Technological Advancement in the
Surgical Treatment of War Wounds
Eric Elster MD FACS
CAPT MC USN
Professor and Chairman
Norman 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 beyond
regulation when systemically activated.”
Immediate Response
to injury
Timing of regenerative
medicine
Injury Cycle:
Target areas
for personalized treatment and
improved medical decision-making
Debridement and Critical Care
Personalized
treatment of
systemic response
and bioburden
VTE prophylaxis and therapy
Assess bioburden
Acute Resuscitation
Heterotopic ossification (HO)
prophylaxis
Regenerative Medicine
Maximize return
to duty
Assess tissue viability
Assess systemic response
Adaptive vs. Maladaptive Response to Injury
Bilateral lower-extremity amputations
LLE
RLE
LLE
RLE
LLE
RLE
LLE
RLE
LLE
RLE
Tools to Understand Inflammatory
Response
Real-time PCR
Multiplex Protein Assay
Raman Spectroscopy
Visible Reflectance
Imaging
Bayesian Belief
Network modeling
FTIR Imaging
Thermography
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
Sample Collection
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)
Timing of Wound Closure (WounDXTM)
Wound Vac
Tissue biopsy
Wound
Status
•
•
•
Serum
Systemic
Response
Decreased Expression
Increased Expression
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
Wound Outcome
Normal Healing 85%
Impaired Healing 14%
Serum IP-10
Debridement 3
Effluent IL-5
Serum MCP-1
Serum MCP-1
Debridement 1
Debridement 3
Serum IP-10
Effluent MCP-1
Debridement 3
Debridement 2
Effluent RANTES
Serum IL-6
Serum MCP-1
Effluent IL-5
Effluent RANTES
Debridement 2
Debridement 1
Serum IP-10
Serum MCP-1
Debridement 1
Closure
Probabilistic (Bayesian) Model
Prospective WounDX to start this fall in military and civilian sites
Biomarker Assessment of Combat Wounded
Plast Reconstr Surg. 2011 Jan;127 Suppl 1:21S-26S.
Inflammatory Biomarkers in Combat
Wound Healing
Annals Surg. 2009 Apr;197(4):515-24
WounDXTM
Prospective Biomarker Study
• Internal AUCs
0.82 ± 0.015
• Cross-Validated AUCs
0.71 ± 0.04
-1
-1.2
CV 2
CV 3
CV 4
CV 5
PC2
Dehisced
Healed
0.35
-1
0.25
Models:
Models
0.15
KNN12 C V (average) 0.1
CV 1
0.05
-0.1
-0.15
-0.2
-0.5
0
PC1
0
100%
-0.05
100%
87.50%
0
0.3
1
0.2
Accuracy
87.50%
Predicted
classes
0.5
C onfusion matrix for: KNN12 C V (average)
91% ± 8.8
80.00%
-0.3
-0.35
-0.15
True classes
Dehisced Healed
Dehisced 21
2
Healed
2
18
No class
0
0
Error
2
2
-0.1 -0.05
0
0.05
0.1
PC1
2 0 1 2 _0 9 1 9 8 3 1 5 2 C y3 (edited).tif
2 0 1 2 _0 8 1 7 8 8 9 0 3 C y5 (edited).tif
2 0 1 2 _0 8 1 7 8 8 9 0 3 C y3 (edited).tif
2 0 1 2 _0 8 2 1 8 8 8 9 9 C y3 (edited).tif
2 0 1 2 _0 8 1 7 8 8 9 0 0 C y5 (edited).tif
2 0 1 2 _0 8 2 1 8 8 8 9 8 C y5 (edited).tif
0.15
2 0 1 2 _0 8 2 1 8 8 8 9 5 C y5 (edited).tif
2 0 1 2 _0 8 2 1 8 8 8 9 5 C y3 (edited).tif
2 0 1 2 _0 9 1 9 8 9 3 6 3 C y5 (edited).tif
2 0 1 2 _0 9 1 9 8 3 1 5 2 C y5 (edited).tif
0.2
2 0 1 2 _0 8 2 1 8 8 8 9 6 C y3 (edited).tif
2 0 1 2 _0 9 1 9 8 9 3 6 4 C y3 (edited).tif
0.25
(using 5 folds for
cross validation)
2 0 1 2 _0 9 1 9 8 9 3 6 3 C y3 (edited).tif
2 0 1 2 _0 9 1 9 8 9 3 6 4 C y5 (edited).tif
2 0 1 2 _0 9 1 9 8 9 3 6 5 C y3 (edited).tif
2 0 1 2 _0 8 2 1 8 8 8 9 6 C y5 (edited).tif
0.3
Discriminant Analysis
2 0 1 2 _0 8 1 7 8 8 9 0 1 C y3 (edited).tif
2 0 1 2 _0 9 1 9 8 9 3 7 1 C y5 (edited).tif
0.35
2 0 1 2 _0 8 2 1 8 8 8 9 9 C y5 (edited).tif
2 0 1 2 _0 8 1 7 8 8 9 0 2 C y5 (edited).tif
2 0 1 2 _0 8 1 7 8 8 9 0 2 C y3 (edited).tif
Proteins (Loading Plot)
2 0 1 2 _0 8 1 7 8 8 9 0 0 C y3 (edited).tif
-0.8
2 0 1 2 _0 8 2 1 8 8 8 9 8 C y3 (edited).tif
-0.15
2 0 1 2 _0 8 2 1 8 8 8 9 4 C y3 (edited).tif
-0.6
2 0 1 2 _0 8 2 1 8 8 8 9 4 C y5 (edited).tif
-0.1
2 0 1 2 _0 8 2 1 8 8 8 9 7 C y5 (edited).tif
-0.4
2 0 1 2 _0 8 2 1 8 8 8 9 7 C y3 (edited).tif
-0.05
2 0 1 2 _0 9 1 9 8 9 3 6 6 C y3 (edited).tif
-0.2
2 0 1 2 _0 9 1 9 8 9 3 6 5 C y5 (edited).tif
0.05
2 0 1 2 _0 8 1 7 8 8 9 0 1 C y5 (edited).tif
0.2
2 0 1 2 _0 9 1 9 8 9 3 7 0 C y3 (edited).tif
0.1
2 0 1 2 _0 9 1 9 8 9 3 6 6 C y5 (edited).tif
0.4
2 0 1 2 _0 8 2 1 8 8 8 9 3 C y5 (edited).tif
0.6
2 0 1 2 _0 9 1 9 8 9 3 7 0 C y5 (edited).tif
0.8
2 0 1 2 _0 9 1 9 8 9 3 6 9 C y5 (edited).tif
1
2 0 1 2 _0 8 2 1 8 8 8 9 3 C y3 (edited).tif
Dehisced
Healed
2 0 1 2 _0 9 1 9 8 3 1 5 3 C y5 (edited).tif
PC2
0
2 0 1 2 _0 9 1 9 8 9 3 6 9 C y3 (edited).tif
Spot Maps (Score Plot)
2 0 1 2 _0 9 1 9 8 3 1 5 3 C y3 (edited).tif
2 0 1 2 _0 9 1 9 8 9 3 6 7 C y5 (edited).tif
2 0 1 2 _0 9 1 9 8 9 3 6 7 C y3 (edited).tif
2 0 1 2 _0 9 1 9 8 9 3 6 8 C y5 (edited).tif
1.2
2 0 1 2 _0 9 1 9 8 9 3 6 8 C y3 (edited).tif
PC2
2D Gel Analysis – UC Davis
Proteins (Loading Plot)
0.35
0.3
0.25
0.2
0.15
0
-0.2
-0.25
195
201
199
200
198
202
59
95
99
102
106
1 3 74
168
1 1 77
1 1 86
602
607
329
332
333
763
1381
548
336
787
1 4 87
800
869
976
872
876
1 3 92
330
341
343
337
344
345
347
375
389
1 0 80
582
436
978
1 7 96
1 7 97
980
981
814
794
Protein Biomarker Discovery
A Comparison
(left vs. right)
Healed
Dehisced
B
Spot No.
Differential
Proteins
Markers
selected
Accuracy (%)
52
9
83.83 ± 2.8
Healed vs. Dehisced Discriminate Markers
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
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
Wound Colonization
Achromobacter
sp.
Acinetobacter
Citrobacter
Freundii
E Coli
Enterococcus
faecium
Pseudomonas
Stutzeri
Am J Surg. 2010 Oct;200(4):489-95.
Wound Colonization and Inflammatory Response
Serum
*
*
*
Effluent
IL-6, IL10
* *
*
*
*
**
IL-8, IP-10, MIP-1a
**
MMP-3, -7, -13
<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-1b, IL-6, IL10
IL-8, MIP-1a
Surg Infect (Larchmt). 2011 Oct;12(5):351-7.
Response to Emerging Patterns: Predicting IFI
in Complex Dismounted Blast Injuries
Differe
Cytokin Mean
Mean SD
Cytokin Mean
SD IFI
nce in P Value
e
IFI
Control Control
e
IFI
Means
IL-1B 155.46 56.93 70.89 18.51 84.57 0.03
IL-7 104.02
EGF 60.85 19.45 117.37 43.75 -56.52 0.056 IL-15 95.81
TNF-a 128.43 48.27 60.64 31.89 67.79 0.058 MIP-1B 107.86
IL-17 98.46 3.08 66.29 30.23 32.17 0.079 MCP-1 123.08
G-CSF 100.47 14.67 75.97 18.8 24.5 0.086 IL-3
100
IL-RA 111.46 16.53 80.3 28.13 31.16 0.105 IL-12 103.59
IFN-y 136.86 30.32 106.89 9.08 29.97 0.107 IL-10 143.67
IL-4 97.05 15.46 77.25 18.4 19.81 0.15 MIG 118.59
IL-2 150.95 110.89 63.75 21.22 87.2 0.173 IL-13 153.72
MIP-1a 106.02 31.28 77.6 20.76 28.43 0.181 IL-1a 135.66
IFN-a 109.18 37.52 81.38 8.1 27.79 0.198 IL-5 92.22
GMIL-8 108.24 41.98 56.55 58.68 51.68 0.202
100
CSF
FGF108.2 43.77 69.42 35.46 38.78 0.218 HGF 111.15
Basic
RANTE
VEGF 108.04 40.75 73.22 34.69 34.82 0.241
83.1
S
IP-10 109.05 53.91 72.22 17.63 36.83 0.242 IL-2R 95.07
Eotaxin 121.79 38.29 93.82 21.75 27.97 0.251 IL-6 105.02
Differe
Mean SD
nce in P Value
Control Control
Means
37.55 76.36 22.44 27.65 0.253
39.36 63.91 32.09 31.89 0.256
49.73 74.88 28.31 32.99 0.293
46.08 79.94 59.1 43.14 0.293
0
99
2
1
0.356
16.29 93.25 12.88 10.34 0.358
114.41 92.02 9.09 51.65 0.403
49.53 92.23 31.28 26.35 0.403
158.59 95.9 19.23 57.82 0.496
138.44 89.43 12.71 46.23 0.531
9.03 85.66 17.64 6.56 0.532
SD IFI
0
91.72 29.13
8.28
0.59
65.52 91.89 54.39 19.26 0.667
10.29 76.31
38.2
6.79
0.743
59.98 90.38 52.62 4.7
23.93 103.59 171.89 1.43
0.91
0.987
Whole-genome approach allows for ID of viral sequence
Viral taxIDs with mapped sequence data (0.02% of all reads) for sample
KS702EBON
OIF/OEF Injuries and HO:
Risk Factors
• 63% of all combat-related amputations1
– Amputation in zone of injury
– Blast mechanism of injury
• 65% of all major extremity injuries2
1.
2.
Potter BK et al. J Bone Joint Surg Am. 2007;89:476-86.
Forsberg JA et al. J Bone Joint Surg Am 2009; 91: 1084-1091
Basic Science Meets Clinical Care
Heterotopic Ossification
•
More prevalent in OIF/OEF casualties than in similar
civilian trauma (60% vs. 20%)
•
An ongoing problem for rehabilitation/prosthetics
Laboratory
Biomarkers
Predictive of HO
in Casualties
6.3
Clinical Observation
Basic Research
Stem Cell
Differentiation
Blast Effects On
HO (Animal
Model)
6.1
Wound effluent promotes bone
growth in culture
Assessment of
Novel Treatments
to prevent HO
Small animal
model
6.2
Clinic
A basic/applied Research Program
Randomized trial underway to assess efficacy of COX-2 inhibitors and biomarkers
Inflammatory Biomarkers and HO
Tissue Biomarkers
*IP-10 predictive of not developing HO
Evans, Brown et al, J Orthop Trauma. 2012 May 14.
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
HO Polytrauma Model
• 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
•
•
•
•
Small animal mode
Blast tube (systemic)
Amputation or fracture (local)
Biobuden
Image Analysis of Tissue Integrity – Real Time
Feedback
Laboratory
Image
Enhancement and
Integration
6.2
Preclinical
assessment of
diagnostic
imaging of
wounds
6.3
S&T Gap/Warfighting Requirement:
Improved wound diagnostics
Current State-of-the-Art:
Visual inspection of wounds by surgeons
Spectroscopic
analysis of injury
6.1
Clinic
Anticipated Impact:
Save tissue that would have been surgically
otherwise removed
Decreased costs
Improved patient outcome
Improved function from preservation of
tissue
Direct regenerative medicine approaches
Product/Deliverable:
Enhanced diagnostics
Optic markers of tissue integrity
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
1
2
2
Examine multiple spots across the tissue.
40 accumulations, 5s spectrum
1800 1600 1400 1200 1000 800
Raman Shift (cm-1)
600
1004
1800
1600
1400
1200
940
1125
1040
1250
1320
1380
1555
1665
1445
Peakfitting for Spectral Deconvolution
1000
800
Raman Shift (cm-1)
Raman Shift (cm
860
920,940
1004
1040
1125
1250
1320
1445
1555
1665
-1
)
Vibrational Band Assignment
Component
n(C-C)
n(C-N), n(C-C)
n(C-C) ring
n(C-C) skeletal
n(C-C), n(C-N)
n (C-N) and d(N-H); Amide III
d(CH 2 ) twisting
nucleic acids
nucleic acids, keratin
phenylalanine
glycogen, keratin
nucleic acids, protein
protein
nucleic acids, protein
d(CH 3 ) and d(CH
protein
aromatic amino acids, heme
protein
2)
scissoring
n(C=O); Amide I
600
Factors indicate what is present, and score images indicate where
the factors are present and how much of the factors are present.
A Raman spectrum is collected at each yellow
cross, as illustrated on the image below.
Factors
PCA is
performed to
extract factors
and score
images.
Score Images
1
1
1
0.5
0.5
0.5
0
0.8
0.6
0.4
0.2
0
0
0
1
0.8
0.6
0.5
0.4
0.2
0
0
500 1000 1500 500 1000 1500
500 1000 1500
1
0.8
0.6
0.4
0.2
high
intensity
500 1000 1500
0.62 mm
1
Raman Shift
Last debridement
First debridement
0.9
(cm-1)
This process
was performed
to extract
tissue
“components”
for the first and
final
debridement of
each patient
included in the
Raman
mapping study.
low
intensity
144
1445
5
0.8
0.7
0.6
0.5
0.4
0.3
860
860
0.2
0.1
1310
1004
1310
1035,
100
1035,
1080
4
1080
1242
1242
1210
12
10
0
600
800
1000
1200
Raman Shift
1400
1665
16
65
1609
15716
0 1509
70
1600
(cm-1)
Intensity
1665/1448 cm-1
1668
1668
First Debridement
Curve-fitting of
the tissue
1800
“components”
enables band
area ratio
Areacalculations.
1665/1448 cm-1
0.5034
0.4492
0.4525
0.4413
1
144
4
0.8
1444
0.8
920
0.6
0.6
10041028 1304
1076
0.4
0.2
1665
160
9
0.4
0.2
860
1240
1032
100
1068
4
1665
1300
0
0
Difference between 1665/1448 band
area ratios: -1.8%; Transcript data
collaborates spectroscopy
Last Debridement
600
800
1000 1200 1400 1600 1800
Raman Shift (cm-1)
500
1000
1500
Raman Shift (cm-1)
Wound Repair Regen. 2010 Jun 8.
Early Mineralization/HO Detected by Raman
Mineral vibrational
bands (carbonated
apatite)
Normal muscle
Combat-injured
muscle
Muscle with pre-HO
(gritty soft-tissue;
no radiographic
evidence)
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)
Immunomodulation
Debridement Adequate - Raman
Bioburden – 16/18S
Timing - WounDX
Peri-op risk assessment
(VTE, VAP, sepsis)
Targeted therapy – PCR assay
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)
FTY720 - Novartis
In Advanced Development for treatment of shock in closed,
laparoscopically-induced, hemorrhage in nonhuman primates (6.4)
Lymphocyte Immunomodulation
Attenuates Innate And Cellular Response
Neutrophils
25.0
Hemorrhage
Reperfusion
(103/mm3)
20.0
15.0
10.0
p=0.04
5.0
p=0.1
0.0
Time (hours)
Control
PATG
FTY720
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
Develop
Models
Validate
Models
Treat
Iterate
Training the Next Generation
•
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
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
Critical Care:
Lessons from the battlefield translate to
civilian rehabilitation and back again
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
Concept
1. 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 biomarkers
3. Goal: maximize patient outcomes while
minimizing complications.
Research Transitions to Practice
Clinical Care/OM&N
Research/RDTE
Research Lab
Clinical Lab
Patient samples & Data
Change in clinical practice
Multiplex Assays &
Data Analysis
Clinician makes more
informed decisions using
personalized approach
Hospital
Models power
persistent, ubiquitous
CDS applications
Decreased Expression
Increased Expression
Decisions
Wound Outcome
Normal Healing 85%
Impaired Healing 14%
Serum IP-10
Debridement 3
Effluent IL-5
Serum MCP-1
Debridement 1
Serum MCP-1
Debridement 3
Serum IP-10
Effluent MCP-1
Debridement 3
Debridement 2
Effluent RANTES
Serum IL-6
Serum MCP-1
Effluent IL-5
Effluent RANTES
Clinical data-to registries
Samples go
to lab
Debridement 2
Debridement 1
Serum IP-10
Serum MCP-1
Debridement 1
Closure
Models are
created &
validated
1
2
Registry
Database /
Application
1
3
2
4
5
3
6
4
7
8...
5
6
Biomarker data-to registries
Lab
• Current status
•
• Population based studies
• Hypothesis driven
• Best judgment
• 85% solution
1
2
7
3
Registry
Database /
Application
4
8...
5
6
A.I. processes
information in real time
7
8...
Data
Future
• Decisions based on biology
• Personalized solutions
• Patient centered medicine
• 95 – 99% solution
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
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