Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

The new trauma score (NTS): a modification of the revised trauma score for better trauma mortality prediction

  • Jin Hee Jeong1, 3,
  • Yong Joo Park2,
  • Dong Hoon Kim1, 3Email author,
  • Tae Yun Kim1,
  • Changwoo Kang1, 3,
  • Soo Hoon Lee1,
  • Sang Bong Lee1,
  • Seong Chun Kim1, 2 and
  • Daesung Lim2
Contributed equally
BMC SurgeryBMC series – open, inclusive and trusted201717:77

DOI: 10.1186/s12893-017-0272-4

Received: 19 February 2017

Accepted: 23 June 2017

Published: 3 July 2017

Abstract

Background

Since its introduction, the Revised Trauma Score (RTS) has been widely used to determine the prognosis of trauma patients. Recent studies have revealed a need to change the parameters of the RTS. We have designed a new trauma score (NTS) based on revised parameters, including the adoption of the actual Glasgow Coma Scale (GCS) score instead of a GCS code, the revision of the systolic blood pressure interval used for the code value and the incorporation of peripheral oxygen saturation (SpO2) instead of respiratory rate. The purpose of this study was to evaluate the predictive performance of the NTS for in-hospital mortality compared with the RTS and other trauma scores.

Methods

This was a prospective observational study using data from the trauma registry of a tertiary hospital. The subjects were selected from patients who arrived at the ED between July 1, 2014, and June 30, 2016, and, for external validation purposes, those who arrived at the ED between July 1, 2011, and June 30, 2013. Demographic data and physiological data were analyzed. NTS models were calculated using logistic regression for GCS score, SBP code values, and SpO2. The mortality predictive performance of NTS was compared with that of other trauma scores.

Results

A total of 3263 patients for derivation and 3106 patients for validation were included in the analysis. The NTS showed better discrimination than the RTS (AUC = 0.935 vs. 0.917, respectively, AUC difference = 0.018, p = 0.001; 95% CI, 0.0071–0.0293) and similar discrimination to that of mechanism, Glasgow Coma scale, age, and arterial pressure (MGAP) and the Glasgow Coma Scale, age, and systolic arterial pressure (GAP). In the validation cohort, the global properties of the NTS for mortality prediction were significantly better than those of the RTS (AUC = 0.919 vs. 0.906, respectively; AUC difference = 0.013, p = 0.013; 95% CI, 0.0009–0.0249) and similar to those of the MGAP and GAP.

Conclusions

The NTS predicts in-hospital mortality substantially better than the RTS.

Keywords

Trauma severity indices Injury severity score Emergency department

Background

In the 30 years since Champion et al. introduced the Revised Trauma Score (RTS), it has been widely used to assess prognosis in trauma patients. The RTS is a convenient tool for trauma triage and initial severity estimation that does not require sophisticated medical tests or devices and is especially useful in prehospital and emergency department (ED) settings. This physiological scoring system consists of the Glasgow Coma Scale (GCS), systolic blood pressure (SBP) and respiratory rate (RR). The parameters are converted to coded values (0, 1, 2, 3 or 4) assigned by specified ranges. Each value is multiplied by a weighted coefficient before it is added (Table 1) [1]. The RTS is calculated using the following equation:
Table 1

Modification of the Revised Trauma Score

Revised Trauma Score

New Trauma Score

Glasgow Coma Scale

Systolic Blood Pressure

Respiratory Rate

Coded Value

Glasgow Coma Scale

Systolic Blood Pressure

Oxygen saturation

13–15

>89

10–29

4

3–15

110–149

≥94

9–12

76–89

>29

3

≥150

80–93

6–8

50–75

6–9

2

90–109

60–79

4–5

1–49

1–5

1

70–89

40–59

3

0

0

0

<70

<40

RTS = (0.9368 x GCS code value) + (0.7326 x SBP code value) + (0.2908 x RR code value).

The Trauma and Injury Severity Score (TRISS), developed in 1987 by Boyd et al., has been used worldwide to predict trauma survival. The TRISS consists of physiological (RTS) and anatomical scores (Injury Severity Score, ISS) and age, stratified by the injury mechanism (blunt or penetrating trauma) [2]. The TRISS showed substantially improved predictive power of survival for trauma patients over that of the RTS and was validated in subsequent studies [3, 4]. Despite the complicated calculation and its inapplicability for triage, the TRISS remains the most widely used and prominent survival predictor for research in the quality control of trauma management and prevention.

The Mechanism, GCS, and Age and Arterial Pressure (MGAP) score is a recently developed physiological trauma scoring system. Similar to the TRISS, the MGAP utilized mechanism and age, which are important variables that affect the prognosis of trauma patients. The final scores can be easily obtained after the simple addition of several numbers. While the GCS is transformed to a code value ranging from 0 to 4 in the RTS, the MGAP consists of the actual GCS score with no variation due to its highly informative value and relatively unbiased calculation. Additionally, a SBP of 120 mmHg was chosen for the threshold, whereas 90 mmHg is the first cutoff for decreasing the code value in the RTS [5]. The GCS, Age and Systolic Arterial Pressure (GAP) is a scoring system that was simplified by deleting the mechanism from the MGAP [6]. The MGAP and GAP were shown to be superior to the RTS in mortality prediction of trauma patients. However, they remain inferior to the TRISS.

Uncontrolled hemorrhage is a major leading cause of traumatic injury that is responsible for 35% of prehospital deaths and over 40% of deaths within the first 24 h [7]. Traditionally, a SBP of <90 mmHg has been a widely accepted threshold for hypotension. The American College of Surgeons Committee on Trauma and National Expert Panel on Field Triage recommends a SBP of <90 mmHg for specialized trauma centers [8, 9]. Recently, the concept of a SBP of <90 mmHg as an early indicator of hypotension in trauma patients has become controversial. SBPs of 90–109 mmHg in the ED or the operating room result in worse outcomes than higher SBPs [10]. Furthermore, in a large population cohort study using data from the Trauma Audit and Research Network (n = 47,927), a SBP of <110 mmHg was identified as a cut off for hypotension, at which a significant increase in mortality occurred [11].

Instead of the recommended formal measurement technique of 1 min, the RR is commonly assessed during a short period of less than 30 s [12]. In the prehospital and ED settings, exact RR measurement by auscultation for 1 min is challenging due to patient conditions, loud noises and psychological or emotional pressures on medical personnel. Short RR counting for a 30-s interval is naturally inaccurate compared to a one-minute interval [13]. Recent research has shown that RR measurements obtained by triage nurses using an electronic device in the ED are inaccurate [14]. The same results were found in another study of medical doctors working at a teaching hospital who had been taught accurate measurement techniques immediately before the study [15]. Pulse oximetry is a popular monitoring method widely used in various settings [1620]. Peripheral oxygen saturation (SpO2) is an objective, efficient and unequivocal parameter for screening patient pulmonary function [21, 22]. Practically, SpO2 has become a substitute for the RR in the past decade. Accordingly, we speculated that SpO2 could be a better component than the RR for trauma mortality prediction.

The RTS is a widely valued but somewhat outdated scoring system for trauma mortality prediction. Therefore, we modified the RTS and designed a new trauma score based on recent developments in the trauma setting. The main ideas include (i) the adoption of the actual GCS score instead of a GCS code, (ii) the revision of the systolic blood pressure interval used for the code value and (iii) the incorporation of SpO2 instead of RR. The details are represented in Table 1. We termed this measure the New Trauma Score (NTS).

The purpose of this study was to evaluate the predictive performance of the NTS for in-hospital mortality compared to the RTS, MGAP and GAP as well as to provide a proper triage tool during the initial phase of trauma management.

Methods

Study design and participants

This is a prospectively recorded registry-based observational study using data from the trauma registry of a tertiary hospital located in Jinju, Republic of Korea. Data collection started on July 1, 2011 and was recorded by professional heath information managers in our ED. Demographic data, age, gender and physiological data regarding the initial presentation to the ED, as well as outcome and in-hospital mortality, were automatically transferred to the trauma registry from the electronic medical record. Injury mechanisms were categorized as blunt trauma, penetrating trauma, burn, drowning, hanging, asphyxia, poisoning and heat or cold-related injury. Abbreviated Injury Scales (AIS) were calculated according to clinical presentation, imaging results, intervention findings and operative records. Injury descriptions and scoring procedures were fully supervised by emergency physicians. The trauma registry was originally developed as a part of Emergency Department-based Injury In-depth Surveillance conducted by the Korea Centers for Disease Control and Prevention. Informed consent was not needed because the data were collected without identifiable personal information. Data used for derivation were obtained from patients arriving in the ED between July 1, 2014 and June 30, 2016. The inclusion criteria consisted of (i) patients categorized with blunt or penetrating mechanisms and (ii) age ≥ 15 years. The exclusion criteria consisted of (i) patients who died before ED arrival, (ii) patients discharged or transferred from the ED. We used the data from patients arriving at the same hospital between July 1, 2011 and June 30, 2013 for external validation purposes. This study was approved by the Gyeongsang National University hospital institutional review board (number 2016–09-008).

Development of NTS

To compare the value of the GCS codes used in the RTS and the actual GCS score, we plotted frequency charts for in-hospital mortality (Fig. 1a, b). Using the group with a score of 4 as a reference, the odds ratios of GCS codes of 3, 2, 1, and 0 were calculated as categorical variables in the univariate regression. The actual GCS scores were entered into the logistic regression as continuous variables. Both parameters were significant and were used in the next step.
Fig. 1

The mortality according to variables for the New Trauma Score. a Actual Glasgow Coma Scale score. b Coded value of Glasgow Coma Scale. c Systolic blood pressure. d Oxygen saturation

The patient distribution showed a bimodal rather than linear correlation between SBP and death (Fig 1c). We chose 110–149 mmHg as the range for a score of 4, and SBP ≥ 150 mmHg received a score of 3. Blood pressure measurement of trauma victims is very challenging in prehospital or ED triage settings, especially when patients are exsanguinating and progressing to profound shock. It might be impractical for clinical decision making if the interval boundaries are set too low. For that reason, we chose 70 mmHg as the lowest cutoff, and half of the interval (SBP 90 mmHg) was set as another cut-point. The SpO2 was divided into five categories, ≥ 94%, 80–93%, 60–79%, 40–59% and <40% (Fig 1d). Because no references are available to determine SpO2 interval ranges related to trauma mortality prediction, we must depend on clinical experience.

Data analysis

We performed multiple imputation using multivariate imputation by chained equation (MICE) to impute missing values [23]. The number of multiple imputations should reach the percentage of the missing proportion [24]. In our data set, the ISS was the most frequently missing variable (8.9% and 7.9% in the derivation and validation cohorts, respectively), followed by SpO2 (3.2% and 2.8%), SBP, RR, and GCS (not exceeding 0.1% in both cohorts). Therefore, we conducted ten imputations using a predictive mean-matching method for all variables included in the RTS, MGAP, GAP, and the NTS. Age, gender, the ISS and the outcome variable (in-hospital mortality) were also included in the imputation model.

The χ2 test and the Mann-Whitney U test (because all the continuous data showed non-normal distribution) were used to describe demographic and physiological characteristics of the derivation and validation groups.

A univariate analysis was performed on the GCS score, SBP, RR, SpO2, and the coded GCS value. Multivariate logistic regression was conducted using the GCS score, SBP and RR, SpO2 as continuous variables and using the GCS score, SBP and SpO2. We categorized the SBP and SpO2 and assigned code values of 0, 1, 2, 3 or 4 (Table 1). Multivariate logistic regression was performed using the actual GCS score, the coded SBP (SBPNTS) and SpO2 (SpO2NTS) values, and the coded GCS, SBPNTS, and SpO2NTS. Predictive survival (Ps) was calculated using the following equation: Ps = 1 / (1 + e-b), b = b0 + b1 x GCS + b2 x SBPNTS + b3 x SpO2NTS. b0, b1, b2 and b3 were coefficients derived in the regression analysis. The discriminatory ability of the final models was assessed using the receiver operating characteristics (ROC) curve, and calibration was evaluated using the Hosmer-Lemeshow (H-L) statistic. The areas under the ROC curve (AUC) were compared between the NTS, RTS, MGAP and GAP using the nonparametric approach described by DeLong et al. [25]. We deleted the regression coefficients from the NTS to provide an easy triage tool called the NTS for Triage (T-NTS).

The sensitivity and specificity were obtained from the point of the ROC curve. The specificity of NTS was compared to those of RTS, MGAP, GAP, respectively, at the point at which their sensitivity reached 95% [4, 10]. We divided patients into four groups according to the T-NTS and observed mortality in each group. The final model was validated with separate data using ROC curve analysis to compare our method with the RTS, MGAP and GAP.

All p values were two-sided, and a value of p < 0.05 was considered statistically significant. Analyses were performed using MedCalc 17 (MedCalc Software BVBA, Ostend, Belgium) and Stata version 13 (StataCorp, LP, College Station, TX).

Results

Baseline characteristics

A total of 24,128 patients were enrolled in the Gyeongsang National University Hospital Trauma Registry between January 2014 and June 2016. Among these patients, 13,862 matched the inclusion criteria (age ≥ 15 years, blunt or penetrating trauma) for the derivation cohort. Nineteen patients were confirmed dead on ED arrival, and 10,580 were discharged from the ED or transferred to other medical facilities. A total of 3263 patients were finally included for analysis. A total of 12,403 out of 21,461 patients were included in the validation cohort. Fifty-three patients were confirmed dead on ED arrival, and 9244 were discharged from the ED or transferred to other medical facilities. A total of 3106 patients were finally included in the analysis (Fig. 2).
Fig. 2

Patient flow according to inclusion and exclusion criteria. a Derivation cohort. b Validation cohort

The patients in the derivation cohort had a median age of 60 (IQR, 46–73) years, and 66.0% were male. A total of 94.4% suffered from blunt trauma. The median SBP was 130 (IQR, 110–140) mmHg, the median RR was 20 (IQR, 18–20) per minute, the median GCS was 15 (IQR, 15–15) and the median SpO2 was 98% (IQR, 96% - 99%). The median RTS was 7.84 (IQR, 7.84–7.84), and the median ISS was 9 (IQR, 4–13). Overall, the in-hospital mortality rate was 11.3%. Table 2 shows the main demographic characteristics and physiological data of both groups.
Table 2

Characteristics of the derivation and validation groups

Characteristics

Derivation cohort

Missing case

Validation cohort

Missing case

(N = 3263)

N, %

(N = 3106)

N, %

Age, yrs., median (IQR)

60 (46–73)

0 (0)

59 (45–72)a

0 (0)

Men (%)

2155 (66.0)

0 (0)

2065 (66.5)b

0 (0)

Trauma type, n (%)

 Blunt trauma

3079 (94.4)

0 (0)

2944 (94.8)

0 (0)

 Penetrating trauma

184(5.6)

0 (0)

162 (5.2)

0 (0)

Physiological parameter, median (IQR)

 Systolic blood pressure (mmHg)

130 (110–140)

4 (0.1)

135 (118–150)a

0 (0)

 Respiratory rate (per a minute)

20 (18–20)

4 (0.1)

20 (18–20)a

0 (0)

 Glasgow Coma Scale

15 (15–15)

3 (0.1)

15 (15–15)

2 (0.1)

 Oxygen saturation (%)

98 (96–99)

105 (3.2)

98 (96–99) a

87 (2.8)

Trauma location, n (%)

 Head & neck injury

1057 (32.4)

 

1290 (41.5) b

 

 Face injury

739 (22.6)

 

612 (19.7) b

 

 Chest injury

857 (26.2)

 

804 (25.9)

 

 Abdomen injury

500 (15.3)

 

440 (14.2)

 

 Extremity injury

1383 (42.4)

 

1241 (40.0)

 

RTS, median (IQR)

7.84 (7.84–7.84)

6 (0.2)

7.84 (7.84–7.84)

2 (0.1)

ISS, median (IQR)

9 (4–13)

289 (8.9)

9 (4–14)a

244 (7.9)

Death, n (%)

368 (11.3)

0 (0)

332 (10.7)

0 (0)

RTS Revised Trauma Score; ISS Injury Severity Score

a p < 0.05 compared with derivation group using Mann-Whitney U test

b p < 0.05 compared with derivation group using chi-square test

Actual GCS vs. coded GCS value in the univariate analysis

The mortality prediction of the actual GCS score and the coded GCS value were also compared using univariate logistic regression. The OR of the actual GCS score was 1.62 (p < 0.001; 95% CI, 1.5647–1.6781). The ORs of the coded GCS scores of 3, 2, 1, and 0 were 8.56 (p < 0.001; 95% CI, 5.1683–14.1706), 22.77 (p < 0.001; 95% CI, 13.5975–38.1538), 60.87 (p < 0.001; 95% CI, 34.4921–107.4113), and 757.67(p < 0.001; 95% CI, 402.4326–1426.4840), respectively. These results were included to show which parameter was superior. After further analysis, we eventually chose to use the actual GCS score because it contributed to better calibration of the final model. While the conclusions were drawn in the final step of the analysis, we dropped the coded GCS value in next subsection.

SpO2 vs. respiratory rate

The GCS score, SBP, RR and SpO2 were entered into the univariate logistic regression as continuous variables, and all of them showed significance. To evaluate the predictive ability of the multivariate model, RR and SpO2 was entered separately with GCS and SBP. The adjusted ORs of RR and SpO2 were 1.01 (p = 0. 974; 95% CI, 0.9648–1.0378) and 1.07 (p < 0.001; 95% CI, 1.0372–1.0956), respectively (Table 3).
Table 3

Univariate and multivariate analysis for Glasgow Coma Scale score, systolic blood pressure, respiratory rate, and oxygen saturation

 

Univariate

Multivariate

Multivariate

OR

P value

95% CI

OR

P value

95% CI

OR

P value

95% CI

GCS

1.62

<0.001

1.5647–1.6781

1.57

<0.001

1.5132–1.6326

1.52

<0.001

1.4597–1.5766

SBP

1.03

<0.001

1.0289–1.0352

1.01

<0.001

1.0090–1.0183

1.01

0.008

1.0018–1.0120

RR

1.25

<0.001

1.2218–1.2850

1.00

0.974

0.9648–1.0378

-

-

-

SpO2

1.16

<0.001

1.1340–1.1858

-

-

-

1.07

<0.001

1.0372–1.0956

OR odds ratio; CI confidence interval; GCS Glasgow Coma Scale; SBP systolic blood pressure; RR respiratory rate; SpO 2 peripheral oxygen saturation

Multivariate analysis of SBPNTS, SpO2NTS, and actual GCS vs. SBPNTS, SpO2NTS, and coded GCS value

SBPNTS, SpO2NTS, and the actual GCS reached statistical significance in the multivariate logistic regression. The global predictive performance exhibited good discrimination (AUC = 0.935, p < 0.001; 95% CI, 0.9174–0.9526) and calibration (H-L χ 2 = 6.303, p = 0.178; Table 4). SBPNTS, SpO2NTS, and the coded value of GCS also showed significance but with less calibration than our final model (H-L χ 2 = 18.404, p = 0.001). The following equation was used to calculate Ps: Ps = 1 / (1 + e-b), b = −6.5406 + (0.4006 x GCS) + (0.2983 x SBPNTS) + (0.8709 x SpO2NTS). The final NTS model ranged from 1.2019 to 10.6867 and was calculated as follows:
Table 4

Results of logistic regression of GCS, SBPNTS, and SpO2NTS

 

β

SE

p

95% CI

Constant

- 6.5406

0.5805

0.000

−7.6786

−5.4027

GCS

0.4006

0.0196

0.000

0.3622

0.4391

SBPNTS

0.2983

0.0754

0.000

0.1504

0.4461

SpO2NTS

0.8709

0.1580

0.000

0.5611

1.1808

AUC = 0.935, Hosmer-Lemeshow χ2 = 6.303 (p = 0.178)

NTS = (0.4006 x GCS) + (0.2983 x SBPNTS) + (0.8709 x SpO2NTS).

The NTS vs. the RTS, the MGAP, the GAP

The NTS showed better discrimination than the RTS (AUC = 0.935 vs. 0.917, respectively, AUC difference = 0.018, p = 0.001; 95% CI, 0. 0071–0. 0293) and a slightly lower AUC than the MGAP (AUC = 0.935 vs. 0.938, respectively, AUC difference = 0.003, p = 0.713; 95% CI, −0. 0114–0. 0166) and the GAP (AUC = 0.935 vs. 0.941, respectively, AUC difference = 0.006, p = 0.423; 95% CI, −0. 0080–0. 0190). In the validation cohort, the NTS showed better discrimination than the RTS, MGAP, and GAP (Table 5).
Table 5

Predictive performance of the NTS compared with the RTS, MGAP, and GAP in derivation and validation cohort

Score

Derivation cohort

Validation cohort

AUC

AUC difference

p value

95% CI

AUC

AUC difference

p value

95% CI

NTS

0.935

    

0.919

    

RTS

0.917

0.018

0.001

0.0071

0.0293

0.906

0.013

0.015

0.0009

0.0249

MGAP

0.938

0.003

0.713

−0.0114

0.0166

0.907

0.012

0.096

−0.0039

0.0271

GAP

0.941

0.006

0.423

−0.0080

0.0190

0.912

0.007

0.399

−0.0090

0.0224

Observed mortality rate according to the NTS for triage (T-NTS) in complete data

The T-NTS is calculated using the formula T-NTS = GCS + SBPNTS + SpO2NTS. It ranges from 3 to 23. For triage, a T-NTS of 18, for which the sensitivity and specificity were 95% and 82%, respectively, was chosen to transfer patients to the trauma center. We categorized the patients into four groups: low (T-NTS 18–23), intermediate (T-NTS 12–17), high (T-NTS 6–11) and very high (T-NTS 3–5) risk for death. The observed mortality rates of the derivation cohort in each defined stratum were visualized along with those of the validation cohort in Fig. 3. The specificities of the RTS, the MGAP, and GAP were 80% (cutoff, RTS < 7.0), 80% (cutoff, MGAP score < 20), and 82% (cutoff, GAP score < 17), respectively.
Fig. 3

Observed mortality of low, intermediate, high, and very high risk groups categorized by New Trauma Score for triage in the derivation and validation cohorts

Discussion

The aim of our study was to develop a new trauma scoring system based on initial patient physiological data, the GCS score, SBP and SpO2. We found that the NTS significantly outperformed the RTS in mortality prediction of trauma patients but did not exceed the MGAP and GAP. At the fixed rate of 5% undertriage (sensitivity 95%), the NTS showed overtriage rate equal to or slightly lower than those of the RTS, MGAP and GAP in our study population. In the trauma setting, a high level of sensitivity rather than specificity for transferring patients to a specialized trauma center is essential to avoid patient deaths due to suboptimal care [8]. Increasing evidence shows that the overtriage of trauma patients leads to large wastes of socioeconomic and medical resources [26]. Lowering the overtriage rate with assurance of a convincing undertriage rate is important. According to our results, the specificity of the NTS at 95% sensitivity was over 82%, which is higher than RTS and MGAP and slightly lower than the GAP.

The MGAP and GAP may be superior to the NTS from some perspectives. For example, the components of the MGAP and GAP (mechanism, age, the GCS, and SBP) are immediately available at presentation, whereas the NTS needs SpO2. However, SpO2 measurement is not time-consuming or expensive in a modern trauma care system. In our study, the AUCs of the MGAP and GAP were larger than that of the NTS in the derivation cohort but were within the scope of statistical error. In the validation cohort, the AUCs of the MGAP and GAP were smaller than that of the NTS but were also within the range of statistical error. Despite the inconclusive results, the superiority of the NTS over the MGAP and GAP was to be expected; the MGAP and GAP already include age and mechanism, whereas the NTS comprises the same element used in the RTS. For this reason, only the NTS can be incorporated into the TRISS, which is the most widely used mortality prediction model for trauma patients. To date, no trauma scoring system has shown better performance than the TRISS. In addition to its use as a triage tool in clinical practice, the NTS may play an important role in trauma research due to its potential applicability as a substitute for the RTS in the TRISS.

We chose to use the actual GCS score rather than the coded GCS, which was initially proposed by Champion et al., in our prediction model. There is no firm evidence of an advantage of the GCS compared with the coded GCS. Among the severity scoring systems used for patients in the intensive care unit, the Acute Physiology and Chronic Health Evaluation (APACHE) uses the actual GCS [27], whereas the Simplified Acute Physiology Score (SAPS) and the Sequential Organ Failure Assessment (SOFA) use the coded GCS value [28, 29]. Most recently developed trauma scoring systems adopt the actual GCS, including the MGAP, GAP, Emergency Trauma Score (EMTRAS), BIG score (composed of base deficit, international normalized ratio, and the GCS), the UK Trauma Audit & Research Network prediction model, and the corticosteroid randomization after significant head injury (CRASH) model [5, 6, 3032]. In our study, the actual GCS showed better calibration of the final model compared with the coded GCS.

The association between SBP and mortality in trauma patients has been assessed in many previous studies. Recent studies have recommended that the SBP threshold be increased up to 110 mmHg. Several authors have reported that elevated blood pressure was related to poor outcomes in traumatic brain injury (TBI). Additionally, Zafar et al. and Fuller et al. specifically mentioned that mortality in TBI showed a bimodal distribution [3336]. In the general trauma population, the association between hypertension and mortality is not clearly known. However, during the initial period of trauma care, whether a given patient has TBI is not certain. To the best of our knowledge, there has been no study regarding this issue. In our population, hypertensive patients (SBP ≥ 150 mmHg) showed higher mortality than normotensive patients (SBP ≥ 110 mmHg; Fig 1c), and we believe this distribution might be caused by the relatively high proportion of head injury in the study patients (32.4% of the derivation cohort) (Table 2).

Only one study has assessed the possibility of replacing RR with SpO2 in the RTS (observational cohort study, prehospital setting, n = 1481) [37]. The authors concluded that RR and SpO2 do not add significant value to other variables in the RTS and TRISS. However, they said the power of the study might be insufficient to detect significance because of abundant missing data (approximately 35% of RR and SpO2). Although it was not advocated in the main conclusion of the study, SpO2 showed larger AUC for mortality compared with the RR (AUC, 0.747 vs. 0.691), and the SpO2 was more strongly correlated mortality than the RR was. Our study consistently showed that SpO2 is a better parameter than RR. A major concern regarding the use of SpO2 is non-measurability. We could not determine whether missing SpO2 values were caused by non-measurability or were simply missing in our patients. In terms of the retrospective analysis, we believe that this problem was not serious since we imputed the missing SpO2 on significant variables, including the ISS and outcome (in-hospital mortality). However, in clinical practice, non-measurable SpO2 leads to a failure to gain the final score. Most non-measurable SpO2 in trauma patients is associated with extremely low oxygenation or poor peripheral circulation caused by profound hemorrhagic shock, tension pneumothorax, cardiac tamponade, or cardiac arrest. Therefore, non-measurable SpO2 could be counted as zero (the lowest code value of the NTS) considering the patients’ symptoms, clinical appearance, and other physiological parameters.

Our study has some limitations. Of greatest concern is the selection bias. First, patients were transferred to our hospital from a relatively small area of approximately 60 km in radius. Our hospital is the only tertiary care center for major trauma in this area. Second, this was a single-center study, and therefore generalization could be erroneous. Accordingly, external validation in different populations and countries should be performed. Third, the target population was restricted to adults. Therefore, this score may not be useful for patients under 15 years of age. Because pediatric patients have unique physiological characteristics, separate studies are needed for the development and application of a trauma scoring system for the pediatric population.

Conclusion

The NTS predicts in-hospital mortality substantially better than the RTS and not inferior to the MGAP and GAP. We hope that the NTS will be a useful tool for triage in trauma patients and will lead to an improvement in trauma management.

Abbreviations

AIS: 

Abbreviated Injury Scales

AUC: 

areas under the receiver operating characteristic curve

CI: 

confidence interval

ED: 

emergency department

GAP : 

GCS, Age and Systolic Arterial Pressure

GCS: 

Glasgow Coma Scale

H-L : 

Hosmer-Lemeshow

ISS : 

Injury Severity Score

MGAP : 

Mechanism, GCS, and Age and Arterial Pressure

NTS: 

New Trauma Score

OR: 

odds ratio

Ps : 

predictive survival

ROC: 

receiver operating characteristic

RR: 

respiratory rate

RTS: 

Revised Trauma Score

SBP : 

systolic blood pressure

SBPNTS

code value of systolic blood pressure

SPo2

peripheral oxygen saturation

SPo2NTS

code value of peripheral oxygen saturation

T-NTS: 

New Trauma Score for Triage

TRISS: 

Trauma and Injury Severity Score

Declarations

Acknowledgements

None.

Funding

None.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

DHK, SHL and SCK designed the study. JHJ, YJP, and DHK wrote the manuscript. TYK, DL and CK extracted the data. JHJ, SBL and YJP conducted the statistical analysis. All authors were involved in drafting and critically revised the manuscript. All authors approved the final version.

Ethics approval and consent to participate

This study was approved by the Gyeongsang National University hospital institutional review board (IRB file number: GNUH 2016–09-008).

The trauma registry was originally developed as a part of Emergency Department-based Injury In-depth Surveillance conducted by the Korea Centers for Disease Control and Prevention. Informed consent was not needed because the data were collected without identifiable personal information.

Consent for publication

Not applicable.

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.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Emergency Medicine, Gyeongsang National University School of Medicine
(2)
Department of Emergency Medicine, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital
(3)
Institute of Health Sciences, Gyeongsang National University

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.View ArticlePubMedGoogle Scholar
  2. Boyd CR, Tolson MA, Copes WS. Evaluating trauma care: the TRISS method. Trauma score and the injury severity score. J Trauma. 1987;27:370–8.View ArticlePubMedGoogle Scholar
  3. Joosse P, Soedarmo S, Luitse JS, Ponsen KJ. Trauma outcome analysis of a Jakarta University hospital using the TRISS method: validation and limitation in comparison with the major trauma outcome study. Trauma and injury severity score. J Trauma. 2001;51:134–40.View ArticlePubMedGoogle Scholar
  4. Hannan EL, Mendeloff J, Farrell LS, Cayten CG, Murphy JG. Validation of TRISS and ASCOT using a non-MTOS trauma registry. J Trauma. 1995;38:83–8.View ArticlePubMedGoogle Scholar
  5. Sartorius D, Le Manach Y, David JS, Rancurel E, Smail N, Thicoipe 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;38:831–7.View ArticlePubMedGoogle Scholar
  6. Kondo Y, Abe T, Kohshi K, Tokuda Y, Cook EF, Kukita I. Revised trauma scoring system to predict in-hospital mortality in the emergency department: Glasgow Coma scale, age, and systolic blood pressure score. Crit Care. 2011;15:R191.View ArticlePubMedPubMed CentralGoogle Scholar
  7. 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.View ArticlePubMedGoogle Scholar
  8. American College of Surgeons Committee on Trauma. Resources for optimal care of the injured patient. Chicago: American College of Surgeons Committee on Trauma; 2014.Google Scholar
  9. Sasser SM, Hunt RC, Faul M, Sugerman D, Pearson WS, Dulski T, et al. Guidelines for field triage of injured patients: recommendations of the National Expert Panel on field triage, 2011. MMWR Recomm Rep. 2012;61:1–20.PubMedGoogle Scholar
  10. Edelman DA, White MT, Tyburski JG, Wilson RF. Post-traumatic hypotension: should systolic blood pressure of 90-109 mmHg be included? Shock. 2007;27:134–8.View ArticlePubMedGoogle Scholar
  11. Hasler RM, Nuesch E, Juni P, Bouamra O, Exadaktylos AK, Lecky F. Systolic blood pressure below 110 mm hg is associated with increased mortality in blunt major trauma patients: multicentre cohort study. Resuscitation. 2011;82:1202–7.View ArticlePubMedGoogle Scholar
  12. Perry A, Potter P, Ostendorf W. Nursing Interventions & Clinical Skills. 6th ed. St. Louis: Elsevier; 2016.Google Scholar
  13. Simoes EA, Roark R, Berman S, Esler LL, Murphy J. Respiratory rate: measurement of variability over time and accuracy at different counting periods. Arch Dis Child. 1991;66:1199–203.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Lovett PB, Buchwald JM, Sturmann K, Bijur P. The vexatious vital: neither clinical measurements by nurses nor an electronic monitor provides accurate measurements of respiratory rate in triage. Ann Emerg Med. 2005;45:68–76.View ArticlePubMedGoogle Scholar
  15. Philip KE, Pack E, Cambiano V, Rollmann H, Weil S, O'Beirne J. The accuracy of respiratory rate assessment by doctors in a London teaching hospital: a cross-sectional study. J Clin Monit Comput. 2015;29:455–60.View ArticlePubMedGoogle Scholar
  16. Yelderman M, New W Jr. Evaluation of pulse oximetry. Anesthesiology. 1983;59:349–52.View ArticlePubMedGoogle Scholar
  17. Hannhart B, Haberer JP, Saunier C, Laxenaire MC. Accuracy and precision of fourteen pulse oximeters. Eur Respir J. 1991;4:115–9.PubMedGoogle Scholar
  18. Severinghaus JW, Naifeh KH, Koh SO. Errors in 14 pulse oximeters during profound hypoxia. J Clin Monit. 1989;5:72–81.View ArticlePubMedGoogle Scholar
  19. Falconer RJ, Robinson BJ. Comparison of pulse oximeters: accuracy at low arterial pressure in volunteers. Br J Anaesth. 1990;65:552–7.View ArticlePubMedGoogle Scholar
  20. Jay GD, Hughes L, Renzi FP. Pulse oximetry is accurate in acute anemia from hemorrhage. Ann Emerg Med. 1994;24:32–5.View ArticlePubMedGoogle Scholar
  21. Mower WR, Sachs C, Nicklin EL, Safa P, Baraff LJ. A comparison of pulse oximetry and respiratory rate in patient screening. Respir Med. 1996;90:593–9.View ArticlePubMedGoogle Scholar
  22. Woodford MR, Mackenzie CF, DuBose J, Hu P, Kufera J, Hu EZ, et al. Continuously recorded oxygen saturation and heart rate during prehospital transport outperform initial measurement in prediction of mortality after trauma. J Trauma Acute Care Surg. 2012;72:1006–11.View ArticlePubMedGoogle Scholar
  23. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30:377–99.View ArticlePubMedGoogle Scholar
  24. Graham JW, Olchowski AE, Gilreath TD. How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prev Sci. 2007;8:206–13.View ArticlePubMedGoogle Scholar
  25. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.View ArticlePubMedGoogle Scholar
  26. Newgard CD, Staudenmayer K, Hsia RY, Mann NC, Bulger EM, Holmes JF, et al. The cost of overtriage: more than one-third of low-risk injured patients were taken to major trauma centers. Health Aff. 2013;32:1591–9.View ArticleGoogle Scholar
  27. Knaus WA, Zimmerman JE, Wagner DP, Draper EA, Lawrence DE. APACHE — acute physiology and chronic health evaluation: a physiologically based classification system. Crit Care Med. 1981;9:591–7.View ArticlePubMedGoogle Scholar
  28. Le Gall J-R, Loirat P, Alperovitch A, Glaser P, Granthil C, Mathieu D, et al. A simplified acute physiology score for ICU patients. Crit Care Med. 1984;12:975–7.View ArticlePubMedGoogle Scholar
  29. Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al. The SOFA (sepsis-related organ failure assessment) score to describe organ dysfunction/failure. On behalf of the working group on sepsis-related problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22:707–10.View ArticlePubMedGoogle Scholar
  30. Raum MR, Nijsten MWN, Vogelzang M, Schuring F, Lefering R, Bouillon B, et al. Emergency trauma score: an instrument for early estimation of trauma severity. Crit Care Med. 2009;17:1972–7.View ArticleGoogle Scholar
  31. Borgman M, Maegele M, Wade CE, Blackbourne LH, Spinella PC. Pediatric trauma BIG score: predicting mortality in children after military and civilian trauma. Pediatrics. 2011;17:e892–7.View ArticleGoogle Scholar
  32. Perel P, Arango M, Clayton T, Edwards P, Komolafe E, Poccock S, et al. MRC CRASH trial collaborators. Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ. 2008;336:425–9.View ArticlePubMedGoogle Scholar
  33. Butcher I, Maas AI, Lu J, Marmarou A, Murray GD, Mushkudiani NA, et al. Prognostic value of admission blood pressure in traumatic brain injury: results from the IMPACT study. J Neurotrauma. 2007;24:294–302.View ArticlePubMedGoogle Scholar
  34. Ley EJ, Singer MB, Clond MA, Gangi A, Mirocha J, Bukur M, et al. Elevated admission systolic blood pressure after blunt trauma predicts delayed pneumonia and mortality. J Trauma. 2011;71:1689–93.PubMedGoogle Scholar
  35. Zafar SN, Millham FH, Chang Y, Fikry K, Alam HB, King DR, et al. Presenting blood pressure in traumatic brain injury: a bimodal distribution of death. J Trauma. 2011;71:1179–84.View ArticlePubMedGoogle Scholar
  36. Fuller G, Hasler RM, Mealing N, Lawrence T, Woodford M, Juni P, et al. The association between admission systolic blood pressure and mortality in significant traumatic brain injury: a multi-centre cohort study. Injury. 2014;45:612–7.View ArticlePubMedGoogle Scholar
  37. Raux M, Thicoipe M, Wiel E, Rancurel E, Savary D, David JS, et al. Comparison of respiratory rate and peripheral oxygen saturation to assess severity in trauma patients. Intensive Care Med. 2006;32:405–12.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2017