60080010001200140016001800

Raman Shift (cm-1)

1665

1555

1445

1320

1250

1040 10

04

1125

1380

940

Raman Shift (cm -1) Vibrational Band Assignment Component

860 (C-C) nucleic acids 920,940 (C-N), (C-C) nucleic acids, keratin

1004 (C-C) ring phenylalanine1040 (C-C) skeletal glycogen, keratin1125 (C-C), (C-N) nucleic acids, protein1250 (C-N) and N-H); Amide III protein1320 (CH 2) twisting nucleic acids, protein1445 (CH 3) and (CH 2) scissoring protein1555 aromatic amino acids, heme1665 C=O); Amide I protein

0.62 mm

A Raman spectrum is collected at each yellow cross, as illustrated on the image below.

PCA is performed to

extract factors and score images.

Factors Score Images

0

0.5

1

0

0.5

1

0

0.5

1

500 1000 15000

0.20.40.60.8

500 1000 15000

0.20.40.60.8

500 1000 15000

0.5

1

500 1000 15000.20.40.60.8

1

high intensity

low intensity

Raman Shift (cm-1)

Factors indicate what is present, and score images indicate where the factors are present and how much of the factors are present.

This process was performed

to extract tissue

“components” for the first and

final debridement of

each patient included in the

Raman mapping study.

Raman Shift (cm-1)

600 800 1000 1200 1400 1600 18000

0.2

0.4

0.6

0.8

1 144

4 1304 166510041028

1609

1076

First Debridement Last Debridement

500 1000 1500

0

0.2

0.4

0.6

0.81444

1300

1665

1004

1032

1240

1068

Raman Shift (cm-1)

920860

1665

1445

13101668

1004 1035,

1080

860 1242

Raman Shift (cm-1)

600 800 1000 1200 1400 1600 1800

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1570

1609

1210

Last debridementFirst debridement

1665

1445

1310 16681004

1035,1080

860 1242

1570

1609 1210

Curve-fitting of the tissue

“components” enables band

area ratio calculations.

Difference between 1665/1448 band area ratios: -1.8%; Transcript data collaborates spectroscopy Wound Repair Regen. 2010 Jun 8.

Early Mineralization/HO Detected by Raman

Normal muscle

Combat-injured muscle

Muscle with pre-HO(gritty soft-tissue; no radiographic evidence)

Mineral vibrational bands (carbonated apatite)

J Bone Joint Surg Am. 2010 Dec;92 Suppl 2:74-89. J Bone Joint Surg Am. 2010 Dec;92 Suppl 2:74-89.

Adapting to Injury (not treating)

Timing - WounDX

Bioburden – 16/18S

Targeted therapy – PCR assay

Debridement Adequate - Raman

Peri-op risk assessment(VTE, VAP, sepsis)

Immunomodulation

FTY720 - Novartis

Immune Modulation and Hemorrhage

Lymphocyte depletional or sequestration agents given at the time of severe hemorrhage will attenuate innate immune molecular and cellular activation following hemorrhage

Control (n=9)

PATG (n=8)

FTY720 (n=9)

In Advanced Development for treatment of shock in closed, laparoscopically-induced, hemorrhage in nonhuman primates (6.4)

Lymphocyte Immunomodulation Attenuates Innate And Cellular Response

Hawksworth JS, Graybill JC , et al. PLoS ONE 7(4): e34224.

Laparoscopic Traumatic Liver HS Injury Model

Time 0: Initiation of liver injury/hemorrhage

Time 15 minutes post injury: Start resuscitation with test material

Time 15 – 120 minutes post injury: Pre-hospital phase with up to a total of 20cc/kg of resuscitation fluid

Time 120 minutes post injury: Begin hospital care with repair of liver laceration

Time 120 – 240 minutes post injury: Simulation of hospital care with continuous monitoring and resuscitation and blood transfusion

Time 240 minutes post injury:Animals awoken from anesthesia and transferred to individual housing cages

Time 24 hr-2 weeks post injury: On each post operative day blood samples drawn for labs (other than ABG) and evaluation. At day 14 post injury the animals will be euthanized, necropsy and tissue samples collected for histologic and RNA analysis obtained.

Program Benefits•Accelerating care with earlier RTD•Significant cost reduction•USUHS based joint effort (Navy/Army/Air Force)•Better timing and selection of regenerative medicine approaches•Introduction of patient-centered personalized medicine•Information and outcomes, rather than hypothesis based•Civilian translation

–Lessons learned change practice–Train next generation (Military and Civilian)–Improvements cycle back into Military Medicine

Collect Data DevelopModels

ValidateModels Treat Iterate

• 20 Surgical/Orthopedic Residents trained– Jonathan Forsberg LT MC USN– Jason Hawksworth, CPT MC USA – Suzannes Gillern, CPT MC USA– John Graybill, CPT MC USA– Korboi, Evans, CPT MC USA– Kennett Moses, CPT MC USA– Kristin Stevens, LT MC USN– Paul Hwang, CPT MC USA– Sam Phinney, CPT MC USA– Fred O’Brien, CPT MC USA– Alan Strawn, LT MC USN– Maridelle Millendez, CPT MC USA– Steven Grijalva, LT MC USN– Keith Alferi, CPT MC USA– Jason Radowsky, CPT MC USA– Earl Lee, CPT MC USA– Elizabeth Polfer, CPT MC USA– Diego Vincente, LT MC USN– Benjamin Bograd, LT MC USN– Joseph Caruso, CPT MC USA

• 5 Medical Students trained– Edward Utz– Scott Wagner – Kevin Wilson– Philip Yam– Ryan Kachur

• Staff Support/Development– Forest Sheppard, CDR MC USA– Shawn Safford, CDR MC USA– Jonathan Forsberg, CDR MC USA– Kyle Potter, LTC MC USA

Training the Next Generation

Training the Next Generation

2013 Winner, Navy-wide Resident Research Competition, CPT Elizabeth Polfer

2012 Winner, WRNMMC Research Competition, CPT Keith Alferi

2011 Winner, Sheikh Zayed Institute Award for Innovative Surgery, CPT Mar Melindez

2011 Navy-wide Resident Research CIP Winner, LT Alan Strawn

2010 Navy-wide Resident Research Competition Winner, CPT Fred O’Brien

2010 Baugh Research Award, LT Kristin Stevens

2010 USUHS Charles Hufnagel Research Award, CPT Sam Phinney

2009 Diane S. Malcolm Research Award, CPT Korboi Evans

2009 Founder’s Award, Society of Military Orthopedic Surgery, CPT Korboi Evans

2009 USUHS Charles Hufnagel Research Award, CPT Korboi Evans

2009 AAS Outstanding Medical Student Award, American Surgical Congress, ENS Edward Utz

2008 Young Investigator Award, American Transplant Congress, CPT Jason Hawksworth

2008 Navy-wide Resident Research Competition Winner, CPT Jason Hawksworth

2007 Navy-wide Resident Research Competition Winner ,LT Jonathan Forsberg

Combat Wounded Civilian Critical Care

• More than 5 million Americans are admitted to Intensive Care Units each year. • Critical care saves lives but…

• is complex• error prone• very expensive.

• Integrated effort can accelerate knowledge between military and civilian trauma facilities for the benefit of both.• Applicable to land-based, HA/DR, or Sea Based personnel

Critical Care:Lessons from the battlefield translate to

civilian rehabilitation and back again

Concept1. Apply best of breed technologies in

• biomarker analysis, • informatics• medical technology,

2. Clinical Decision Support tools can be developed that can optimize and personalize treatment using:• patient-specific clinical variables combined

with local and systemic biomarkers3. Goal: maximize patient outcomes while

minimizing complications.

Research Transitions to Practice

Multiplex Assays &Data Analysis

Change in clinical practicePatient samples & Data

Research/RDTE Clinical Care/OM&N

Research Lab Clinical Lab

• Current status• Population based studies• Hypothesis driven• Best judgment• 85% solution

• Future• Decisions based on biology• Personalized solutions• Patient centered medicine• 95 – 99% solution

Registry Database / Application

Samples go to lab

Registry Database / Application

Clinical data--to registries

Biomarker data--to registries

A.I. processes information in real time

Models power persistent, ubiquitous CDS applications

Clinician makes more informed decisions using personalized approach

Hospital

Lab Data

Decisions

Serum MCP-1 Debridement 1

Serum MCP-1 Debridement 3

Serum IP-10 Debridement 2

Effluent MCP-1 Debridement 3

Serum IP-10 Debridement 3

Serum MCP-1 Debridement 2

Serum IP-10 Debridement 1

Serum MCP-1 Closure

Effluent IL-5 Debridement 1

Wound Outcome Normal Healing 85%

Impaired Healing 14%

Serum IL-6

Effluent RANTES

Effluent IL-5

Effluent RANTES

Decreased Expression Increased Expression Models are

created &validated

Acknowledgements

• The multidisciplinary care of these patients would not have been possible without the dedicated efforts of everyone at WRAMC and NNMC. Both civilian and military personnel have rendered skilled and compassionate care for these casualties. All of our efforts are dedicated to those who have been placed in harm’s way for the good of our nation.

• The views expressed are those of the authors and do not reflect the official policy of the Department of the Navy, Army, the Department of Defense, or the US Government.

• Funding provided by US Navy BUMED Advanced Development Program , Office of Naval Research and the US Army Medical Research and Material Command

Acknowledgements• NMRC

– Doug Tadaki– Thomas Davis– Trevor Brown– Nicole Crane– Chris Eisemann– Steve Ahlers– Forest Sheppard– Darren Fryer– Crystal Gifford– Jeff Hyde– Fred Gage– Al Black– Nancy Porterfield– Mihert Amare– Steven Zins

• WRAIR– Paul Keiser– David Craft– Robert Bowden

• WRNMMC– Jason Hawksworth– Jim Dunne– Jonathan Forsberg– Carlos Rodriguez– Phil Perdue– John Denobile– Craig Shriver– Stephanie Sincock– Kyle Potter– Romney Anderson– Alexander Stojadinovic– Dan Valiak– Chris Graybill– Sue Gillern

• USUHS– Ted Utz– David Burris– Norman Rich