Download
technological advancement in the surgical treatment of war wounds n.
Skip this Video
Loading SlideShow in 5 Seconds..
Technological Advancement in the Surgical Treatment of War Wounds PowerPoint Presentation
Download Presentation
Technological Advancement in the Surgical Treatment of War Wounds

Technological Advancement in the Surgical Treatment of War Wounds

190 Vues Download Presentation
Télécharger la présentation

Technological Advancement in the Surgical Treatment of War Wounds

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. 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

  2. Multiple Injuries in Combat Wounded

  3. “Following massive injury, physiological responses that were appropriate when applied locally become inappropriate and beyond regulation when systemically activated.”

  4. Immediate Response to injury Maximize return to duty Heterotopic ossification (HO) prophylaxis Regenerative Medicine Acute Resuscitation Assess tissue viability Injury Cycle: Target areas for personalized treatment and improved medical decision-making Debridement and Critical Care Timing of regenerative medicine Assess systemic response Personalized treatment of systemic response and bioburden Assess bioburden VTE prophylaxis and therapy

  5. Adaptive vs. Maladaptive Response to Injury Bilateral lower-extremity amputations Critically colonized LLE RLE LLE RLE Critically colonized and dehisced Critically colonized and formed HO LLE RLE LLE RLE Critically colonized, dehisced and formed HO LLE RLE

  6. Tools to Understand Inflammatory Response Real-time PCR Multiplex Protein Assay Raman Spectroscopy Visible Reflectance Imaging FTIR Imaging Bayesian Belief Network modeling Thermography

  7. 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

  8. 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)

  9. Timing of Wound Closure (WounDXTM) 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 Probabilistic (Bayesian) Model Prospective WounDX to start this fall in military and civilian sites

  10. Biomarker Assessment of Combat Wounded Plast Reconstr Surg. 2011 Jan;127 Suppl 1:21S-26S.

  11. Inflammatory Biomarkers in Combat Wound Healing Annals Surg. 2009 Apr;197(4):515-24

  12. WounDXTMProspective Biomarker Study • Internal AUCs • 0.82 ± 0.015 • Cross-Validated AUCs • 0.71 ± 0.04

  13. 2D Gel Analysis – UC Davis Discriminant Analysis (using 5 folds for cross validation)

  14. Protein Biomarker Discovery A B

  15. 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.

  16. Wound Colonization and Inflammatory Response Serum IL-6, IL10 IL-8, IP-10, MIP-1a MMP-3, -7, -13 <103CFU/g - Undetectable 103CFU/g - Colonized 104CFU/g - Critically Colonized >105CFU/g - Infection *p<0.05 compared to <103 CFU/g Effluent IL-1b, IL-6, IL10 IL-8, MIP-1a Surg Infect (Larchmt). 2011 Oct;12(5):351-7. Surg Infect (Larchmt). 2011 Oct;12(5):351-7. * * * * * * * * * * * * * * * * * * *

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

  18. Whole-genome approach allows for ID of viral sequence Viral taxIDs with mapped sequence data (0.02% of all reads) for sample KS702EBON

  19. 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 • 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

  20. 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 Clinical Observation Basic Research Clinic Stem Cell Differentiation Blast Effects On HO (Animal Model) 6.1 Assessment of Novel Treatments to prevent HO Small animal model 6.2 Biomarkers Predictive of HO in Casualties 6.3 Wound effluent promotes bone growth in culture A basic/applied Research Program Randomized trial underway to assess efficacy of COX-2 inhibitors and biomarkers

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

  22. 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.

  23. Bedside  Bench Celecoxib-HO Prophylaxis PRT HO Polytrauma Model Small animal mode Blast tube (systemic) Amputation or fracture (local) Biobuden • 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

  24. Image Analysis of Tissue Integrity – Real Time Feedback 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 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 Clinic Image Enhancement and Integration 6.2 Spectroscopic analysis of injury 6.1 Preclinical assessment of diagnostic imaging of wounds 6.3

  25. 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 Raman Shift (cm-1) 1800 1800 1600 1600 1400 1400 1200 1200 1000 1000 800 800 600 600

  26. Peakfitting for Spectral Deconvolution 1445 1665 1320 1555 1250 1004 1380 1040 1125 940 1800 1600 1400 1200 1000 800 600 -1 Vibrational Band Assignment Component Raman Shift (cm ) n 860 nucleic acids (C-C) n n 920,940 nucleic acids, keratin (C-N), (C-C) Raman Shift (cm-1) n 1004 phenylalanine (C-C) ring n 1040 glycogen, keratin (C-C) skeletal n 1125 nucleic acids, protein (C-C), n(C-N) 1250 n d( protein (C-N) and N-H); Amide III d (CH ) twisting 1320 nucleic acids, protein 2 d d (CH ) and (CH scissoring 1445 protein 3 2) 1555 aromatic amino acids, heme n( 1665 protein C=O); Amide I

  27. 1 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. 0.8 Factors Score Images 0.6 First Debridement Last Debridement PCA is performed to extract factors and score images. 0.4 1 1445 0.2 Last debridement This process was performed to extract tissue “components” for the first and final debridement of each patient included in the Raman mapping study. 0.9 First debridement 0 1 1 1 600 800 1000 1200 1400 1600 1800 0.8 0.5 0.5 0.5 Raman Shift (cm-1) 1445 0.7 0 0 0 1 0.6 0.8 0.8 0.6 0.6 1665 1668 0.5 0.5 0.4 0.4 1310 1004 0.2 0.2 0.8 1035, 1080 0 0.4 0 0 500 1000 1500 500 1000 1500 500 1000 1500 0.6 1668 1665 0.3 1444 1310 0.4 1004 1 1444 0.8 1242 860 0.2 1035, 1080 1609 Curve-fitting of the tissue “components” enables band area ratio calculations. 0.2 0.6 0.4 1240 0 1570 920 1210 1665 0.2 1304 1028 1004 1665 high intensity low intensity 1000 1500 500 0.1 1032 860 0.62 mm 500 1000 1500 1300 1004 1076 860 1068 1242 1609 1609 0 1570 1210 600 800 1000 1200 1400 1600 1800 Raman Shift (cm-1) Difference between 1665/1448 band area ratios: -1.8%; Transcript data collaborates spectroscopy Raman Shift (cm-1) Raman Shift (cm-1) Wound Repair Regen. 2010 Jun 8.

  28. 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.

  29. 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

  30. 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)

  31. Lymphocyte Immunomodulation Attenuates Innate And Cellular Response Hawksworth JS, Graybill JC , et al. PLoS ONE 7(4): e34224.

  32. 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.

  33. 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

  34. Training the Next Generation • 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 • 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

  35. 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

  36. 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

  37. Concept • Apply best of breed technologies in • biomarker analysis, • informatics • medical technology, • Clinical Decision Support tools can be developed that can optimize and personalize treatment using: • patient-specific clinical variables combined with local and systemic biomarkers • Goal: maximize patient outcomes while minimizing complications.

  38. 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

  39. Models are created & validated Clinician makes more informed decisions using personalized approach Models power persistent, ubiquitous CDS applications • Current status • Population based studies • Hypothesis driven • Best judgment • 85% solution Hospital Decisions Registry Database / Application Clinical data--to registries Samples go to lab Registry Database / Application Biomarker data--to registries A.I. processes information in real time • Future • Decisions based on biology • Personalized solutions • Patient centered medicine • 95 – 99% solution Lab Data

  40. 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

  41. 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