Article

Prehospital quick sequential organ failure assessment as a tool to predict in-hospital mortality

Prehospital quick sequential organ failure assessment as a tool to predict in-hospital mortality

Kyohei Miyamoto, MD ?, Naoaki Shibata, MD, Tsuyoshi Nakashima, MD, Seiya Kato, MD, PhD

Department of Emergency and Critical Care Medicine, Wakayama Medical University, 811-1, Kimiidera, Wakayama City, Wakayama 641-8509, Japan

a r t i c l e i n f o

Article history:

Received 11 January 2018

Received in revised form 5 February 2018

Accepted 9 February 2018

Keywords: Quick sequential organ failure assessment score Physician-staffed helicopter

In-hospital mortality Trauma

Prehospital

a b s t r a c t

Objective: This study aimed to evaluate the Predictive ability of Quick Sequential Organ Failure Assessment score for in-hospital mortality among patients transported by physician-staffed helicopters.

Methods: We conducted a single-center, retrospective observational study using the physician-staffed helicopter registry data between 2003 and 2016. We calculated the qSOFA scores based on the patients’ vital signs, which were measured on the scene. The tool’s discriminatory ability was determined using the area under the curve of the receiver operating characteristic.

Results: A total of 1849 patients with a mean age of 63.0 (standard deviation [SD], 18.4) years were included in this study. The Diagnostic categories included were trauma and nontrauma cases (1038 [56%] and 811 [44%], re- spectively). In-hospital mortality was documented in 169 (9%) patients. Meanwhile, the in-hospital mortality rates among patients with qSOFA scores of 0, 1, 2, and 3 were 5/411 (1%), 69/797 (9%), 71/541 (13%), and 24/ 100 (24%), respectively (P b 0.0001 for trend). If the cutoff point is >=1, the sensitivity and specificity of the qSOFA scores were 0.97 and 0.24, respectively. The area under the curve of the qSOFA scores was 0.67 for all pa- tients, whereas that for trauma patients was 0.75.

Conclusion: An increase in the qSOFA score is associated with a gradual increase in the in-hospital mortality rate among all patients. In particular, a very low mortality rate was observed among patients with a qSOFA score of 0. The qSOFA score predicted the in-hospital mortality of patients with trauma well.

(C) 2018

Introduction

On-the-scene Mortality prediction during prehospital triage is im- portant for the selection of an appropriate hospital for the patient. How- ever, identifying high-risk patients in a prehospital setting is difficult because of the short time interval available for this step and the lack of objective examination. Therefore, several scoring systems have been proposed as assistive tools to predict hospital mortality during out-of-hospital triage. For instance, we can use the Triage Revised Trauma Score [1] and prehospital critical illness score [2] for trauma and non-trauma patients, respectively. However, these scoring systems are difficult to remember, and simpler systems than the cur- rently available ones have been sought.

Recently, the Quick Sequential Organ Failure Assessment scoring system has been developed to rapidly identify patients who will likely have poor outcomes among those with suspected infections [3]. The qSOFA scoring system has only three binary variables that can

* Corresponding author at: Department of Emergency and Critical Care Medicine, Wakayama Medical University, 811-1, Kimiidera, Wakayama City, Wakayama, Japan.

E-mail addresses: [email protected] (K. Miyamoto), [email protected] (T. Nakashima), [email protected] (S. Kato).

be rapidly assessed: Glasgow coma scale (GCS), systolic blood pressure (SBP), and respiratory rate (RR). This scoring system was validated among patients in the emergency department with infection [4] and those without infection, such as in the case of trauma [5,6]. However, the literature on the use of qSOFA in the prehospital setting remains sparse. Therefore, the present study aimed to evaluate the discrimina- tory ability of qSOFA for in-hospital mortality among patients transported by physician-staffed helicopter.

Materials and methods

This retrospective observational study was conducted in a tertiary care hospital that operates a physician-staffed helicopter system and is located in a rural area in Japan. Physician-staffed helicopter transports critically ill patients from the scene to hospitals or from hospitals to hos- pitals. In our area, emergency medical services triage patients to deter- mine those who are critically ill and request the dispatch of a physician- staffed helicopter. The system covered an area with a population of ap- proximately a million people. We included patients aged >=20 years old who were transported by the physician-staffed helicopter from the scene to Wakayama Medical University between January 2003 and De- cember 2016. We excluded patients who were transferred from other

https://doi.org/10.1016/j.ajem.2018.02.009

0735-6757/(C) 2018

hospitals, transported to other hospitals, and b20 years old and had car- diopulmonary arrest on the scene and missing data on discharge out- come or vital signs on the scene, including RR, GCS score, or SBP.

The information available on the physician-staffed helicopter regis- try of Wakayama Medical University was prospectively collected. In contrast, we retrospectively retrieved the data used in the present study from this registry. The in-hospital mortality was the primary out- come, whereas the intensive care unit admission was the second- ary outcome. In terms of the characteristics of the patients, we divided them into the following five groups based on their diagnostic catego- ries: trauma, stroke, cardiovascular disorders, infection, and others. We classified patients who had burns or experienced Near drowning to ‘others,’ not trauma.

The qSOFA score was calculated based on the patients’ initial vital signs, which were measured by the staff members of the physician- staffed helicopter on the scene without any interventions, such as fluid, vasopressor, and sedative administration or intubation, except for oxygen supplementation via face mask. The qSOFA variables in- cluded were <=14 GCS score, <=100 SBP, and >=22 RR. Each variable that met the criteria was given 1 point and 0 point otherwise. The qSOFA score ranged from 0 to 3, with a higher score indicating a more severe disease [3].

For reference, we also evaluated two additional scoring systems that are frequently used in the prehospital setting, namely, the T-RTS and prehospital critical illness score, for trauma and nontrauma patients, re- spectively. These scoring systems were calculated based on the patients’ vital signs, which were measured at the same time as that of the qSOFA. Similar to the qSOFA score, the T-RTS includes three variables, that is, GCS, SBP, and RR, with each variable contributing a maximum of 4 points. The T-RTS ranged from 0 to 12, with a lower score indicating a more severe disease [1]. In contrast, the prehospital critical illness score includes six variables, namely, GCS, SBP, RR, heart rate, pulse ox- imetry, and age, with each variable contributing a maximum of 1 or 2 points. The prehospital critical illness scored ranged from 0 to 8, with a higher score indicating a more severe disease [2].

Statistical analysis

Continuous variables were presented as mean +- standard deviation (SD) or median and interquartile range (IQR). Meanwhile, categorical variables were presented as numbers and percentages (%). For compar- isons between survivors and nonsurvivors, we used the chi-square or Fisher’s exact test for categorical variables and t-test or Wilcoxon rank sum test for continuous variables. We used the Cochran-Armitage trend test to analyze the association between the qSOFA scores and

outcomes. The discriminatory ability of the qSOFA was determined using the area under the curve receiver operating characteristic. A two-sided P value b 0.05 was considered statistically significant, and all analyses were performed using the JMP Pro software (version 12.2; SAS Institute Inc., Cary, NC, USA).

The study design was approved by the Institutional Review Board of Wakayama Medical University, which waived the requirement for in- formed consent because of the retrospective and observational nature of the study.

Results

During the 14-year study period, 5056 patients were transported by the physician-staffed helicopter. Finally, 1849 patients were analyzed in this study (Fig. 1). Table 1 shows the patients’ characteristics. More than half of the patients had trauma cases (56%). The median qSOFA score on the scene was 1.

In terms of the primary and secondary outcomes, among all the pa- tients, 169 (9.1%) died in the hospital, and 299 (16.2%) were admitted to the intensive care unit (ICU). Meanwhile, ICU admission or in-hospital mortality was documented in 411 patients (22.2%). As the qSOFA score increased, the rates of these outcomes also significantly increased (P b 0.0001 for trend for all three outcomes, Fig. 2).

We calculated the sensitivity, specificity, and diagnostic likelihood ratio for each qSOFA score cutoff point (Table 2). A cutoff point of >=1 re- sulted in a sensitivity and specificity of 0.97 (95% confidence interval [CI], 0.93-0.99) and 0.24 (95% CI, 0.24-0.24), respectively. The five cases with false-negative results included the following: burn (n = 2), terminal cancer with a do-not-resuscitate order (n = 1), cerebral infarc- tion (n = 1), and myocardial infarction (n = 1). On the contrary, if the cutoff point was >=2, the sensitivity and specificity were 0.56 (95% CI, 0.49-0.63) and 0.68 (95% CI, 0.67-0.68), respectively. In the subgroup of patients with trauma cases (n = 1038), the number of cases with false-negative results was 0, the sensitivity was 1.00 (95% CI, 0.95-1.00), and the negative likelihood ratio was 0.00 (95% CI 0.00-0.17) if the cutoff point was >=1 (Table s1). Concurrently, the sensi- tivity and the specificity were relatively low in the subgroup of patients with nontrauma cases (n = 811) (Table s2).

Fig. 3 presents the area under the curve (AUC) of the receiver oper- ating characteristic. The AUC of the qSOFA scores in all patients was 0.67 (95% CI, 0.63-0.71), whereas that of the qSOFA scores in trauma patients was 0.75 (95% CI, 0.70-0.80). The AUC of the qSOFA scores in fifty pa- tients with infection was 0.66 (95% CI, 0.43-0.83). The AUC of the qSOFA scores was significantly lower than that of the T-RTS and prehospital critical illness score (P b 0.001 for each score). If a T-RTS

2453 patients transported by physician-staffed helicopter meeting all inclusion criteria

2603 excluded

1297 transferred from other hospitals 884 transported to other hospitals 332 less than 20 years old

90 with cardiopulmonary arrest on scene

5056 patients transported by physician-staffed helicopter

Fig. 1. Patient flowchart.

1849 patients analyzed

604 missing data

446 respiratory rate

100 systolic blood pressure 30 Glasgow coma scale

28 outcomes at discharge

Table 1

Patients’ characteristics

Characteristics

All patients (n = 1849)

Survivor (n = 1680)

Non-survivor (n = 169)

P value

Age, y, mean +- SD

63.0 +- 18.4

61.8 +- 14.2

75.0 +- 14.2

b0.0001

Male, number (%)

1285 (70)

1186 (71)

99 (59)

0.0012

diagnostic category

b0.0001

Trauma, number (%)

1038 (56)

968 (58)

70 (41)

Stroke, number (%)

456 (25)

390 (23)

66 (39)

Cardiovascular, number (%)

134 (7)

128 (8)

6 (4)

Infection, number (%)

50 (3)

43 (3)

7 (4)

Others, number (%)

Glasgow coma scale, median (IQR)a

171 (9)

14 (11-15)

151 (9)

14 (12-15)

20 (12)

7 (4-12)

b0.0001

Glasgow coma scale b 15, number (%)a

1011 (55)

856 (51)

155 (92)

b0.0001

Respiratory rate, breaths/min, mean +- SDa

22.7 +- 6.5

22.7 +- 6.4

23.2 +- 7.3

0.28

Respiratory rate >= 22 breaths/min, number (%)a

925 (50)

830 (49)

95 (56)

0.092

Systolic blood pressure, mmHg, mean +- SDa

137.4 +- 34.3

137.4 +- 33.3

137.9 +- 43.1

0.86

Systolic blood pressure <= 100 mm Hg, number (%)a

243 (13)

210 (13)

33 (20)

0.010

Heart rate, beats/min, mean +- SDa,b

84.1 +- 22.1

83.8 +- 21.3

87.9 +- 28.3

0.021

Pulse oximetry, %, median (IQR)a,c

99 (98-100)

99 (98-100)

99 (96-100)

0.047

qSOFA score, median (IQR)

1 (1-2)

1 (1-2)

2 (1-2)

b0.0001

T-RTS, median (IQR)

12 (11-12)

12 (11-12)

10 (10-11)

b0.0001

Prehospital critical illness score, median (IQR)d

2 (1-3)

2 (1-3)

3 (3-4)

b0.0001

SD: standard deviation, IQR: interquartile range, qSOFA: quick sequential organ failure assessment, T-RTS: triage revised trauma score.

a Vital signs were measured on-scene with no intervention.

b Heart rate was missing in six patients.

c SpO2 was missing in 55 patients.

d Prehospital critical illness score was missing in 60 patients.

cutoff point of <=11 (not a full score) was used to obtain the highest sen- sitivity, the sensitivity and specificity were 0.88 (95% CI, 0.82-0.92) and

0.59 (95% CI, 0.58-0.59) respectively. Meanwhile, if a prehospital critical illness score cutoff point of >=1 was used, the sensitivity was 1.00 (95% CI, 0.97-1.00) but the specificity was only 0.07 (95% CI, 0.07-0.07). Fur- thermore, if a prehospital critical illness score cutoff point of >=2 was used, the sensitivity and specificity were 0.97 (95% CI, 0.93-0.99) and

0.33 (95% CI, 0.32-0.33), respectively.

Discussion

The present study revealed an association between qSOFA score and in-hospital mortality among patients transported using a physician- staffed helicopter. As the qSOFA scores of the patients increased, the in-hospital mortality rate also significantly increased. A cutoff point >=1 resulted in the very high sensitivity (97%) of the qSOFA score for in- hospital mortality. Meanwhile, the AUC of the qSOFA scores for all pa- tients was modestly high (0.67). However, the AUC of the qSOFA scores for trauma patients (0.75) was higher than that for all patients.

70

60

Proportion (%)

Previous studies have calculated and validated the qSOFA scores of patients with suspected infections [4,7,8]. Simultaneously, the qSOFA scores of patients without infection have also been evaluated. Singer et al. [6] reported that the qSOFA score predicted the in-hospital mortal- ity of patients with and without suspected infections well (AUC, 0.75 and 0.70 for patients with and without suspected infections, respec- tively) [6]. Additionally, Jawa et al. [5] also showed that the qSOFA score predicted the in-hospital mortality of patients with trauma well (AUC, 0.73) [5]. The discriminatory ability of the qSOFA scoring system in the present study is similar to that in previous studies.

Additionally, Jawa et al. [5] demonstrated that the AUC of the qSOFA scores was almost the same as that of the RTS (AUC: 0.73 and 0.74 for qSOFA scores and RTS, respectively) [5]. In contrast, the present study showed that the AUC of the qSOFA scores was significantly lower than that of the T-RTS among patients with trauma (AUC: 0.75 and 0.88 for qSOFA scores and T-RTS, respectively). In contrast, the qSOFA score ex- hibited higher sensitivity for in-hospital mortality than the T-RTS (0.97 and 0.88 for qSOFA score and T-RTS, respectively). Considering that a scoring system should have a high sensitivity to identify low-risk pa- tients, the qSOFA score seems to be more useful than the T-RTS in the prehospital setting. In fact, Giannakopoulos et al. [9] showed that the maximum T-RTS was not a safe triage tool for helicopter emergency medical service cancellations [9].

50 Hospital mortality

40 ICU admission

30 Hospital mortality

20 or ICU admission

10

0

qSOFA score

0

1

2

3

Hospital mortality, n (%)

5 (1)

69 (9)

71 (13)

24 (24)

ICU admission, n (%)

36 (9)

106 (13)

108 (20)

49 (49)

Table 2

Operating characteristics for each threshold of quick sequential organ failure assessment score

Hospital mortality or ICU admission, n (%)

38 (9)

160 (20)

153 (28)

60 (60)

411

797

541

100

Total, n

Fig. 2. Quick sequential organ failure assessment scores and outcomes. As the quick sequential organ failure assessment scores increased, the rate of each outcome also increased (P b 0.0001 for each outcome, Cochran-Armitage trend test). Abbreviations: qSOFA, quick sequential organ failure assessment; ICU, intensive care unit.

Operating characteristics

Sensitivity

0.97

0.56

0.14

(0.93-0.99)

(0.49-0.63)

(0.10-0.19)

Specificity

0.24

0.68

0.95

(0.24-0.24)

(0.67-0.68)

(0.95-0.96)

Positive likelihood ratio

1.3 (1.2-1.3)

1.7 (1.5-2.0)

3.1 (2.0-4.8)

Negative likelihood

0.12

0.65

0.90

ratio

(0.05-0.28)

(0.54-0.76)

(0.84-0.95)

Data were shown with 95% confidence interval.

>=1

>=2

>=3

Patients (n = 1849) True positive

164

95

24

False positive

1274

546

76

True negative

406

1134

1604

False negative

5

74

145

a b

1

0.8

0.6

Sensitivity

0.4

0.2

qSOFA

AUC 0.67 (95%CI 0.63-0.71)

T-RTS

AUC 0.81 (95%CI 0.77-0.84)

Prehospital critical illness score AUC 0.80 (95%CI 0.76-0.83)

1

0.8

0.6

Sensitivity

0.4

0.2

qSOFA

AUC 0.65 (95%CI 0.62-0.68)

T-RTS

AUC 0.75 (95%CI 0.72-0.77)

Prehospital critical illness score AUC 0.74 (95%CI 0.71-0.77)

0

0 0.2 0.4 0.6 0.8 1

1 – Specificity

0

0 0.2 0.4 0.6 0.8 1

1 – Specificity

c d

1

qSOFA

AUC 0.75 (95%CI 0.70-0.80)

0.8

T-RTS

Sensitivity

Sensitivity

1

0.8

qSOFA

AUC 0.74 (95%CI 0.71-0.78)

T-RTS

0.6

AUC 0.88 (95%CI 0.83-0.92)

0.6

AUC 0.86 (95%CI 0.83-0.89)

0.4

Prehospital critical illness score AUC 0.88 (95%CI 0.83-0.91)

0.4

Prehospital critical illness score AUC 0.83 (95%CI 0.79-0.86)

0.2 0.2

0

e

1

0.8

0.6

Sensitivity

0 0.2 0.4 0.6 0.8 1

1 – Specificity

qSOFA

AUC 0.59 (95%CI 0.54-0.64)

T-RTS

AUC 0.74 (95%CI 0.68-0.79)

0

f

1

0.8

0.6

Sensitivity

0 0.2 0.4 0.6 0.8 1

1 – Specificity

qSOFA

AUC 0.55 (95%CI 0.51-0.59)

T-RTS

AUC 0.61 (95%CI 0.57-0.65)

0.4

Prehospital critical illness score AUC 0.71 (95%CI 0.65-0.76)

0.4

Prehospital critical illness score AUC 0.62 (95%CI 0.57-0.66)

0.2 0.2

0

0 0.2 0.4 0.6 0.8 1

1 – Specificity

0

0 0.2 0.4 0.6 0.8 1

1 – Specificity

Fig. 3. Area under the receiver operating characteristic curve for three scoring systems for various outcomes. a, Hospital mortality of all patients. b, Hospital mortality or ICU admission of all patients. c, Hospital mortality of trauma patients. d, Hospital mortality or ICU admission of trauma patients. e, Hospital mortality of non-trauma patients. f, Hospital mortality or ICU admission of non-trauma patients. Abbreviations: qSOFA, quick sequential organ failure assessment; AUC, area under the curve; 95% CI, 95% confidential interval; T-RTS, triage revised trauma score; ICU, intensive care unit.

Meanwhile, Kievlan et al. [10] conducted an external validation study among prehospital nontrauma patients and reported that the AUC of the prehospital critical illness score for in-hospital mortality was 0.77 [10], which was similar to the AUC (0.71) in the present study. However, the AUC of the qSOFA score was lower than that of the prehospital critical illness score in the present study, although the sensitivity (0.97 and 0.97 for the qSOFA and prehospital critical illness scores, respectively) and specificity (0.24 and 0.33 for the >=1 qSOFA and >=2 prehospital critical illness score, respectively) were almost iden- tical. In case the predictive abilities of both scores were similar, the sim- pler score is more useful in the prediction of in-hospital mortality, that is, in this instance, the qSOFA score.

A recent study showed that standard vital signs and its combinations such as the Shock Index (heart rate/systolic blood pressure) cannot pre- dict mortality well in a prehospital setting [11]. Therefore, new

measures, such as qSOFA score, are needed to improve triage. Another promising measure is end-tidal carbon dioxide, which can be measured noninvasively and continuously. Childress et al. reported in his small ob- servational study that initial out-of-hospital end-tidal carbon dioxide predicts mortality well in patients with trauma (AUC, 0.84) [12]. End- tidal carbon dioxide could have additional predictive values and should be validated in future trials.

The present study showed that the qSOFA score could be used to predict the in-hospital mortality of prehospital patients well. A cutoff point of >=1 is adequate to maintain a high sensitivity when triaging pa- tients to identify those who are low risk.

The comparison of the discriminatory abilities of the qSOFA and two popular prehospital scoring systems is one of the strengths of our study. Considering that the literature on the use of qSOFA score in the prehospital setting remains scarce, the results of our study is important

Declaration of interests”>in the application of this scoring system during prehospital triage. Fur- thermore, we evaluated the vital signs of the patients without any inter- ventions, such as intubation or sedative administration. Therefore, the vital signs were accurately analyzed in our study.

However, our study has several limitations to consider when interpreting the results. First, patients who were transported to other hospitals, most of which were nonTertiary care hospitals, were excluded in this study. Hence, the patients excluded in our study might have milder conditions than those who were included, so we might have underestimated the discriminatory abilities of the three scoring systems evaluated here. Second, our study focused on a particular context, that is, prehospital patients who were transported by a physician-staffed he- licopter, which may limit the extrapolation of our results to other cir- cumstances. Thus, future studies on the evaluation of the qSOFA scores among triage patients in a prehospital setting are warranted.

Conclusions

As the qSOFA scores of the patients increased, the in-hospital mor- tality rate also gradually increased. In particular, patients with a qSOFA score of 0 exhibited very low mortality rate. Meanwhile, a cutoff point of >=1 provided high sensitivity for in-hospital mortality. Addition- ally, the qSOFA scores predicted the in-hospital mortality of patients with trauma well.

Supplementary data to this article can be found online at https://doi. org/10.1016/j.ajem.2018.02.009.

List of abbreviations

qSOFA quick sequential organ failure assessment CI confidence interval

AUC area under the curve

T-RTS triage revised trauma score GCS Glasgow coma scale

SBP systolic blood pressure RR respiratory rate

ICU intensive care unit

SD standard deviation

IQR interquartile range

Ethics approval and consent to participate

This study was conducted in Wakayama Medical University and was approved by the Institutional Review Boards of the university. Informed consent was waived because of the retrospective nature of the study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and analyzed in this study are not publicly available due to privacy concerns and institutional policy.

Declaration of interests

None.

Funding

This research did not receive any specific grant from funding agen- cies in the public, commercial, or not-for-profit sectors.

Authors’ contributions

KM conceived the study idea, designed the study, and performed the data analysis. NS, TN, and SK helped to draft and revised this manu- script. All authors had read and approved the final manuscript.

Acknowledgments

Our study was presented in part at the 30th Annual Congress of the European Society of Intensive Care Medicine in Vienna, Austria, last Sep- tember 2017. We thank Editage for the English language editing.

References

  1. Champion HR, Sacco WJ, Copes WS, Gann DS, Gennarelli TA, Flanagan ME. A revision of the trauma score. J Trauma 1989;29:623-9.
  2. Seymour CW, Kahn JM, Cooke CR, Watkins TR, Heckbert SR, Rea TD. Prediction of critical illness during Out-of-hospital emergency care. JAMA 2010;304:747-54.
  3. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016;315:801-10.
  4. Freund Y, Lemachatti N, Krastinova E, Van Laer M, Claessens YE, Avondo A, et al. Prognostic accuracy of Sepsis-3 criteria for in-hospital mortality among patients with suspected infection presenting to the emergency department. JAMA 2017; 317:301-8.
  5. Jawa RS, Vosswinkel JA, McCormack JE, Huang EC, Thode Jr HC, Shapiro MJ, et al. Risk assessment of the blunt trauma victim: the role of the quick Sequential Organ Fail- ure Assessment score (qSOFA). Am J Surg 2017;214:397-401.
  6. Singer AJ, Ng J, Thode Jr HC, Spiegel R, Weingart S. Quick SOFA scores predict mor- tality in adult emergency department patients with and without suspected infection. Ann Emerg Med 2017;69:475-9.
  7. Seymour CW, Liu VX, Iwashyna TJ, Brunkhorst FM, Rea TD, Scherag A, et al. Assess- ment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016;315:762-74.
  8. Wang JY, Chen YX, Guo SB, Mei X, Yang P. Predictive performance of quick sepsis- related organ failure assessment for mortality and ICU admission in patients with in- fection at the ED. Am J Emerg Med 2016;34:1788-93.
  9. Giannakopoulos GF, Saltzherr TP, Lubbers WD, Christiaans HM, van Exter P, de Lange-de Klerk ES, et al. Is a maximum revised trauma score a safe triage tool for he- licopter emergency medical services cancellations? Eur J Emerg Med 2011;18: 197-201.
  10. Kievlan DR, Martin-Gill C, Kahn JM, Callaway CW, Yealy DM, Angus DC, et al. Exter- nal validation of a prehospital risk score for critical illness. Crit Care 2016;20:255.
  11. Liu NT, Holcomb JB, Wade CE, Salinas J. Inefficacy of standard vital signs for predicting mortality and the need for prehospital Life-saving interventions in blunt trauma patients transported via helicopter: a repeated call for new measures. J Trauma Acute Care Surg 2017;83:S98-s103.
  12. Childress K, Arnold K, Hunter C, Ralls G, Papa L, Silvestri S. Prehospital end-tidal car- bon dioxide predicts mortality in trauma patients. Prehosp Emerg Care 2017:1-5.

Leave a Reply

Your email address will not be published. Required fields are marked *