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Prediction of Life-Threatening Hemorrhage

  • Dominick A. Vitale
  • Marc Maegele
  • Matthew A. Borgman
Chapter

Abstract

Tools to identify life-threatening hemorrhage in traumatic injury can be valuable in activating a massive transfusion protocol, alerting the blood bank, and accelerating the delivery of critical blood products to the patient. Recent literature has identified that any delay in blood product delivery to the hemorrhaging patient is associated with potentially preventable death. In the past decade based on recent military experiences, the transition to the use of more plasma (which may require thawing) and platelets highlight the need for early recognition of life-threatening hemorrhage. While the process of initiating a massive transfusion protocol is often based upon clinical judgment, many clinical tools have been developed to assist in identifying patients who are at risk of exsanguination. Most of these tools are derived from retrospective studies, based on different definitions of a massive transfusion, and utilize a range of variables to calculate a predictive “score.” Other scoring systems have been developed to meet the needs of a particular trauma center or system, as has been done in the military. The following is a review of these scores and a description of the potential utility for predicting life-threatening hemorrhage.

Keywords

Trauma Transfusion Resuscitation Damage control Protocol Hemorrhage Whole blood Plasma RBCs Platelets Scoring Prediction 

References

  1. 1.
    Haagsma JA, Graetz N, Bollinger I, Naghavi M, et al. The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013. Inj Prev. 2016;22:3–18.CrossRefGoogle Scholar
  2. 2.
    Rossaint R, et al. The European guideline on management of major bleeding and coagulopathy following trauma: fourth edition. Crit Care. 2016;20(100):1–55.Google Scholar
  3. 3.
    Brockamp T, Nienaber U, Mutschler M, Wafaisade A, Peiniger S, Lefering R, Bouillon B, Maegele M. Predicting on-going hemorrhage and transfusion requirement after severe trauma: a validation of six scoring systems and algorithms on the TraumaRegister DGU. Crit Care. 2012;16:R129.CrossRefGoogle Scholar
  4. 4.
    Maegele M, Brockamp T, Nienaber U, Probst C, Schoechl H, Goerlinger K, Spinella P. Predictive models and algorithms for the need of transfusion including massive transfusion in severely injured patients. Transfus Med. 2012;39:85–97.Google Scholar
  5. 5.
    Patregnani JT, Borgman MA, Maegele M, Wade CE, Blackbourne LH, Spinella PC. Coagulopathy and shock on admission is associated with mortality for children with traumatic injuries at combat support hospitals. Pediatr Crit Care Med. 2012;13(3):273–7.CrossRefGoogle Scholar
  6. 6.
    Mitra B, Rainer TH, Cameron PA. Predicting massive blood transfusion using clinical scores post-trauma. Vox Sang. 2012;102(4):324–30.CrossRefGoogle Scholar
  7. 7.
    Simmons JW, Powell MF. Acute traumatic coagulopathy: pathophysiology and resuscitation. Br J Anaesth. 2016;117(S3):iii31–43.CrossRefGoogle Scholar
  8. 8.
    Yucel N, Lefering R, Maegele M, Vorweg M, Tjardes T, Ruchholtz S, et al. Trauma Associated Severe Hemorrhage (TASH)-Score: probability of mass transfusion as surrogate for life threatening hemorrhage after multiple trauma. J Trauma. 2006;60(6):1228–36; discussion 36–7.CrossRefGoogle Scholar
  9. 9.
    Holcomb JB, del Junco DJ, Fox EE, Wade CE, Cohen MJ, Schreiber MA, et al. The prospective, observational, multicenter, major trauma transfusion (PROMMTT) study: comparative effectiveness of a time-varying treatment with competing risks. JAMA Surg. 2013;148(2):127–36.CrossRefGoogle Scholar
  10. 10.
    Gunter OL Jr, Au BK, Isbell JM, et al. Optimizing outcomes in damage control resuscitatio: identifying blood product ratios associated with improved survival. J Trauma. 2008;65(3):527–34.CrossRefGoogle Scholar
  11. 11.
    Nessen SC, Eastridge BJ, Cronk D, et al. Fresh whole blood use by forward surgical teams in Afghanistan is associated with improved survival compared to component therapy without platelets. Transfusion. 2013;53(Supp1):107S–13S.CrossRefGoogle Scholar
  12. 12.
    Pidcoke HF, Aden JK, Mora AG, et al. Ten-year analysis of transfusion in Operation Iraqi Freedom and Operation Enduring Freedom: increased plasma and platelet use correlates with improved survival. J Trauma Acute Care Surg. 2012;73(6 Suppl 5):S445–52.CrossRefGoogle Scholar
  13. 13.
    Holcomb JB, Tilley BC, Baraniuk S, Fox EE, et al. Transfusion of plasma, platelets, and red blood cells in a 1:1:1 vs a 1:1:2 ratio and mortality in patients with severe trauma: the PROPPR randomized clinical trial. JAMA. 2015;313(5):471–82.CrossRefGoogle Scholar
  14. 14.
    Kutcher ME, Kornblith LZ, Narayan R, Curd V, Daley AT, Redick BJ, Nelson MF, Fiebig EW, Cohen MJ. A paradigm shift in trauma resuscitation: evaluation of evolving massive transfusion practices. JAMA Surg. 2013;148(9):834–40.CrossRefGoogle Scholar
  15. 15.
    Nunez TC, Voskresensky IV, Dossett LA, Shinall R, Dutton WD, Cotton BA. Early prediction of massive transfusion in trauma: simple as ABC (assessment of blood consumption)? J Trauma. 2009;66(2):346–52.CrossRefGoogle Scholar
  16. 16.
    Meyer DE, Vincent LE, Fox EE, O’Keeffe T, Inaba K, Bulger E, et al. Every minute counts: time to delivery of initial massive transfusion cooler and its impact on mortality. J Trauma Acute Care Surg. 2017;83(1):19–24.CrossRefGoogle Scholar
  17. 17.
    Ogura T, Nakamura Y, Nakano M, et al. Predicting the need for massive transfusion in trauma patients: the Traumatic Bleeding Severity Score. J Trauma Acute Care Surg. 2014;76(5):1243–50.CrossRefGoogle Scholar
  18. 18.
    Cantle PM, Cotton BA. Prediction of massive transfusion in trauma. Crit Care Clin. 2017;33(1):71–84.CrossRefGoogle Scholar
  19. 19.
    Shackelford S, Yang S, Hu P, Miller C, Anazodo A, Glavagno S, Wang Y, et al. Predicting blood transfusion using automated analysis of pulse oximetry signals and labarotory values. J Trauma Acute Care Surg. 2015;79(4 Supp 1):S175–80.CrossRefGoogle Scholar
  20. 20.
    Callcut RA, Cripps MW, Nelson MF, Conroy AS, Robinson BRR, Cohen MJ. The massive transfusion score as a decision aid for resuscitation: learning when to turn the massive transfusion protocol on and off. J Trauma Acute Care Surg. 2016;80(3):450–6.CrossRefGoogle Scholar
  21. 21.
    Neff LP, Cannon JW, Morrison JJ, Edwards MJ, Spinella PC, Borgman MA. Clearly defining pediatric massive transfusion: cutting through the fog and friction with combat data. J Trauma Acute Care Surg. 2015;78(1):22–8.CrossRefGoogle Scholar
  22. 22.
    Rahbar E, Fox EE, Del Junco DJ, et al. Early resuscitation intensity as a surrogate for bleeding severity and early mortality in the PROMMTT study. J Trauma Acute Care Surg. 2013;75(1):S16–23.CrossRefGoogle Scholar
  23. 23.
    Savage SA, Sumislawski JJ, Zarzaur BL, Dutton WP, Croce MA, Fabian TC. The new metric to define large-volume hemorrhage: results of a prospective study of the critical administration threshold. J Trauma Acute Care Surg. 2014;78(2):224–30.CrossRefGoogle Scholar
  24. 24.
    Riskin DJ, Tsai TC, Riskin L, et al. Massive transfusion protocols: the role of aggressive resuscitation versus product ratio in mortality reduction. J Am Coll Surg. 2009;209:198–205.CrossRefGoogle Scholar
  25. 25.
    Dente CJ, Shaz BH, Nicholas JM, Harris RS, et al. Improvements in early mortality and coagulopathy are sustained better in patients with blunt trauma after institution of a massive transfusion protocol in a civilian level I trauma center. J Trauma. 2009;66(6):1616–24.CrossRefGoogle Scholar
  26. 26.
    Schafer N, Driessen A, Frohlich M, Sturmer EK, Maegele M, et al. Diversity in clinical management and protocols for the treatment of major bleeding trauma patients across European level I Trauma Centres. Scand J Trauma Resusc Emerg Med. 2015;23(1):74.CrossRefGoogle Scholar
  27. 27.
    Holcomb JB, Fox EE, Wade CE, Group PS. The PRospective Observational Multicenter Major Trauma Transfusion (PROMMTT) study. J Trauma Acute Care Surg. 2013;75(1 Suppl 1):S1–2.CrossRefGoogle Scholar
  28. 28.
    Holcomb JB, Tilley BC, Baraniuk S, Fox EF, Wade CE, et al. Transfusion of plasma, platelets, and red blood cells in a 1:1:1 vs a 1:1:2 ratio and mortality in patients with severe trauma: the PROPPR randomized clinical trial. JAMA. 2015;313(5):471–82.CrossRefGoogle Scholar
  29. 29.
    Borgman MA, Spinella PC, Holcomb JB, Blackbourne LH, Wade CE, Lefering R, et al. The effect of FFP:RBC ratio on morbidity and mortality in trauma patients based on transfusion prediction score. Vox Sang. 2011;101(1):44–54.CrossRefGoogle Scholar
  30. 30.
    Tapia N, Chang A, Norman M, Welsh F, Scott B, Wall MJ Jr, Mattox KL, Suliburk J. TEG-guided resuscitation is superior to standardized MTP resuscitation in massively transfused penetrating trauma patients. J Trauma. 2013;74:378–86.CrossRefGoogle Scholar
  31. 31.
    McLaughlin DF, Niles SE, Salinas J, Perkins JG, Cox ED, Wade CE, et al. A predictive model for massive transfusion in combat casualty patients. J Trauma. 2008;64(2 Suppl):S57–63; discussion S.CrossRefGoogle Scholar
  32. 32.
    Tonglet ML. Early prediction of ongoing hemorrhage in severe trauma: presentation of the existing scoring systems. Arch Trauma Res. 2016;5(4):e33377.CrossRefGoogle Scholar
  33. 33.
    Cancio LC, Wade CE, West SA, Holcomb JB. Prediction of mortality and of the need for massive transfusion in casualties arriving at combat support hospitals in Iraq. J Trauma. 2008;64(2 Suppl):S51–5; discussion S5–6.CrossRefGoogle Scholar
  34. 34.
    Olaussen A, Peterson EL, Mitra B, O’Reilly G, Jennings PA, Fitzgerald M. Massive transfusion prediction with inclusion of the pre-hospital Shock Index. Injury. 2015;46(5):822–6.CrossRefGoogle Scholar
  35. 35.
    McLennan JV, Mackway-Jones KC, Smith JE. Prediction of massive blood transfusion in battlefield trauma: development and validation of the Military Acute Severe Haemorrhage (MASH) score. Injury. 2018;49:184–90.CrossRefGoogle Scholar
  36. 36.
    Baker JB, Korn CS, Robinson K, Chan L, Henderson SO. Type and crossmatch of the trauma patient. J Trauma. 2001;50:878–81.CrossRefGoogle Scholar
  37. 37.
    Trickey AW, Fox EE, del Junco DJ, Ning J, Holcomb JB, Brasel KJ, et al. The impact of missing trauma data on predicting massive transfusion. J Trauma Acute Care Surg. 2013;75(1 Suppl 1):S68–74.CrossRefGoogle Scholar
  38. 38.
    Pidcoke HF, Aden JK, Mora AG, Borgman MA, et al. Ten-year analysis of transfusion in Operation Iraqi Freedom and Operation Enduring Freedom: increased plasma and platelt use correlates with improved survival. J Trauma Acute Care Surg. 2012;73(6 supp 5):S445–52.CrossRefGoogle Scholar
  39. 39.
    Spinella PC, Cap AP. Whole blood: back to the future. Curr Opin Hematol. 2016;23(6):536–42.CrossRefGoogle Scholar
  40. 40.
    Pommerening MJ, Goodman MD, Holcomb JB, Wade CE, Fox EE, Del Junco DJ, et al. Clinical gestalt and the prediction of massive transfusion after trauma. Injury. 2015;46(5):807–13.CrossRefGoogle Scholar
  41. 41.
    Cotton BA, Dossett LA, Haut ER, et al. Multicenter validation of a simplified score to predict massive transfusion in trauma. J Trauma Crit Care. 2010;69(1):S33–9.CrossRefGoogle Scholar
  42. 42.
    Rainer TH, Ho AM, Yeung JH, Cheung NK, Wong RS, Tang N, et al. Early risk stratification of patients with major trauma requiring massive blood transfusion. Resuscitation. 2011;82(6):724–9.CrossRefGoogle Scholar
  43. 43.
    Maegele M, Lefering R, Wafaisade A, Theodorou P, et al. Revalidation and update of the TASH-Score: a scoring system to predict the probability for massive transfusion as a surrogate for life-threatening haemorrhage after severe injury. Vox Sang. 2010;100:231–8.CrossRefGoogle Scholar
  44. 44.
    Vandromme MJ, Griffin RL, McGwin G, Weinberg J, Rue LW, Kerby JD. Prospective identification of patients at risk for massive transfusion: an imprecise endeavor. Am Surg. 2011;77:155–61.PubMedGoogle Scholar
  45. 45.
    Mutschler M, Brockamp T, Wafaisade A, Lipensky A, Probst C, Bouillon B, Maegele M. ‘Time to TASH’: how long does complete score calculation take to assess major trauma hemorrhage? Transfus Med. 2014;24:58–9.CrossRefGoogle Scholar
  46. 46.
    Maegele M, Brockamp T, Nienaber U, Probst C, Schoechl H, Gorlinger K, Spinella P. Predictive models and algorithms for the need of transfusion including massive transfusion in severely injured patients. Transfus Med Hemother. 2012;39:85–97.CrossRefGoogle Scholar
  47. 47.
    Larson CR, White CE, Spinella PC, Jones JA, Holcomb JB, Blackbourne LH, et al. Association of shock, coagulopathy, and initial vital signs with massive transfusion in combat casualties. J Trauma. 2010;69(Suppl 1):S26–32.CrossRefGoogle Scholar
  48. 48.
    Schreiber MA, Perkins J, Kiraly L, Underwood S, Wade C, Holcomb JB. Early predictors of massive transfusion in combat casualties. J Am Coll Surg. 2007;205(4):541–5.CrossRefGoogle Scholar
  49. 49.
    Schroll R. et al. Accuracy of shock index versus ABC score to predict need for massive transfusion in trauma patients. Injury. 2017.  https://doi.org/10.1016/j.injury.2017.09.015.CrossRefGoogle Scholar
  50. 50.
    Mina MJ, Winkler AM, Dente CJ. Let technology do the work: improving predication of massive transfusion with the aid of a smartphone application. J Trauma Acute Care Surg. 2013;75(4):669–75.CrossRefGoogle Scholar
  51. 51.
    Callcut RA, Johannigman JA, Kadon KS, Hanseman DJ, Robinson BRH. All massive transfusion criteria are not created equal: defining the predictive value of individual transfusion triggers to better determine who benefits from blood. J Trauma. 2011;70(4):794–801.CrossRefGoogle Scholar
  52. 52.
    Callcut RA, Cotton BA, Muskat P, Fox EE, Wade CE, et al. Defining when to initiate massive transfusion: a validation study of individual massive transfusion triggers in PROMMTT patients. J Trauma Acute Care Surg. 2012;74(1):59–68.CrossRefGoogle Scholar
  53. 53.
    Hampton DA, Lee TH, Diggs BS, McCully SP, Schreiber MA. A predictive model of early mortality in trauma patients. Am J Surg. 2014;207(5):642–7.CrossRefGoogle Scholar
  54. 54.
    Schochl H, Cotton B, Inaba K, Nienaber U, Fischer H, Voelckel W, et al. FIBTEM provides early prediction of massive transfusion in trauma. Crit Care. 2011;15(6):R265.CrossRefGoogle Scholar
  55. 55.
    Mackenzie CF, Wang Y, Hu PF, Chen S, Chen HH, Hagegeorge G, Stansbury LG, Shackelford S. Automated prediction of early blood transfusion and mortality in trauma patients. J Trauma Acute Care Surg. 2014;76(6):1379–85.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Dominick A. Vitale
    • 1
  • Marc Maegele
    • 2
  • Matthew A. Borgman
    • 3
  1. 1.Trauma/Critical Care, Brooke Army Medical CenterFort Sam HoustonUSA
  2. 2.Department of Traumatology and Orthopedic SurgeryInstitute for Research in Operative Medicine (IFOM), Cologne-Merheim Medical Center (CMMC), University Witten-Herdecke (UW/H)CologneGermany
  3. 3.Department of PediatricsBrooke Army Medical CenterFort Sam HoustonUSA

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