Archives of Academic Emergency Medicine. 2022; 10(1): e25

OR I G I N A L RE S E A RC H

Glasgow Coma Scale Versus Physiologic Scoring Systems
in Predicting the Outcome of ICU admitted Trauma Pa-
tients; a Diagnostic Accuracy Study
Sorour Khari1, Mitra Zandi2∗, Mahmoud Yousefifard3 †

1. Student Research Committee, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

2. School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

3. Physiology Research Center, Iran University of Medical Sciences, Tehran, Iran.

Received: February 2022; Accepted: March 2022; Published online: 9 April 2022

Abstract: Introduction: There is no consensus on the performance of decision rules in predicting the prognosis of trauma
patients. Therefore, the present study aimed to compare the value of Glasgow coma scale (GCS) and physiologic
scoring systems in predicting mortality and poor outcome of trauma patients. Methods: This diagnostic accu-
racy study was conducted on multiple trauma patients admitted to the intensive care units of two hospitals in
Tehran, Iran, from 21 November 2020 to 22 May 2021. The patients’ demographic characteristics, length of stay
in the intensive care unit (ICU), the vital signs, and the GCS on admission were recorded. Finally, the mortal-
ity, disability, and complete recovery of patients at the time of discharge were evaluated and receiver operating
characteristics (ROC) curve analysis was used to compare the performance of physiologic scoring systems with
GCS. Results: 200 trauma patients with the mean age of 43.53±19.84 years were evaluated (74% male). The area
under the ROC curve for New Trauma Score (NTS), Revised Trauma Score (RTS), Worthing Physiological Scor-
ing System (WPSS), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), Modified
Early Warning Score (MEWS), National Early Warning Score (NEWS), Glasgow Coma Scale, Age, and Systolic
Blood Pressure score (GAPS) ,Glasgow coma scale (GCS) in prediction of mortality were 0.95, 0.95, 0.83, 0.89,
0.91, 0.84, 0.77, 0.97, and 0.98 respectively. The performance of GCS was statistically superior to RTS (P=0.005),
WPSS (P=0.0001), RAPS (P=0.0002), REMS (P=0.002), MEWS (P<0.0001), and NEWS (P<0.0001). However, the
performance of GCS, NTS (P=0.146), and GAPS (P=0.513) were not significantly different. Also, in prediction
of poor outcomes, the AUC of GCS (0.98) was significantly higher than RTS (0.95), RAPS (0.85), REMS (0.85),
MEWS (0.84), NEWS (0.77), and WPSS (0.75). Conclusion: The GCS score seems to be a better instrument to
predict mortality and poor outcome in trauma patients compared to other tools due to its high accuracy, wide
application, and easy calculation.

Keywords: Wounds and Injuries; Clinical Decision Rules; Patient outcome assessment; Glasgow coma scale; Intensive care
units

Cite this article as: Khari S, Zandi M, Yousefifard M. Glasgow Coma Scale Versus Physiologic Scoring Systems in Predicting

the Outcome of ICU admitted Trauma Patients; a Diagnostic Accuracy Study. Arch Acad Emerg Med. 2022; 10(1): e25.

https://doi.org/10.22037/aaem.v10i1.1483.

∗Corresponding Author: Mitra Zandi; School of Nursing and Midwifery,
Shahid Beheshti University of Medical Sciences, Tehran, Iran.E-mail: mi-
tra.zandi@yahoo.com, Tel: +98-21-88202511, ORCID: http://orcid.org/0000-
0002-7395-0280.
† Corresponding Author: Mahmoud Yousefifard; Physiology Research Center,
Iran University of Medical Sciences, Hemmat Highway, P.O Box: 14665-354,
Tehran, Iran. Email: yousefifard.m@iums.ac.ir / yousefifard20@gmail.com,
Phone/Fax: +982186704771, ORCID: http://orcid.org/0000-0001-5181-4985.

1. Introduction

Trauma is one of the most well-known external injuries

that remain a worldwide public health concern. Accord-

ing to statistics, 16% of the global burden of diseases is

related to injuries, and approximately 4.5 million deaths

are caused by traumatic injuries annually (1, 2). Further-

more, lower-middle-income countries sustain 90% of injury-

related deaths (2). Based on the WHO reports, the common

causes of traumas are traffic accidents, falling from a height,

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S. Khari et al. 2

occupational injuries, and personal accidents. Road traffic

accidents will be the fifth major cause of death globally by

2030 (3, 4). Traumatic injuries are the first reason for los-

ing years of potential life and one of the four leading causes

of mortality in developing countries (5). The consequences

of traumatic injuries are affected by the trauma severity, the

physiological reserve, the on-time revival, and appropriate

treatment (6).

Patients with severe trauma need hospitalization in the In-

tensive Care Unit (ICU). Trauma-related death in patients ad-

mitted to the ICU is caused by severe brain injury and mul-

tiple organ failure (7). Management, timely post-traumatic

care, and creating specific care systems at trauma centers are

vital for reducing the mortality rate, disability risk, and long-

term pain in traumatic patients (8, 9). With the increase in

health care costs and the shortage of beds in intensive care

units, patients should be appropriately triaged to avoid un-

necessary costs and the use of beds (10).

In recent years, several scoring systems have been designed

to assess injury severity and determine which patients need

observation, treatment, and allocation of health care re-

sources (11, 12). Although improvements have been made

to multiple scoring systems, each system still has its limi-

tations and shortcomings, including many variables in the

model, failure to evaluate them in different clinical settings,

and complex calculations required to conclude (11, 13). The

physiological scoring systems are helpful for treatment staff

to recognize the severity of trauma and decide the period

of trauma management (9). In all of these scoring sys-

tems, in addition to the level of consciousness, physiologi-

cal criteria such as respiration rate, body temperature, heart

rate, and blood pressure are used to determine the sever-

ity of trauma injury (14). New Trauma Score (NTS), Re-

vised Trauma Score (RTS), Worthing Physiological Scoring

System (WPSS), Rapid Acute Physiology Score (RAPS), Rapid

Emergency Medicine Score (REMS), Modified Early Warning

Score (MEWS), National Early Warning Score (NEWS), Glas-

gow Coma Scale, Age, and Systolic Blood Pressure (GAPS) are

some of these scoring systems. Glasgow coma scale (GCS) is a

valuable, rapid, and accurate method to determine patients’

injury severity, consciousness level, and outcome, especially

in those with a head injury, and has remained an important

method for assessing critically injured patients in the Middle

East region and Iran (5-9, 14, 15).

There are conflicting results from comparing GCS with scor-

ing systems in predicting patient outcomes. The existence of

contradictions indicates the need for more studies. Accord-

ingly, this study was designed to compare the performance

values of eight physiologic scoring systems including NTS,

RTS, WPSS, RAPS, REMS, MEWS, NEWS, and GAPS, with GCS

in predicting poor outcome and mortality of trauma patients

admitted in ICU.

2. Methods

2.1. Study design and setting

This prospective diagnostic accuracy study was carried out

between 21 November 2020 and 22 May 2021 at ICUs of

two hospitals in Tehran, Iran. The present study obtained

ethical approval from Shahid Beheshti University of Medi-

cal Sciences (IR.SBMU.PHARMACY.REC.1399.243). The re-

searchers adhered to the declaration of Helsinki regarding

the ethical issues and confidentiality of patients’ informa-

tion.

2.2. Participants

The population study included 200 trauma patients admit-

ted to the intensive care units (ICUs). The researcher selected

patients using the convenience sampling method. The inclu-

sion criteria were admission to ICU due to traumatic injuries

and age over 18 years. The exclusion criteria included preg-

nancy and transferring patients to other centers.

2.3. Data gathering

The researcher filled out the pre-prepared checklist in each

hospital on admission, including age, gender, trauma mech-

anism, co-morbidities, vital signs on admission, Alert, Voice,

Pain, Unresponsive (AVPU) scale, and Glasgow Coma Scale.

Vital signs for each patient were as follows: heart rate, res-

piratory rate, temperature, systolic blood pressure, diastolic

blood pressure, mean arterial pressure, oxygen saturation.

The variables such as age, mean arterial pressure, heart rate,

respiratory rate, temperature, and oxygen saturation were

used to evaluate the eight values of physiologic models. The

Glasgow Coma Scale (GCS) is the sum of the three tests of

the patient’s eye-opening, verbal, and motor responses with

a minimum score of 3 and a maximum of 15 (16).

The National Early Warning Score (NEWS) is based on seven

simple physiological variables (systolic blood pressure, body

temperature, respiration rate, oxygen saturation, heart rate,

level of consciousness, and supplemental oxygen). The scor-

ing is from 0-20 (17).

The Modified Early Warning Score (MEWS) includes five vari-

ables: systolic blood pressure, heart rate, body temperature,

respiration rate, and level of consciousness. The score ranges

from 0 to a maximum of 14 (12).

The Rapid Emergency Medicine Score (REMS) is determined

using age and five physiological variables including heart

rate, mean blood pressure, respiration rate, oxygen satura-

tion, and Glasgow Coma Scale. The highest score is 26 (12).

The Rapid Acute Physiology Score (RAPS) consists of heart

rate, respiration rate, blood pressure, and Glasgow Coma

Scale. Its scoring range is 0 (normal) to 16 (acute) (13, 18).

The Worthing Physiological Scoring System (WPSS) consists

of six parameters: systolic blood pressure, heart rate, respira-

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3 Archives of Academic Emergency Medicine. 2022; 10(1): e25

Table 1: Comparing the baseline characteristics of included patients based on their survival status and outcome

Variables
Survival

P
Outcome

P
Yes (n=163) No (n=37) Good(n=125) Poor(n=75)

Age (year) 41.28±18.72 53.40±21.80 <0.0001 41.33±18.59 47.18±21.39 0.043
Gender
Male 120 (73.6) 28 (75.7) 0.797 91 (72.8) 57 (76.0) 0.617
Female 43 (26.4) 9 (24.3) 34 (27.2) 18 (24.0)
Trauma mechanism
Motorcycle 49 (30.1) 10 (27.0) 36 (28.8) 23 (30.7)
Car accident 35 (21.5) 8 (21.6) 27 (21.6) 16 (21.3)
Bicycle 5 (3.1) 0 (0.0) 0.766 5 (4.0) 0 (0.0) 0.341
Pedestrian 27 (26.5) 4 (10.8) 23 (18.4) 8 (10.7)
Fall >3m 6 (16.2) 20 (12.3) 13 (10.4) 13 (17.3)
Fall <3m 27 (16.6) 9 (24.3) 21 (16.8) 15 (20.0)
Co-morbidities
Hypertension 44 (27.0) 14 (37.8) 0.189 34 (27.2) 24 (32.0) 0.619
Diabetes 34 (20.9) 7 (18.9) 0.792 27 (21.6) 14 (18.7) 0.469
CVD 8 (21.6) 25 (12.5) 0.063 15 (12.0) 10 (13.3) 0.783
PD 25 (15.3) 8 (21.6) 0.353 19 (15.2) 14 (18.7) 0.523
Other 19 (11.7) 5 (13.5) 0.754 16 (12.8) 8 (10.7) 0.653
Glasgow coma scale
3-8 24 (14.7) 37 (100.0) 1 (0.8) 60 (80.0)
9-12 38 (23.0) 0 (0.0) <0.0001 24 (19.2) 14 (18.7) <0.0001
13-15 101 (62.0) 0 (0.0) 100 (80.0) 1 (1.3)
Vital signs on admission
HR (/min) 99.46±21.34 86.40±28.95 <0.0001 99.05±21.04 93.70±26.72 0.118
RR (/ min) 19.20±4.37 17.67±4.03 0.052 19.49±4.39 17.97±4.12 0.016
T (°C) 36.71±2.43 36.82±0.70 0.781 36.69±2.76 36.79±0.63 0.749
SBP (mmHg) 124.09±22.50 105.32±26.88 <0.0001 125.76±21.18 112.05±27.07 <0.0001
DBP (mmHg) 78.86±17.71 64.05±14.18 <0.0001 79.68±17.38 70.2±17.63 <0.0001
MAP (mmHg) 91.46±19.40 77.11±17.93 <0.0001 92.73±19.60 82.25±18.75 <0.0001
SaO2 (%) 96.20±7.17 95.32±4.75 0.479 95.90±8.07 96.26±3.81 0.715
Length of stay in ICU (days)
Mean ± SD 5.77±5.30 7.56±7.30 0.086 4.59±3.14 8.62±7.87 <0.0001
Data are presented as mean ± standard deviation (SD) or number (%). These data were evaluated at the time of admission to
intensive care unit (ICU). The outcome variables were patient survival status (survived, died), good outcome (complete recovery),
and poor outcome (mortality, disability). CVD: cardiovascular disease; PD: pulmonary disease; HR: heart rate; RR: respiratoty rate;
t: temperature; SBP: systolic blood pressure; DBP: diastolic blood pressure; MAP: mean arterial pressure; SaO2: saturation O2.

tion, body temperature, level of consciousness, and oxygen

saturation. Its maximum score is 14 (19, 20).

The Revised Trauma Score (RTS) includes the systolic blood

pressure, Glasgow Coma Scale, and respiration. The final

score of this tool is in the range of 0-12 (21).

The New Trauma Score (NTS) system is a new physiological

scoring tool, it is a modified version of the Revised Trauma

Score (RTS). This scoring system includes physiological vari-

ables (Glasgow coma scale, systolic blood pressure, and oxy-

gen saturation level). The scoring method is that the Glasgow

Coma Scale (GCS) score is added to the scores of the other

two parameters, and the total score ranges from 3 to 23 (22).

The Glasgow Coma Scale, Age, and Systolic Blood Pressure

score (GAPS) is a physiological scoring system with a small

number of parameters: Glasgow Coma Scale, blood pressure,

and age. Its score varies from 3-24 (23).

2.4. Outcomes

The researcher recorded the patient’s status at the time of dis-

charge from ICU as an outcome assessment. The outcome

variables were patient survival status (survived, died), good

outcome (complete recovery), and poor outcome (mortality,

disability).

2.5. Statistical analysis

We assessed the normality assumption of data based on

the histograms and Kolmogorov–Smirnov test. Descriptive

statistics were means ± SDs for continuous variables and fre-

quency (percentage) for categorical variables. The indepen-

dent sample t-test and Fisher’s exact test were conducted to

compare the variables between survivors and non-survivors.

Then, the receiver operating characteristic (ROC) curve anal-

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S. Khari et al. 4

Table 2: Performance of physiologic scoring systems and Glasgow coma scale in prediction of mortality in intensive care unit (ICU) admitted

trauma patients

Score CP TP TN FP FN Sensitivity Specificity PPV NPV PLR NLR
GCS 9 37 133 30 0 100(90.5-100) 81.6(74.8-7.2) 55.2(42.6-67.4) 100(97.3-100) 5.43(3.9-7.5) 0
GAPS 18 35 147 16 2 94.6(81.8-99.3) 90.2(84.5-94.3) 68.6(54.1-80.9) 98.7(95.2-99.8) 9.64(6.01-15.4) 0.06(0.02-0.2)
NTS 14 36 145 18 1 97.3(85.8-99.9) 89.0(83.1-93.3) 66.7(52.5-78.9) 99.3(96.2-100) 8.81(5.7-13.7) 0.03(0.0-0.2)
RTS 7 37 131 32 0 100(90.5-100) 80.4(73.4-86.2) 53.6(41.2-65.7) 100(97.2-100) 5.09(3.7-6.9) 0
MEWS 4 35 84 79 2 94.6(81.8-99.3) 51.5(43.6-59.4) 30.7(22.4-40.0) 97.7(91.9-99.7) 1.95(1.6-2.3) 0.10(0.03-0.4)
NEWS 5 36 48 115 1 97.3(85.8-99.9) 29.4(22.6-37.1) 23.8(17.3-31.4) 98.0(89.1-99.9) 1.38(1.2-1.5) 0.09(0.01-0.6)
WPSS 4 31 100 63 6 83.8(68.0-93.8) 61.3(53.4-68.9) 33.0(23.6-43.4) 94.3(88.1-97.9) 2.17(1.7-2.7) 0.26(0.1-0.5)
REMS 6 31 132 31 6 83.8(68.0-93.8) 81.0(74.1-86.7) 50.0(37.0-63.0) 95.7(90.8-98.4) 4.41(3.1-6.2) 0.20(0.10-0.4)
RAPS 4 32 123 40 5 86.5(71.2-95.5) 75.5(68.1-81.9) 44.4(32.7-56.6) 96.1(91.1-98.7) 3.52(2.6-4.7) 0.18(0.08-0.4)
Data are presented with 95% confidence interval. CP: Cut off point; TP: True positive; TN: True negative; FP: False positive;
FN: False negative; PPV: Positive predictive value; NPV: Negative predictive value; PLR: Positive likelihood ratio;
NLR: Negative likelihood ratio; GCS: Glasgow coma scale; GAPS: Glasgow Coma Scale, Age, and Systolic Blood Pressure score;
NTS: New trauma score; RTS: Revised trauma score; MEWS: Modified Early Warning Score; NEWS: National Early Warning Score;
WPSS: Worthing physiological scoring system; REMS: Rapid emergency medicine score; RAPS: Rapid acute physiology score.

Figure 1: Comparison of area under the receiver characteristics curve (AUC) between assessed scoring systems in prediction of intensive care

unit (ICU) mortality. A) Demonstration of the prediction rules with excellent performance (AUC >0.90); B) Demonstration of the prediction

rules with good performance (AUC between 0.70 and 0.90).

ysis was used to estimate the sensitivity, specificity, positive

predictive value (PPV ), negative predictive value (NPV ), pos-

itive likelihood ratio (+LR), and negative likelihood ratio (-

LR) for each of GCS, NTS, RTS, WPSS, RAPS, REMS, MEWS,

NEWS, and GAPS models. Finally, the area under the curves

(AUCs) of all eight models were compared with GCS. The dif-

ferences were considered statistically significant at p values

< 0.05. Analyses were performed using STATA 14.0 software.

The best cut-off point in each scoring system was determined

using the Youden index and similar studies (20, 24-26) .

3. Results

3.1. Baseline characteristics of studied cases

A total of 200 trauma patients with the mean age of

43.53±19.84 years were included in the study (74% male). The

percentage of the non-survivors among trauma patients in

ICU was 18.5% (n=37). The most common trauma mecha-

nisms were motorcycle accidents (29.5%) and car accidents

(21.5%). 29% of the patients had hypertension. The av-

erage length of stay in ICU was 6.10±5.75 days. The sig-

nificantly different vital signs between the two groups (sur-

vivors, non-survivors) were heart rate, systolic blood pres-

sure, diastolic blood pressure, and mean arterial pressure

(P<0.0001). The mean values of these vital signs in survivors

were significantly higher than non-survivors. The percentage

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5 Archives of Academic Emergency Medicine. 2022; 10(1): e25

Figure 2: Comparison of area under the receiver characteristics curve (AUC) between assessed scoring systems in prediction of intensive

care unit (ICU) poor outcome (mortality or disability). A) Demonstration of the prediction rules with excellent performance (AUC >0.90); B)

Demonstration of the prediction rules with good performance (AUC between 0.70 and 0.90).

Table 3: Performance of physiologic scoring systems and Glasgow coma scale in prediction of poor outcome in intensive care unit (ICU)

admitted trauma patients

Score CP TP TN FP FN Sensitivity Specificity PPV NPV PLR NLR
GCS 9 66 124 1 9 88.0(78.4-94.4) 99.2(95.6-100) 98.5(92.0-100) 93.2(87.5-96.9) 110.00(15.6-776.2) 0.12(0.07-0.2)
GAPS 18 48 122 3 27 64(52.1-74.8) 97.6(93.1-99.5) 94.1(83.8-98.8) 81.9(74.7-87.7) 26.67(8.6-82.6) 0.37(0.3-0.5)
NTS 14 54 125 0 21 72.0(60.4-81.8) 100.0(97.1-100.0) 100.0(93.4-100.0) 85.6(78.9-90.9) 0 0.28(0.2-0.4)
RTS 7 62 118 7 13 82.7(72.2-90.4) 94.4(88.8-97.7) 89.9(80.2-95.8) 90.1(83.6-94.6) 14.76(7.1-30.5) 0.18(0.1-0.3)
MEWS 4 66 77 48 9 88.0(78.4-94.4) 61.6(52.5-70.2) 57.9(48.3-67.1) 89.5(81.1-95.1) 2.29(1.8-2.9) 0.19(0.1-0.4)
NEWS 5 73 47 78 2 97.3(90.7-99.7) 37.6(29.1-46.7) 48.3(40.1-56.6) 95.9(86.0-99.5) 1.56(1.3-1.8) 0.07(0.02-0.3)
WPSS 4 51 82 43 24 68.0(56.2-78.3) 65.6(56.6-73.9) 54.3(43.7-64.6) 77.4(68.2-84.9) 1.98(1.5-2.6) 0.49(0.3-0.7)
REMS 6 48 111 14 27 64.0(52.1-74.8) 88.8(81.9-93.7) 77.4(65.0-87.1) 80.4(72.8-86.7) 5.71(3.4-9.6) 0.41(0.3-0.5)
RAPS 4 51 104 21 24 68.0(56.2-78.3) 83.2(75.5-89.3) 70.8(58.9-81.0) 81.3(73.4-87.6) 4.05(2.6-6.1) 0.38(0.3-0.5)
Data are presented with 95% confidence interval. CP: Cut off point; TP: True positive; TN: True negative; FP: False positive;
FN: False negative; PPV: Positive predictive value; NPV: Negative predictive value; PLR: Positive likelihood ratio;
NLR: Negative likelihood ratio; GCS: Glasgow coma scale; GAPS: Glasgow Coma Scale, Age, and Systolic Blood Pressure score;
NTS: New trauma score; RTS: Revised trauma score; MEWS: Modified Early Warning Score; NEWS: National Early Warning Score;
WPSS: Worthing physiological scoring system; REMS: Rapid emergency medicine score; RAPS: Rapid acute physiology score.

of poor outcomes was 37.5% (n=75). The mean values of vital

signs, including respiratory rate, systolic blood pressure, di-

astolic blood pressure, and mean arterial pressure, were sig-

nificantly higher in the good outcome group compared to the

poor outcome group (P<0.0001). The mean length of stay in

ICU in the poor outcome group was higher than the good

outcome (P<0.0001) (Table1).

3.2. Accuracy of physiologic scoring systems in
mortality prediction

Table 2 displays the ROC curve analyses of the eight physio-

logic scoring systems and GCS. The sensitivity value of GCS

was 100%. However, the sensitivity values of physiologic

scoring systems, including NTS, RTS, WPSS, RAPS, REMS,

MEWS, NEWS, and GAPS, were 97.3%, 100%, 83.8%, 86.5%,

83.8%, 94.6%, 97.3%, and 94.6%, respectively. Also, the speci-

ficity value of GCS was 81.6%, and the specificity values of

NTS, RTS, WPSS, RAPS, REMS, MEWS, NEWS, and GAPS were

89%, 80.4%, 61.3%, 75.5%, 81%, 51.5%, 29.4%, 90.2%, respec-

tively (Table 2).

Figure1 shows the AUC values of physiologic scoring systems.

The AUC value for GCS was 0.98 (95% CI: 0.96 - 0.99). The

AUC values of NTS, RTS, WPSS, RAPS, REMS, MEWS, NEWS,

and GAPS were 0.95 (95% CI: 0.92 - 0.98), 0.95 (95% CI: 0.92

- 0.98), 0.83 (95% CI: 0.76 - 0.90), 0.89 (95% CI: 0.84 - 0.94),

0.91 (95% CI: 0.86 - 0.95), 0.84 (95% CI: 0.78 - 0.90), 0.77

(95% CI: 0.69 - 0.85), and 0.97 (95% CI: 0.95 - 0.99), respec-

tively. The AUC of GCS was significantly higher than those

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S. Khari et al. 6

of RTS (P=0.005), WPSS (P=0.0001), RAPS (P=0.0002), REMS

(P=0.002), MEWS (P=0.0001), and NEWS (P=0.0001). How-

ever, the AUC of GCS was not significantly different from NTS

(P=0.146) and GAPS (P=0.513).

3.3. Accuracy of physiologic scoring systems in
poor outcome prediction

Table 3 represents the results of ROC curve analysis for the

eight physiologic scoring systems and Glasgow coma scale.

The sensitivity value of GCS was 88%. The sensitivity values

of the eight physiologic scoring systems, including NTS, RTS,

WPSS, RAPS, REMS, MEWS, NEWS, and GAPS, were 72%,

82.7%, 68%, 68%, 64%, 88%, 97.3%, and 64%, respectively.

The specificity value for GCS was 99.2% and for NTS, RTS,

WPSS, RAPS, REMS, MEWS, NEWS, and GAPS, values were

100%, 94.4%, 65.6%, 83.2%, 88.8%, 61.6%, 37.6%, and 97.6%,

respectively (Table3).

Figure 2 shows the AUC values for all of the physiologic scor-

ing systems. The AUC value for GCS was 0.98 (95% CI: 0.97

- 1.00). The AUC values of NTS, RTS, WPSS, RAPS, REMS,

MEWS, NEWS, and GAPS, were 0.97 (95% CI: 0.95 - 0.99),

0.95 (95% CI: 0.92 - 0.98), 0.75 (95% CI: 0.68 - 0.81), 0.85

(95% CI: 0.80 - 0.90), 0.85 (95% CI: 0.80 - 0.91), 0.84 (95%

CI: 0.79 - 0.89), 0.77 (95% CI: 0.71 - 0.84), and 0.97 (95% CI:

0.95 - 0.99), respectively. The AUC for GCS was not signifi-

cantly different from NTS (P=0.182) and GAPS (P=0.089), but

the AUC of GCS was considerably higher than those of RTS

(P=0.001), WPSS (P<0.001), RAPS (P<0.001), REMS (P<0.001),

MEWS (P<0.001), and NEWS (P<0.001).

4. Discussion

This study illustrated the performance of physiologic scor-

ing systems including NTS, RTS, WPSS, RAPS, REMS, MEWS,

NEWS, GAPS, and GCS in predicting traumatic patient mor-

tality and poor outcomes in ICU using the ROC curve. Ac-

cordingly, NTS, RTS, WPSS, RAPS, REMS, MEWS, NEWS, and

GAPS performed well. However, compared to GCS, the scor-

ing systems of RTS, WPSS, RAPS, REMS, and MEWS had

poorer performance and NTS, and GAPS were not signifi-

cantly different from GCS. Moreover, the sensitivity of GCS

was higher than NTS and GAPS in predicting the mortality of

patients.

In this regard, different studies have been conducted, which

had results consistent or inconsistent with the present study.

In a cross-sectional study conducted on 125 traumatic brain

injury patients admitted in ICU, the efficacy of GCS and

APACHE II score were compared. The values of ROC curve

analysis for GCS (AUC=0.81, PPV=69.2, sensitivity=61.4) and

APACHE II (AUC=0.83, PPV=80.6, sensitivity=56.9) were ac-

ceptable, and they had no significant difference. However,

for the initial evaluation, GCS was suggested because of its

simplicity and quickness (5). In a diagnostic accuracy study

with 1702 trauma patients in emergency departments of four

hospitals, the performance of RTS, RAPS, REMS, and WPSS

was compared with GCS in predicting in-hospital deaths and

poor outcomes. The results demonstrated that the AUC value

of GCS was not significantly different from that of RAPS,

REMS, and WPSS. However, GCS performed significantly bet-

ter than RTS in prediction of in-hospital deaths. In addition,

this conclusion was also proper for predicting poor outcomes

in the emergency department such as mortality, vegetative

state, and disability (14). Another prospective observational

study was conducted to evaluate the power of scoring sys-

tems including GCS, APACHE-II, RAPS, and REMS in pre-

dicting the need for mechanical ventilation in patients with

drug overdose. The ROC curve analysis showed that there

were no significant differences between them. However, it

seemed that the utilization of the combination of GCS >8

(NPV=100%) and REMS was beneficial in excluding patients

without the need for ventilator support (27). A prospective

diagnostic study was conducted to compare the accuracy of

GCS and KTS in prediciting in-hospital mortality. The AUC

for the GCS value on admission (0.91) and after 24 hours

(0.96) was significantly higher than KTS on admission (0.82)

and 24 hours later (0.85). Also, the GCS was more precise

than KTS in diagnosis of head injury patients (28). A diagnos-

tic accuracy study on 1861 trauma patients assessed scoring

systems including GAP, MGAP, ISS, and GCS. The AUC val-

ues of GAP, MGAP, ISS, and GCS were 0.91 (sensitivity=72.99,

specificity=95.52), 0.90 (sensitivity=81.04, specificity=87.70),

0.80 (sensitivity=89.10, specificity=61.11), and 0.88 (sensitiv-

ity=81.52, specificity=92.00), respectively.

Therefore, it seemed that both GAP and MGAP scoring sys-

tems could predict mortality (8). On the other hand, a retro-

spective study was performed to assess the accuracy of scor-

ing systems such as GCS, ISS, and RTS in predicting out-

comes in young children with traumatic injuries. The re-

sults demonstrated that the AUC of ISS in predicting mortal-

ity was higher than GCS and RTS. Also, worse trauma scores

of ISS, GCS, and RTS correlated with more deaths (29). A

retrospective study was conducted to determine the predic-

tors of trauma patients’ deaths in ICU. The AUC values of

scoring systems including GCS, ISS, NISS, RTS, TRIS, RISC II,

APACHE II, SAPS II, and SOFA were 0.69, 0.82, 0.90, 0.74, 0.86,

0.88, 0.69, 0.67, and 0.69, respectively; so the NISS and RISC

II were more accurate in prediction of short-term mortality

of patients with severe trauma (7). In a recent diagnostic ac-

curacy study with 754 patients, the results indicated that the

performance of REMS was more precise than GCS, ISS, and

MEWS for prediction of in-hospital mortality rate of multi-

ple trauma patients ≥ 24 hours after admission, and the AUC
value of REMS (0.94) was significantly higher than GCS (0.85;

P=0.035) (12).

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7 Archives of Academic Emergency Medicine. 2022; 10(1): e25

The differences between the results of this study and oth-

ers may be related to considering different cut-off values,

patient settings, and demographic characteristics. As an il-

lustration, consider Heydari et al.’s study with traumatic pa-

tients in the emergency department, in which 90% of patients

had GCS≥13 (12). However, the present study was conducted
in the ICU and about half of the patients had GCS<13. In ad-

dition, Heydari’s study is one of the few studies in which the

sensitivity of GCS is found to be low (12), while most of the

previous studies reported a high sensitivity for GCS (8, 14, 27,

28).

5. Limitations

A limitation of this study was the relative small sample size.

A larger sample size and examining the patient at different

time points provide more valuable and reliable results. Con-

venience sampling was another shortcoming of the present

diagnostic accuracy study.

6. Conclusion

According to this study, GCS has excellent accuracy in pre-

diction of in-hospital outcome of trauma patients. Since it

is easy to use and calculate, GCS can be considered as the

optimum predictive instrument in trauma patients. GCS is

more practical and simple than physiological scoring sys-

tems, which are complex and time-consuming to measure.

7. Declarations

7.1. Acknowledgments

This article is based on Sorour Khari’s Master’s thesis and the

study was conducted with the financial support of Shahid Be-

heshti University of Medical Sciences.

7.2. Authors’ contributions

Study design: SK, MZ, MY; Data gathering: SK; Analysis and

interpretation of results: MY, MZ Drafting: SK; Critically re-

vised the paper: MZ and MY. All authors read and approved

the final draft of manuscript and are responsible for all parts

of study.

7.3. Funding

Shahid Beheshti University of Medical Sciences financially

contributed to this study.

7.4. Conflict of interest

The authors declare that they have no conflict of interest.

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	Introduction
	Methods
	Results
	Discussion
	Limitations 
	Conclusion 
	Declarations
	References