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Original research
Impact of trauma centre capacity and volume on the mortality risk of incoming new admissions
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  1. William C Chiu1,
  2. D B Powers2,
  3. J M Hirshon3,
  4. S A Shackelford4,
  5. P F Hu5,
  6. S Y Chen6,
  7. H H Chen3,
  8. C F Mackenzie7,
  9. C H Miller8,
  10. J J DuBose1,9,
  11. C Carroll10,
  12. R Fang11 and
  13. T M Scalea1
  1. 1 R Adams Cowley Shock Trauma Center, Baltimore, Maryland, USA
  2. 2 Director, Craniomaxillofacial Trauma Program, Duke University Hospital, Durham, North Carolina, USA
  3. 3 Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA
  4. 4 Joint Trauma System, JBSA Ft. Sam Houston, Texas, USA
  5. 5 University of Maryland Medical Center, Baltimore, Maryland, USA
  6. 6 National Yunlin University of Science and Technology, Douliou, Taiwan
  7. 7 Shock Trauma and Anesthesiology Research - Organized Research Center (STAR-ORC), University of Maryland School of Medicine, Baltimore, Maryland, USA
  8. 8 US Air Force Materiel Command, Wright-Patterson AFB, Ohio, USA
  9. 9 Center for Sustainment of Trauma and Readiness Skills - Baltimore, US Air Force Medical Service, Baltimore, Maryland, USA
  10. 10 Baltimore, Maryland, USA
  11. 11 Surgery, Johns Hopkins Bayview Medical Center, Baltimore, Maryland, USA
  1. Correspondence to Dr D B Powers, Director, Craniomaxillofacial Trauma Program, Duke University Hospital, Durham, NC 27710, USA; David.Powers{at}duke.edu

Abstract

Introduction Trauma centre capacity and surge volume may affect decisions on where to transport a critically injured patient and whether to bypass the closest facility. Our hypothesis was that overcrowding and high patient acuity would contribute to increase the mortality risk for incoming admissions.

Methods For a 6-year period, we merged and cross-correlated our institutional trauma registry with a database on Trauma Resuscitation Unit (TRU) patient admissions, movement and discharges, with average capacity of 12 trauma bays. The outcomes of overall hospital and 24 hours mortality for new trauma admissions (NEW) were assessed by multivariate logistic regression.

Results There were 42 003 (mean=7000/year) admissions having complete data sets, with 36 354 (87%) patients who were primary trauma admissions, age ≥18 and survival ≥15 min. In the logistic regression model for the entire cohort, NEW admission hospital mortality was only associated with NEW admission age and prehospital Glasgow Coma Scale (GCS) and Shock Index (SI) (all p<0.05). When TRU occupancy reached ≥16 patients, the factors associated with increased NEW admission hospital mortality were existing patients (TRU >1 hour) with SI ≥0.9, recent admissions (TRU ≤1 hour) with age ≥65, NEW admission age and prehospital GCS and SI (all p<0.05).

Conclusion The mortality of incoming patients is not impacted by routine trauma centre overcapacity. In conditions of severe overcrowding, the number of admitted patients with shock physiology and a recent surge of elderly/debilitated patients may influence the mortality risk of a new trauma admission.

  • trauma management
  • surgery
  • epidemiology

Data availability statement

All data relevant to the study are included in the article or uploaded as online supplementary information. All data from randomised/anonymous patient identification numbers.

https://doi.org/10.1136/bmjmilitary-2020-001483

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    Key messages

    • Trauma care involves time-critical assessments and therapeutic actions.

    • Trauma systems strive to deliver the critically injured patient with minimal delay.

    • Trauma centre capacity and surge volume may affect decisions on where to transport a critically injured patient, and whether to bypass the closest facility.

    • New admission hospital and 24 hours mortality were only associated with new admission age, and prehospital Glasgow Coma Scale and Shock Index.

    • The mortality of incoming patients is not impacted by routine or daily trauma centre overcapacity and surge volume.

    • In severe overcrowding, the number of patients in shock and a recent surge of elderly/debilitated patients may affect the mortality risk of new admissions.

    Introduction

    Trauma care involves time-critical assessments and therapeutic actions. However, in combat or civilian mass casualty situations, resources may be limited and casualties may be transported with vehicles of opportunity such as passenger cars, trucks or military transport vehicles, rather than with emergency ambulances or helicopters from the incident scene to the treatment area. Medical manpower availability at the initial treatment area, which can be limited due to social unrest or battlefield situations, influence the treatment of injuries. At times, primary assessment and initial treatment may be delayed until the casualty and medic are in the transport vehicle.

    Ideally, casualties would have the best possible prediction of their needs based on initial injuries and initially obtainable physiologic information, to minimise undertriage and to deliver casualties expediently to the most appropriate echelon of care. Originally conceptualised based on a military model, this study was undertaken to see if definitive treatment be delayed 10, 20, 30 min or more, to reach a facility with greater resources and manpower? Which treatment facility is best prepared to care for a specific casualty at a given time? These patient-acuity factors, combined with information about facility-resource factors, would allow an aeromedical evacuation team to best decide ‘Should I Land Now?’ at the closest hospital or proceed to the nearest functional trauma centre.

    From a practical perspective, should aeromedical transport flyover the closest trauma centre to another facility based on the volume/acuity of patient care ongoing? In a mature trauma system, trauma centre capacity and surge volume may affect decisions on where to transport a critically injured patient, and whether to bypass the closest facility. Interaction among the paradigms of trauma triage, access to emergency care and crowding, resource availability and utilisation, and ambulance diversion must all be dynamically balanced for optimal patient outcomes.

    The objective of this study was to evaluate the impact of admitting area occupancy rate, recent admissions, patient demographics and patient acuity on the mortality of incoming patients in an academic state referral centre for trauma. Our hypothesis was that overcrowding, a surge of admissions and high patient acuity would contribute to increase the mortality risk for incoming new admissions. The ultimate goal was to identify recent, existing and incoming patient and facility factors that would contribute to a predictive model facilitating optimum trauma triage by prehospital and emergency medical evacuation providers, and also to better inform trauma centre bypass decisions.

    Methods

    The chosen location for this study was the R Adams Cowley Shock Trauma Center (STC) in Baltimore, Maryland USA. The STC is the primary adult resuscitation centre for the Maryland Institute for emergency medical services (EMS) Systems.1 The geography and topography of the state of Maryland is unique in that from essentially any location in the state a patient could be transported to the STC within a 60 min helicopter transport, the parameter used by essentially all military and civilian trauma centres to initiate primary trauma surgical care.1 2 The Maryland Medical Protocols for EMS Providers are approved by the Governor-appointed state EMS Board, and the Trauma Decision Tree has been adapted from national guidelines for on-scene triage (figure 1).2 For the 6-year period from 2006 to 2011, we merged and cross-correlated our institutional trauma registry with a database on the STC Trauma Resuscitation Unit (TRU) patient admissions, movement and discharges. The TRU serves as the admitting area for all trauma patients arriving from the incident scene and from interhospital transfers. During the study period, the TRU underwent unit expansion from 10 to 13 trauma resuscitation bays, with an average capacity of 12 bays during the study period. The theoretical surge capacity was estimated at 24 total patients (when bed burden is two patients per trauma bay). The medical personnel to patient ratio was highly variable during the study period because of the unpredictable timing and pattern of trauma admissions. All data used in the development of this project were from randomised computer identification protocols which compiled patient data.

    Figure 1
    Figure 1

    Trauma Decision Tree. BP, blood pressure; EMS, emergency medical service; GCS, Glasgow Coma Scale.

    Critical Index viewer

    A computerised programme (Facility/Patient Critical Index Viewer) was created to allow review and analysis of TRU status at any point in time throughout the 6 year study period, using the following three facility factors:

    1. Number of patients present in the TRU (EXIST).

    2. Number of new patients who have arrived in the previous 1 hour (RECENT).

    3. Individual, median and mean admission vital signs of all EXIST patients.

    For any given point in time, and all points in time, the programme calculates the total number of patients in the TRU, the number of new admissions in the past hour, the mean admission vital signs and Shock Index (SI=heart rate/systolic blood pressure) and TRU length-of-stay of all patients.

    Predictive model

    The outcomes of overall in-hospital mortality (death from any cause during inpatient admission) and mortality within 24 hours for new trauma admissions (NEW) were assessed by multivariate logistic regression, statistical modelling, and receiver operating characteristic (ROC) analysis using four groups of variables: NEW admissions, recent admissions (TRU <1 hour, RECENT), existing patients (TRU >1 hour, EXIST) and facility factors (table 1). We also examined hospital mortality and 24 hours mortality for TRU occupancy level intervals from 6 to 18 patients.

    View this table:
    Table 1

    Multivariate logistic regression statistical modelling variables

    Results

    Entire 6-year cohort

    There were 42 003 (mean=7000/y) admissions having complete data sets, with 36 354 (87%) patients who were primary trauma admissions, age ≥18 and survival ≥15 min (figure 2). The overall demographic analysis included: mean age was 43 years, 70% male, 26% air transport, hospital mortality was 1485 (4.08%), 24 hours mortality was 808 (2.22%) (table 2). In patients with both age ≥65 and SI ≥0.9, hospital mortality was 23.8% and 24 hours mortality was 11.4% (table 3).

    View this table:
    Table 2

    Demographics of primary trauma admissions, 2006–2011 (n=36 354)

    View this table:
    Table 3

    Mortality rates for age and SI categories (n=29 077)

    Figure 2
    Figure 2

    Patient enrolment and analysis flow diagram. ADT, Admissions-Discharges-Transfers; STC, Shock Trauma Center; TRU, Trauma Resuscitation Unit.

    In the logistic regression model for the entire cohort, NEW admission hospital mortality and 24 hours mortality were only associated with NEW admission age, prehospital Glasgow Coma Scale (GCS), and prehospital SI (all p<0.05) (table 4). NEW admission mortality was not influenced by the number, status, or injury severity of EXIST patients, RECENT admissions or any of the facility factors.

    View this table:
    Table 4

    Independent predictors of mortality for new trauma admissions*

    TRU overcapacity

    The estimated average capacity of the TRU is 12 patients. There was no significant effect on NEW admission hospital mortality or 24 hours mortality for any TRU occupancy level from 6 to 14 patients. However, when TRU occupancy reached ≥16 patients (capacity ≈ 133%, n=1905, 5.24% admissions), the factors independently associated with increased NEW admission hospital mortality were EXIST patients with SI ≥0.9, RECENT admissions with age ≥65, NEW admission age, prehospital GCS and prehospital SI (all p<0.05).

    Increased NEW admission 24 hours mortality was associated with NEW admission prehospital GCS and RECENT admissions with age ≥65 (both p<0.05). For the condition when TRU occupancy reached ≥16 patients, our statistical model of 18 demographic, physiologic, occupancy and surge variables resulted in a prediction power for NEW patient mortality of area under the ROC curve=0.92 (p<0.0001).

    Discussion

    This study explores the complex relationship and interaction between initial emergency triage protocols and trauma centre capacity on the dynamics of best destination decision making and impact on patient outcome, highlighting the importance of time-critical assessments and therapeutic actions. The results suggest that in a mature level I trauma centre, routine or daily overcapacity does not impact trauma admission survival, but there is a capacity threshold in which patient characteristics and facility factors become important considerations that may affect new admission outcome. The admitting area existing patient factors that proved to be most relevant are patient age and shock physiology.

    Brown et al conducted a National Trauma Data Bank (NTDB) analysis on whether the triage guidelines could predict which patients would benefit from helicopter transport, as trauma centres strive to deliver the critically injured patient to definitive care with minimal delay.3 Out of 258 387 patients, logistic regression identified helicopter transport as an independent predictor of survival in patients with penetrating injury, GCS score <14, abnormal respiratory rate and age >55 years. They recommended considering these specific criteria when evaluating the decision scheme for helicopter transport.

    The utility of helicopter EMS, compared with ground EMS, has been the subject of great debate.4 The reduction in transit time must be balanced with the expense of the resource. Galvagno et al conducted a large cohort study of 223 475 NTDB patients transported to a trauma centre.4 They found that patients transported by helicopter had higher Injury Severity Scores (ISS), but also improved odds of survival compared with those transported by ground. Because of the retrospective design and multiple covariates, it could not be speculated which aspect of helicopter transport is responsible for the mortality benefit.

    When Mackenzie et al analysed mortality data, they found that both in-hospital and 1-year mortality rates were significantly lower at trauma centres than at non-trauma centres (7.6% vs 9.5% and 10.4% vs 13.8%, respectively), highlighting trauma centre capability and capacity may affect decisions on where to transport a critically injured patient and whether to bypass a closer medical care facility.5 They also found that the incremental cost for treatment at a trauma centre, compared with a non-trauma centre was US$36 319 per life-year gained and US$36 961 per quality-adjusted life years gained. While the value of a year of life is not easily quantified, commonly used benchmarks range from US$50 000 to US$200 000.6

    A competing predicament is the global crisis of emergency department (ED) crowding, in which a considerable amount of evidence and attention has been directed.7 Population growth, increasing uninsured patients, poor access to preventive and primary care, the Emergency Medical Treatment and Active Labor Act in the USA, and hospital financial constraints have all contributed to the problem. Four general themes result from ED crowding—adverse outcomes, reduced quality, impaired access and resource loss.8

    In a recent systematic review by Hoot and Aronsky, it was reported that commonly studied causes of crowding included non-urgent visits, ‘frequent-flyer’ patients, inadequate staffing, inpatient boarding, and hospital bed shortages.9 Within the themes resulting from ED crowding (adverse outcomes, reduced quality, impaired access, resource loss), ambulance diversion is the most commonly studied in the category of impaired access. One point prevalence pilot study of a random sample of EDs in an American College of Emergency Physicians database, from Schneider et al, reported that more than 30% of institutions had been on ambulance diversion status on a given week.10 They suggested that the nation’s EDs even lack adequate resources to see patients on an average Monday evening.

    Burt et al analysed the National Hospital Ambulatory Medical Care Survey from 2003, data provided by 405 participating EDs on 40 253 visits.11 They reported that an estimated 501 000 ambulances were diverted, or approximately one ambulance per minute. Moreover, large EDs represented 47% of all hours spent in diversion status and 70% of all ambulances diverted to another ED.

    Evidence is lacking on whether ED crowding is associated with delays in resuscitation effort and/or increased mortality. Research from the Republic of Korea (South Korea) suggests that both of these outcomes are affected by crowding. Hong et al reported a retrospective study on 1296 ED admissions into their resuscitation room.12 Delayed resuscitative efforts (DRE) were noted in 17% and the incidence of DRE was significantly higher on crowded days (OR=2.0). Moreover, ED mortality and in-hospital mortality were both significantly higher in the DRE group (OR=3.4 and OR=4.0, respectively) compared with the non-DRE group.

    Managing the daily volume and throughput of patients in an ED or a trauma centre is a substantial challenge. The ED crowding epidemic has given rise and increased awareness to the study of surge capacity and volume in hospitals and healthcare systems.13 There is discussion over the transition, overlap and relationship between daily surge and extraordinary surge volume, as a multiple casualty incident (MCI) would bring. More debate concerns the distinctions between surge capacity, surge response capacity and surge capability; although our study appears to indicate the mortality of patients is not impacted by routine or daily trauma centre overcapacity or surge volume.

    There is a paucity of research on the impact of ED daily surge volume on outcome of trauma patients. Ball et al retrospectively reviewed 861 severely injured trauma patients (ISS ≥12) presenting to their regional trauma centre.14 They defined MCI as three or more trauma admissions within 3 hours. In this study, 10% of all trauma patients were treated in an MCI setting. MCI patients had a significantly greater time to first surgical procedure, time to emergency laparotomy, and ED and hospital lengths of stay. MCI and non-MCI patients did not differ in intensive care unit length of stay or mortality. In our study, new admission hospital and 24 hours mortality were only associated with new admission age, prehospital GCS and SI. These findings would support our contention that in severe overcrowding, the number of patients and a recent surge of elderly/debilitated patients may affect the mortality risk of new admissions.

    One limitation of this study is the 6-year retrospective design and the use of two large databases. The Admissions-Discharges-Transfers (ADT) database was merged and cross-correlated with our trauma registry of vital signs and outcomes. As shown in our patient enrolment and analysis flow diagram, 479 patients were excluded from analysis because of ADT records mismatch. Another limitation of analysis is that the TRU capacity was somewhat variable because the facility underwent construction and expansion during the study period. Although the capacity ranged from 10 to 13 trauma resuscitation bays, the exact transition dates and daily variance were not defined. Finally, our Facility/Patient Critical Index Viewer was created using retrospective data. Therefore, analyses on facility acuity were based on patient demographics and admission vital signs. Exact real-time facility acuity could only be prospectively evaluated using continuous vital signs monitoring and recording of all existing patients in the admitting area.

    In summary, this study analysed the dynamic relationship and interaction between trauma centre capacity and volume on the mortality risk of incoming new admissions in a mature trauma system. Our results show that the mortality of incoming patients is not impacted by routine or daily trauma centre overcapacity and surge volume, indicating transport to the nearest functional trauma centre would be generally recommended. In conditions of severe overcrowding, the number of admitting area or ED patients with shock physiology and a recent surge of elderly/debilitated patients are the most relevant considerations affecting the mortality risk of new admissions.

    Prehospital scoring systems and algorithms currently inform our decisions on which patients should be brought to a trauma centre. Integrating contemporaneous information about facility acuity and capacity status would enhance real-time best destination decision making. We had created a Facility/Patient Critical Index Viewer specifically for this project, allowing comprehensive review and analysis of admitting area status (patient volume, recent admissions, admission vital signs) at any point, and at all points in time throughout the study period. Future development and monitoring of a real-time continuous prospective Facility/Patient Critical Index Viewer will allow EMS providers, EMS systems and trauma centres to collaboratively improve best destination decision making. This methodology can be used to further study trauma centre capacity, resource utilisation and bypass decisions in addition to the obvious military applications as they relate to remote, forward deployed operating locations and access to more robust surgical facilities elsewhere in the theatre of operations.

    Data availability statement

    All data relevant to the study are included in the article or uploaded as online supplementary information. All data from randomised/anonymous patient identification numbers.

    Ethics statements

    Patient consent for publication

    Ethics approval

    The protocol and procedures for this current project were approved by both the University of Maryland Baltimore (HP-00049488, Trauma Injury and Evacuation Timeline) and the Air Force Research Laboratory (FWR20110164H, Traumatic Injury and Medical Evacuation - Patient Outcomes) Institutional Review Boards.

    References

    1. ↵
      1. R Adams Cowley Shock Trauma Center
      . Available: https://www.umms.org/ummc/health-services/shock-trauma [Accessed 23 Mar 2020].
    2. ↵
      1. The Maryland Medical Protocols for Emergency Medical Services Providers
      . Available: http://www.miemss.org/home/Portals/0/Docs/Guidelines_Protocols/2017-MD-Medical-Protocols-WEB.pdf?ver=2017-04-04-143321-600 [Accessed 23 Mar 2020].
    3. ↵
      2. Brown JB ,
      3. Forsythe RM ,
      4. Stassen NA , et al
      . The National trauma triage protocol: can this tool predict which patients with trauma will benefit from helicopter transport? J Trauma Acute Care Surg 2012;73:319–25.doi:10.1097/TA.0b013e3182572bee pmid:http://www.ncbi.nlm.nih.gov/pubmed/22846934
      OpenUrlCrossRefPubMedWeb of Science
    4. ↵
      2. Galvagno SM ,
      3. Haut ER ,
      4. Zafar SN , et al
      . Association between helicopter vs ground emergency medical services and survival for adults with major trauma. JAMA 2012;307:1602–10.doi:10.1001/jama.2012.467 pmid:http://www.ncbi.nlm.nih.gov/pubmed/22511688
      OpenUrlCrossRefPubMedWeb of Science
    5. ↵
      2. Mackenzie EJ ,
      3. Rivara FP ,
      4. Jurkovich GJ , et al
      . The national study on costs and outcomes of trauma. J Trauma 2007;63:S54–67.doi:10.1097/TA.0b013e31815acb09 pmid:http://www.ncbi.nlm.nih.gov/pubmed/18091213
      OpenUrlCrossRefPubMedWeb of Science
    6. ↵
      2. MacKenzie EJ ,
      3. Rivara FP ,
      4. Jurkovich GJ , et al
      . A national evaluation of the effect of trauma-center care on mortality. N Engl J Med 2006;354:366–78.doi:10.1056/NEJMsa052049 pmid:http://www.ncbi.nlm.nih.gov/pubmed/16436768
      OpenUrlCrossRefPubMedWeb of Science
    7. ↵
      2. MacKenzie EJ ,
      3. Weir S ,
      4. Rivara FP , et al
      . The value of trauma center care. J Trauma 2010;69:1–10.doi:10.1097/TA.0b013e3181e03a21 pmid:http://www.ncbi.nlm.nih.gov/pubmed/20622572
      OpenUrlCrossRefPubMedWeb of Science
    8. ↵
      2. Vieth TL ,
      3. Rhodes KV
      . The effect of crowding on access and quality in an academic ED. Am J Emerg Med 2006;24:787–94.doi:10.1016/j.ajem.2006.03.026 pmid:http://www.ncbi.nlm.nih.gov/pubmed/17098098
      OpenUrlCrossRefPubMedWeb of Science
    9. ↵
      2. Hoot NR ,
      3. Aronsky D
      . Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med 2008;52:126–36.doi:10.1016/j.annemergmed.2008.03.014 pmid:http://www.ncbi.nlm.nih.gov/pubmed/18433933
      OpenUrlCrossRefPubMedWeb of Science
    10. ↵
      2. Schneider SM ,
      3. Gallery ME ,
      4. Schafermeyer R , et al
      . Emergency department crowding: a point in time. Ann Emerg Med 2003;42:167–72.doi:10.1067/mem.2003.258 pmid:http://www.ncbi.nlm.nih.gov/pubmed/12883503
      OpenUrlCrossRefPubMedWeb of Science
    11. ↵
      2. Burt CW ,
      3. McCaig LF ,
      4. Valverde RH
      . Analysis of ambulance transports and diversions among US emergency departments. Ann Emerg Med 2006;47:317–26.doi:10.1016/j.annemergmed.2005.12.001 pmid:http://www.ncbi.nlm.nih.gov/pubmed/16546615
      OpenUrlCrossRefPubMedWeb of Science
    12. ↵
      2. Hong KJ ,
      3. Shin SD ,
      4. Song KJ , et al
      . Association between ED crowding and delay in resuscitation effort. Am J Emerg Med 2013;31:509–15.doi:10.1016/j.ajem.2012.09.029 pmid:http://www.ncbi.nlm.nih.gov/pubmed/23159432
      OpenUrlPubMed
    13. ↵
      2. Nager AL ,
      3. Khanna K
      . Emergency department surge: models and practical implications. J Trauma 2009;67:S96–9.doi:10.1097/TA.0b013e3181ad2aaa pmid:http://www.ncbi.nlm.nih.gov/pubmed/19667863
      OpenUrlCrossRefPubMedWeb of Science
    14. ↵
      2. Ball CG ,
      3. Kirkpatrick AW ,
      4. Mulloy RH , et al
      . The impact of multiple casualty incidents on clinical outcomes. J Trauma 2006;61:1036–9.doi:10.1097/01.ta.0000231764.00067.54 pmid:http://www.ncbi.nlm.nih.gov/pubmed/17099505
      OpenUrlCrossRefPubMed

    Footnotes

    • Presented at Poster presented at the 118th Annual Continuing Education Meeting of the Association of Military Surgeons of the United States (AMSUS), the Society of Federal Health Professionals, November 5, 2013, Seattle, Washington.

    • Contributors WCC: contributed to literature search, study design, data collection, data analysis, data interpretation, writing and critical revision. JMH and SAS: contributed to study design, data analysis, data interpretation and critical revision. PFH, S-YC and HHC: contributed to study design, data collection, data analysis, statistical analysis, data interpretation and critical revision. CFM and CHM: contributed to data analysis, data interpretation and critical revision. DBP, JJD and CC: contributed to literature search, study design and critical revision. RF and TMS: contributed to data interpretation and critical revision. DBP and JJD: contributed to funding from United States Air Force/Air Force Material Command

    • Funding This material is based on a research grant sponsored by the United States Air Force / Air Force Materiel Command (USAF/AFMC) 711th Human Performance Wing (711 HPW/XPT) under Cooperative Agreement number FA8650-11-2-6142, entitled 'Expeditionary Medicine, Trauma, and En Route Care (EMTEC) Research and Technology Support,' and all Agreement Orders placed under this Cooperative Agreement Award number FA8650-11-2-6D03. The US government is authorised to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation thereon.

    • Disclaimer The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the United States Air Force, the Department of Defense, or the U.S. Government.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; internally peer reviewed.