Development and validation of a pre-hospital "Red Flag" alert for activation of intra-hospital haemorrhage control response in blunt trauma

Sophie Rym Hamada, Anne Rosa, Tobias Gauss, Jean-Philippe Desclefs, Mathieu Raux, Anatole Harrois, Arnaud Follin, Fabrice Cook, Mathieu Boutonnet, Traumabase® Group, Arie Attias, Sylvain Ausset, Mathieu Boutonnet, Gilles Dhonneur, Jacques Duranteau, Olivier Langeron, Catherine Paugam-Burtz, Romain Pirracchio, Guillaume de St Maurice, Bernard Vigué, Alexandra Rouquette, Jacques Duranteau, Sophie Rym Hamada, Anne Rosa, Tobias Gauss, Jean-Philippe Desclefs, Mathieu Raux, Anatole Harrois, Arnaud Follin, Fabrice Cook, Mathieu Boutonnet, Traumabase® Group, Arie Attias, Sylvain Ausset, Mathieu Boutonnet, Gilles Dhonneur, Jacques Duranteau, Olivier Langeron, Catherine Paugam-Burtz, Romain Pirracchio, Guillaume de St Maurice, Bernard Vigué, Alexandra Rouquette, Jacques Duranteau

Abstract

Background: Haemorrhagic shock is the leading cause of early preventable death in severe trauma. Delayed treatment is a recognized prognostic factor that can be prevented by efficient organization of care. This study aimed to develop and validate Red Flag, a binary alert identifying blunt trauma patients with high risk of severe haemorrhage (SH), to be used by the pre-hospital trauma team in order to trigger an adequate intra-hospital standardized haemorrhage control response: massive transfusion protocol and/or immediate haemostatic procedures.

Methods: A multicentre retrospective study of prospectively collected data from a trauma registry (Traumabase®) was performed. SH was defined as: packed red blood cell (RBC) transfusion in the trauma room, or transfusion ≥ 4 RBC in the first 6 h, or lactate ≥ 5 mmol/L, or immediate haemostatic surgery, or interventional radiology and/or death of haemorrhagic shock. Pre-hospital characteristics were selected using a multiple logistic regression model in a derivation cohort to develop a Red Flag binary alert whose performances were confirmed in a validation cohort.

Results: Among the 3675 patients of the derivation cohort, 672 (18%) had SH. The final prediction model included five pre-hospital variables: Shock Index ≥ 1, mean arterial blood pressure ≤ 70 mmHg, point of care haemoglobin ≤ 13 g/dl, unstable pelvis and pre-hospital intubation. The Red Flag alert was triggered by the presence of any combination of at least two criteria. Its predictive performances were sensitivity 75% (72-79%), specificity 79% (77-80%) and area under the receiver operating characteristic curve 0.83 (0.81-0.84) in the derivation cohort, and were not significantly different in the independent validation cohort of 2999 patients.

Conclusion: The Red Flag alert developed and validated in this study has high performance to accurately predict or exclude SH.

Keywords: Anticipation; Organization; Protocol; Severe haemorrhage; Severe trauma.

Conflict of interest statement

Ethics approval and consent to participate

The data come from the Traumabase®. This registry obtained approval from the Advisory Committee for Information Processing in Health Research (CCTIRS, 11.305bis) and from the National Commission on Informatics and Liberties (CNIL, 911461), and the analysis meets the requirement of the local and national ethics committee (Comité de Protection des Personnes, Paris VI).

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Flowchart of the study. SH severe haemorrhage
Fig. 2
Fig. 2
Calibration plot of the model in the validation cohort: agreement between observed and predicted proportion of severe haemorrhage (SH) by the model
Fig. 3
Fig. 3
a Red Flag alert. b Contingency mosaic according to threshold of activation. FN false negative, FP false positive, Hb haemoglobin MBP mean arterial blood pressure, OTI Oro-tracheal intubation, SI Shock Index, TN true negative, TP true positive

References

    1. Kauvar DS, Wade CE. The epidemiology and modern management of traumatic hemorrhage: US and international perspectives. Crit Care. 2005;9(Suppl 5):S1–S9. doi: 10.1186/cc3779.
    1. Dutton RP, Stansbury LG, Leone S, Kramer E, Hess JR, Scalea TM. Trauma mortality in mature trauma systems: are we doing better? An analysis of trauma mortality patterns, 1997-2008. J Trauma. 2010;69:620–626. doi: 10.1097/TA.0b013e3181bbfe2a.
    1. Teixeira PGR, Inaba K, Hadjizacharia P, Brown C, Salim A, Rhee P, et al. Preventable or potentially preventable mortality at a mature trauma center. J Trauma. 2007;63:1338–46. doi: 10.1097/TA.0b013e31815078ae.
    1. Kauvar DS, Lefering R, Wade CE. Impact of hemorrhage on trauma outcome: an overview of epidemiology, clinical presentations, and therapeutic considerations. J Trauma. 2006;60:S3–11. doi: 10.1097/01.ta.0000199961.02677.19.
    1. Gerardo CJ, Glickman SW, Vaslef SN, Chandra A, Pietrobon R, Cairns CB. The rapid impact on mortality rates of a dedicated care team including trauma and emergency physicians at an academic medical center. J Emerg Med. 2011;40:586–591. doi: 10.1016/j.jemermed.2009.08.056.
    1. Cotton BA, Gunter OL, Isbell J, Au BK, Robertson AM, Morris JA, et al. Damage control hematology: the impact of a trauma exsanguination protocol on survival and blood product utilization. J Trauma. 2008;64:1177-1182-1183. doi: 10.1097/TA.0b013e31816c5c80.
    1. Glen J, Constanti M, Brohi K, Guideline Development Group Assessment and initial management of major trauma: summary of NICE guidance. BMJ. 2016;353:i3051. doi: 10.1136/bmj.i3051.
    1. van der Wulp I, van Baar ME, Schrijvers AJP. Reliability and validity of the Manchester Triage System in a general emergency department patient population in the Netherlands: results of a simulation study. Emerg Med J. 2008;25:431–434. doi: 10.1136/emj.2007.055228.
    1. Riou B. Le traumatisé grave. Actualités en réanimation préhospitalière. Journées scientifiques des SAMU de France. In: SFEM; 2002. pp. 115-8.
    1. Brown JB, Lerner EB, Sperry JL, Billiar TR, Peitzman AB, Guyette FX. Prehospital lactate improves accuracy of prehospital criteria for designating trauma activation level. J Trauma Acute Care Surg. 2016;81:445–452. doi: 10.1097/TA.0000000000001085.
    1. Sartorius D, Le Manach Y, David J-S, Rancurel E, Smail N, Thicoïpé M, et al. Mechanism, glasgow coma scale, age, and arterial pressure (MGAP): a new simple prehospital triage score to predict mortality in trauma patients. Crit Care Med. 2010;38:831–837. doi: 10.1097/CCM.0b013e3181cc4a67.
    1. Raux M, Sartorius D, Le Manach Y, David J-S, Riou B, Vivien B. What do prehospital trauma scores predict besides mortality? J Trauma Acute Care Surg. 2011;71:754–759. doi: 10.1097/TA.0b013e3181fd0dae.
    1. Yücel 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;60:1228–36. doi: 10.1097/.
    1. 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:S57–S63. doi: 10.1097/TA.0b013e318160a566.
    1. 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;66:346–352. doi: 10.1097/TA.0b013e3181961c35.
    1. Brockamp T, Nienaber U, Mutschler M, Wafaisade A, Peiniger S, Lefering R, et al. 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. doi: 10.1186/cc11432.
    1. Weaver AE, Hunter-Dunn C, Lyon RM, Lockey D, Krogh CL. The effectiveness of a “Code Red” transfusion request policy initiated by pre-hospital physicians. Injury. 2016;47:3–6. doi: 10.1016/j.injury.2015.06.023.
    1. Reed MJ, Glover A, Byrne L, Donald M, McMahon N, Hughes N, et al. Experience of implementing a national pre-hospital Code Red bleeding protocol in Scotland. Injury. 2017;48(1):41–6. doi: 10.1016/j.injury.2016.09.020.
    1. Khan S, Allard S, Weaver A, Barber C, Davenport R, Brohi K. A major haemorrhage protocol improves the delivery of blood component therapy and reduces waste in trauma massive transfusion. Injury. 2013;44:587–592. doi: 10.1016/j.injury.2012.09.029.
    1. Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162:W1. doi: 10.7326/M14-0698.
    1. Hamada SR, Gauss T, Duchateau F-X, Truchot J, Harrois A, Raux M, et al. Evaluation of the performance of French physician-staffed emergency medical service in the triage of major trauma patients. J Trauma Acute Care Surg. 2014;76:1476–1483. doi: 10.1097/TA.0000000000000239.
    1. Mitra B, Cameron PA, Gruen RL, Mori A, Fitzgerald M, Street A. The definition of massive transfusion in trauma: a critical variable in examining evidence for resuscitation. Eur J Emerg Med. 2011;18:137–142. doi: 10.1097/MEJ.0b013e328342310e.
    1. Savage SA, Zarzaur BL, Croce MA, Fabian TC. Redefining massive transfusion when every second counts. J Trauma Acute Care Surg. 2013;74:396-400-402. doi: 10.1097/TA.0b013e31827a3639.
    1. Callaway DW, Shapiro NI, Donnino MW, Baker C, Rosen CL. Serum lactate and base deficit as predictors of mortality in normotensive elderly blunt trauma patients. J Trauma. 2009;66:1040–1044. doi: 10.1097/TA.0b013e3181895e9e.
    1. Clarke JR, Trooskin SZ, Doshi PJ, Greenwald L, Mode CJ. Time to laparotomy for intra-abdominal bleeding from trauma does affect survival for delays up to 90 minutes. J Trauma. 2002;52:420–425.
    1. Como JJ, Dutton RP, Scalea TM, Edelman BB, Hess JR. Blood transfusion rates in the care of acute trauma. Transfusion. 2004;44:809–813. doi: 10.1111/j.1537-2995.2004.03409.x.
    1. Savage SA, Sumislawski JJ, Bell TM, Zarzaur BL. Utilizing group-based trajectory modeling to understand patterns of hemorrhage and resuscitation. Ann Surg. 2016;264:1135–1141. doi: 10.1097/SLA.0000000000001555.
    1. Montmany Vioque S, Navarro Soto S, Rebasa Cladera P, Luna Aufroy A, Gómez Díaz C, Llaquet BH. Measurement of lactic acid in multiple injury patients and its usefulness as a predictor of multiorgan failure and mortality. Cir Esp. 2012;90:107–113. doi: 10.1016/j.ciresp.2011.07.011.
    1. Régnier M-A, Raux M, Le Manach Y, Asencio Y, Gaillard J, Devilliers C, et al. Prognostic significance of blood lactate and lactate clearance in trauma patients. Anesthesiology. 2012;117:1276–1288. doi: 10.1097/ALN.0b013e318273349d.
    1. Raux M, Le Manach Y, Gauss T, Baumgarten R, Hamada S, Harrois A, et al. Comparison of the prognostic significance of initial blood lactate and base deficit in trauma patients. Anesthesiology. 2017;126:522–533. doi: 10.1097/ALN.0000000000001490.
    1. Sanchis-Gomar F, Cortell-Ballester J, Pareja-Galeano H, Banfi G, Lippi G. Hemoglobin point-of-care testing: the HemoCue system. J Lab Autom. 2013;18:198–205. doi: 10.1177/2211068212457560.
    1. Mutschler M, Nienaber U, Münzberg M, Wölfl C, Schoechl H, Paffrath T, et al. The Shock Index revisited—a fast guide to transfusion requirement? A retrospective analysis on 21,853 patients derived from the TraumaRegister DGU®. Crit Care. 2013;17:R172. doi: 10.1186/cc12851.
    1. Boyd CR, Tolson MA, Copes WS. Evaluating trauma care: the TRISS method. Trauma Score and the Injury Severity Score. J Trauma. 1987;27:370–378. doi: 10.1097/00005373-198704000-00005.
    1. Champion HR, Sacco WJ, Copes WS. Injury severity scoring again. J Trauma. 1995;38:94–95. doi: 10.1097/00005373-199501000-00024.
    1. Raux M, Thicoïpé M, Wiel E, Rancurel E, Savary D, David J-S, et al. Comparison of respiratory rate and peripheral oxygen saturation to assess severity in trauma patients. Intensive Care Med. 2006;32:405–412. doi: 10.1007/s00134-005-0063-8.
    1. Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32–35. doi: 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>;2-3.
    1. Buuren S, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in R. J Stat Softw. 2011 45. Available from: . [cited 2017 Jun 26].
    1. Hosmer Jr DW, Lemeshow S, Sturdivant RX. Applied logistic regression. Wiley; 2013. Available from: [cited 2017 Jun 26].
    1. Steyerberg EW, Harrell FE, Borsboom GJ, Eijkemans MJC, Vergouwe Y, Habbema JDF. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54:774–781. doi: 10.1016/S0895-4356(01)00341-9.
    1. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC bioinformatics. 2011;12:77. doi: 10.1186/1471-2105-12-77.
    1. Tonglet ML. Early prediction of ongoing hemorrhage in severe trauma: presentation of the existing scoring systems. Arch Trauma Res. 2016;5(4):e33377. doi: 10.5812/atr.33377.
    1. Moffat B, Vogt KN, Inaba K. The Shock Index: is it ready for primetime? Crit Care. 2013;17:196. doi: 10.1186/cc13040.
    1. Olaussen A, Blackburn T, Mitra B, Fitzgerald M. Review article: shock index for prediction of critical bleeding post-trauma: a systematic review. Emerg Med Australas. 2014;26:223–228. doi: 10.1111/1742-6723.12232.
    1. Meldrum SJ. The principles underlying Dinamap—a microprocessor based instrument for the automatic determination of mean arterial pressure. J Med Eng Technol. 1978;2:243–246. doi: 10.3109/03091907809161808.
    1. Yeguiayan J-M, Garrigue D, Binquet C, Jacquot C, Duranteau J, Martin C, et al. Medical pre-hospital management reduces mortality in severe blunt trauma: a prospective epidemiological study. Crit Care. 2011;15:R34. doi: 10.1186/cc9982.
    1. Champion HR, Sacco WJ, Copes WS, Gann DS, Gennarelli TA, Flanagan ME. A revision of the Trauma Score. J Trauma. 1989;29:623–629. doi: 10.1097/00005373-198905000-00017.
    1. Jansen TC, van Bommel J, Mulder PG, Rommes JH, Schieveld SJM, Bakker J. The prognostic value of blood lactate levels relative to that of vital signs in the pre-hospital setting: a pilot study. Crit Care. 2008;12:R160. doi: 10.1186/cc7159.
    1. Euser AM, Zoccali C, Jager KJ, Dekker FW. Cohort studies: prospective versus retrospective. Nephron Clin Pract. 2009;113:c214–c217. doi: 10.1159/000235241.
    1. MacKenzie EJ, Rivara FP, Jurkovich GJ, Nathens AB, Frey KP, Egleston BL, et al. A national evaluation of the effect of trauma-center care on mortality. N Engl J Med. 2006;354:366–378. doi: 10.1056/NEJMsa052049.

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren