Keywords Glasgow Coma Scale - Pupil - Brain Injuries - Traumatic - Prognosis - Mortality
Palavras-chave Escala de Coma de Glasgow - Pupila - Lesões Encefálicas Traumáticas - Prognóstico
- Mortalidade
INTRODUCTION
Traumatic brain injury (TBI) is a public health problem[1 ] that may result in death or permanent disability.[2 ]
[3 ]
[4 ] It represents a significant economic burden to society through high healthcare costs
and lost productivity.[5 ]
[6 ] In 2016, there were 27 million new TBI cases worldwide, with > 60% of these in low-
and middle-income countries. Recently estimated annual incidence per 100.000 inhabitants
was 383 in Brazil, 333 in the USA, and 313 in China.[6 ] Santa Catarina, a southern Brazilian state with ∼ 7 million inhabitants in 2019,
had 1,146 deaths related to TBI in the same year. Our recent prospective study in
two metropolitan areas in the Santa Catarina state, with a combined population of
1,527,378, showed over 101.5 years of life lost per 100,000 inhabitants per year.[7 ] Unfortunately, the worldwide TBI incidence is rising, mainly due to the injuries
associated with increased urban traffic and violence, leading to TBI being considered
a “silent epidemic”[6 ]
[8 ]
[9 ].
Many prognostic models have been developed to predict the outcome after TBI.[10 ]
[11 ]
[12 ]
[13 ]
[14 ] The corticosteroid randomization after a significant head injury (CRASH) trial (with
10,008 patients), which demonstrated that variables including Glasgow coma scale (GCS),
pupil reactivity, the presence of significant extracranial injury, subarachnoid bleeding,
and other abnormal results on computed tomography (CT), are all well-known prognostic
factors.[14 ]
[15 ] Furthermore, the same research revealed that patients from low- and middle-income
countries experienced higher mortality at 14 days than those from high-income countries,
but a similar functional outcome at 6 months after trauma among the survivors.[14 ] Nevertheless, the use of prognostic scores that combine multiple risk factors has
not found widespread acceptance in clinical practice because of a significant number
of clinical measurements required.[15 ]
[16 ]
In combination, the pupil reactivity and the GCS score are the most clinically relevant
information to predict the survival of TBI patients.[11 ]
[12 ]
[15 ]
[17 ]
[18 ] To simplify the use of prognostic information in TBI, Brennan et al. proposed an
arithmetic combination of the GCS score and pupillary response (GCS-P).[15 ] The GCS-P score was applied to the combined data from the CRASH study[19 ] and the International Mission for Prognosis and Clinical Trials in TBI (IMPACT)
study with 11,989 patients,[18 ] and provided information about patient outcomes in comparison with more complex
methods.[15 ] Although the CRASH study included a group of Brazilian patients (n = 119), we aimed to assess the applicability of the GSC-P score in a large, prospective,
and well-characterized sample of Brazilian patients.
The objective of the present work was to analyze the accuracy of the GCS-P score to
predict the mortality during hospitalization in patients with severe TBI derived from
five previous prospective studies carried out in the Santa Catarina state and compare
the results with those from severe TBI patients in the combined CRASH[19 ] and IMPACT[18 ] data, using the same methodology as described by Brennan and colleagues.[15 ] Also, a comparison was made between the accuracy of the GCS-P model and the model
with the GCS, pupil responsivity, and additional clinical and imagining data.
METHODS
Patients
The initial sample included 1,097 patients with severe traumatic brain injury from
previous 5 prospective studies,[7 ]
[20 ]
[21 ]
[22 ]
[23 ]
[24 ] all of which were part of the Brain Trauma Database Project for the Santa Catarina
state. The Ethics Committee for Research in Humans at the Federal University of Santa
Catarina approved the project (Protocols 163/2005 of 2005 and 02832612.6.1001.0121
of 2013).
Patients were admitted to the hospital “Governador Celso Ramos” between January 1994
and December 2003 (n = 748), and between April 2006 and September 2008 (n = 83). The last 266 patients were admitted between April 2014 and January 2016 at
the regional hospital of the city of Criciúma (n = 61), the regional hospital of the city of São José (n = 122), and the Hospital “Governador Celso Ramos” in the city of Florianópolis (n = 83). These hospitals are the TBI reference centers that circumscribe the catchment
area of over 1.5 million inhabitants in 2 metropolitan areas of the Santa Catarina
state. Thirty-one patients (2.8%) were excluded because of the lack of pupil evaluation
due to ocular trauma (n = 12) or other missing variables (n = 13), so that the final sample consisted of 1,066 patients. The inclusion criteria
were a GCS score ≤ 8 or its deterioration within 48 hours of the TBI. The patients
who evolved to brain death within 24 hours of admission were excluded from the present
study. The primary endpoint was death during hospitalization so that the dependent
variable was hospital mortality. The independent variables analyzed were age, sex,
GCS score, cranial CT findings, glucose levels, and pupil reactivity at admission.
Cranial CT findings were classified into six categories according to the Marshall
classification.[25 ]
[26 ] The presence of traumatic subarachnoid hemorrhage was another independent variable.
Computed tomography analysis was performed by one of the researchers and confirmed
by the neurosurgeon when necessary, not blinded for the patient clinical status but
always blinded for the patient outcome.
Combining information about GCS score and pupil reactivity
We used the method reported by Brennan et al.[15 ] that combine a patient's GCS score and pupil findings into a single unidimensional
index. First, we categorized pupils in the pupil reactivity score (PRS) according
to the number of nonreactive pupils: if both pupils were unreactive to light, the
score was 2, if only one pupil was unreactive to light, the score was 1, if both pupils
were reactive to light, the score was 0. The GCS-pupil (GCS-P) score was obtained
by subtracting the PRS from the GCS total score: GCS-P = GCS - PRS.
Another modification tested as a prognostic factor of hospital mortality among severe
TBI patients was based on a previous study[24 ] that showed about a sixfold increase in mortality among the patients with bilateral
mydriatic compared to anisocoric pupils at admission. A modified GCS-P proposed by
the present study authors scored 3 instead of 2 in the Brennan et al. scheme.[15 ] If only one pupil was unreactive to light; the score was 1; if both pupils were
reactive to light, the score was 0. The modified GCS-pupil score was obtained by subtracting
the PRS from the GCS total score.
Statistical analysis
Bivariate associations between the hospital mortality and the independent variables
were analyzed by binary logistic regression, and the results were expressed as odds
ratio (OR) with its 95% confidence interval (CI). The independent variables with significance
level < 0.20 in the bivariate regression were included in a multivariate binary regression
using the stepwise selection criterion. The Hosmer-Lemeshow test was used to evaluate
the goodness of fit of the final model.
The area under the receiver operating characteristic (ROC) curve, abbreviated as AUROC,
and its 95%CI, were used to assess the classification performance of the models under
comparison. Split-half cross-validation was used to avoid fitting and testing classification
performance on the same sample.
RESULTS
Eighty-five percent (n = 908) of the patients were men. The mean age was 35 years old, and the overall hospital
mortality was 32.8%. The most frequent cause of TBI were road accidents (76.3%), followed
by falls (15.1%), assaults (4.5%), firearm injuries (1.2%), and others (3%). The characteristics
of survivors and nonsurvivors are shown in [Table 1 ]. Mortality was associated with older age, higher glucose levels, Marshall CT classification
injury type > II, traumatic subarachnoid hemorrhage on CT, lower GCS scores on hospital
admission, and anisocoric or mydriatic pupils ([Table 2 ]). The association between female sex and mortality shown in the univariate analysis
([Table 1 ]) was not confirmed by the multivariate binary logistic regression (p = 0.24) and this variable was not included in [Table 2 ].
Table 1
Bivariate logistic regression analysis for the association of mortality during hospitalization
with demographic and clinical risk factors among the patients with severe traumatic
brain injury
Predictive variables
All Patients
N = 1,066 (%)
Outcome
Crude OR
(CI 95%)
P -value
Survivors
n = 716 (%)
Non-survivors
n = 350 (%)
Sex
Male
908 (85)
624 (68.7)
284 (31.3)
1.0
Female
158 (15)
92 (58.2)
66 (41.8)
1.56 (1.11–2.207)
0.01
Age (years old)
Mean (±SD)
35.18 (16.52)
34.26 (15.92)
37 (17.56)
NA
12–30
530 (49.6)
368 (69)
164 (31)
1.0
31–45
264 (25.4)
181 (66.5)
91 (33.5)
1.12 (0.82–1.54)
0.44
46–60
156 (14.8)
108 (68)
51 (32)
1.06 (0.72–1.55)
0.76
> 60
109 (10.2)
62 (57)
47 (43)
1.70 (1.11–2.59)
0.01
Glucose
Mean (±SD)
160.4 (63.5)
< 0.0001
≤ 110
148 (15.0)
110 (74.3)
38 (25.7)
1.0
111–220
721 (73.0)
495 (68.7)
226 (31.3)
1.32 (0.88–1.97)
0.17
221–300
85 (8.6)
44 (51.8)
41 (48.2)
2.70 (1.54–4.74)
< 0.001
> 300
33(3.3)
13(39.4)
20(60.6)
4.45 (2.02–9.81)
< 0.0001
Marshall cranial CT classification
Type I injury
93 (8.8)
79 (84.9)
14 (15.1)
1.0
Type II injury
239 (22.4)
200 (83.7)
39 (16.3)
1.1 (0.56–2.13)
0.77
Type III injury
274 (25.7)
173 (63.0)
101 (37.0)
3.29 (1.77–6.11)
< 0.001
Type IV injury
110 (10.3)
40 (36.4)
70 (63.6)
9.87 (4.96–19.65)
< 0.001
Type V injury
300 (28.3)
197 (65.6)
103 (34.4)
2.95 (1.59–5.46)
0.001
Type VI injury
44 (4.1)
22 (50)
22 (50)
5.64 (2.48–12.81)
< 0.001
SAH
No
641 (60.6)
464 (72.4)
177 (27.6)
1.0
Yes
417 (39.4)
248 (59.5)
169 (40.5)
1.78 (1.37–2.32)
< 0.001
GCSa
8
211(19.9)
175(81.8)
39(18.2)
1.0
7
224(20.9)
187(83.1)
38 (16.9)
0.9(0.55–1.5)
0.71
6
177(16.6)
135 (75.8)
43 (24.2)
1.43(0.87–2.32)
0.15
5
60 (5.6)
33 (55.0)
27 (45.0)
3.67(1.98–6.8)
< 0.001
4
151 (14)
70 (46.4)
81 (53.6)
5.2(3.24–8.3)
< 0.001
3
243 (23)
122 (49.4)
125 (50.6)
4.6(2.99–7.05)
< 0.001
Pupilsb
Isochoric
535 (50.2)
433 (80.9)
102 (19.1)
1.0
Anisocorics
422(39.6)
259 (61.4)
163 (38.6)
2.68 (2.0–3.58)
< 0.00001
Mydriatics
109 (10.2)
24 (22.0)
85 (78.0)
15.0 (9.10–24.8)
< 0.00001
TBI Centerc
Criciúma
61 (6.1)
39 (63.9)
22 (36.1)
1.0
São José
122 (12.1)
88 (72.1)
34 (27.9)
0.68 (0.36–1.32)
0.26
Florianópolis
820 (81.8)
547 (66.5)
273 (33.5)
0.88 (0.51–1.52)
0.65
Abbreviations: CI, confidence interval; CT, computed tomography; GCS, Glasgow Coma
Scale; OR, odds ratio; SAH, subarachnoid hemorrhage; TBI, traumatic brain injury.
Notes: a GCS at admission; b pupils reactivity at admission; c cities were the TBI reference centers are located.
Table 2
Multivariate binary logistic regression for the association of mortality during hospitalization
with demographic and clinical risk factors among 1,066 patients with severe traumatic
brain injury
Independent variables
Probability of death
95% CI bounds
Lower
Upper
Age (years old)
12–30
0.31
0.3
0.32
31–45
0.32
0.30
0.32
46–60
0.33
0.29
0.38
> 60
0.42
0.38
0.46
Glucose (mg/dl)
≤ 110
0.31
0.23
0.39
111–220
0.33
0.31
0.34
221–300
0.39
0.36
0.42
> 300
0.39
0.38
0.40
Marshall cranial CT classification
Type I injury
0.24
0.17
0.30
Type II injury
0.23
0.17
0.29
Type III injury
0.34
0.3
0.37
Type IV injury
0.57
0.50
0.63
Type V injury
0.30
0.28
0.31
Type VI injury
0.56
0.55
0.56
Glasgow Coma Scale
3
0.43
0.38
0.49
4
0.46
0.42
0.50
5
0.45
0.39
0.50
6
0.25
0.24
0.26
7
0.20
0.17
0.24
8
0.24
0.22
0.27
Pupil reactivity
Isochoric
0.22
0.19
0.26
Anisocoric
0.37
0.34
0.40
Mydriatic
0.63
0.50
0.75
SAH
No
0.28
0.27
0.29
Yes
0.39
0.38
0.40
Abbreviations: CI, confidence interval; CT, computed tomography; SAH, subarachnoid
hemorrhage.
Split-half cross-validation model showing an average sensibility of 76.9% (range 74.7–79.2%),
a specificity of 63.1% (61.5–64.8%) for this model. The Hosmer & Lemeshow goodness-of-fit
test produced the Pearson chi-square of 392 with 387 degrees of freedom and associated
p -value of 0.418, thus confirming a good fit of the final model.
For comparison, the proportion of patients with severe TBI according to the GCS score
in the IMPACT/CRASH combined data bank, relative to the present study sample, is shown
in [Table 3 ]. The proportion of deaths at 6 months after the hospitalization of patients from
the IMPACT/CRASH data bank (n = 9,057) was 33.9%, similar to the 32.8% observed in the present study. The GCS score
decline was also associated with increased mortality in the studies under comparison
([Table 3 ]).
Table 3
Mortality of patients from the CRASH/IMPACT sample at 6 months after traumatic brain
injury, the mortality during hospitalization in the present study according to the
GCS-P score and the association between the GCS-P score and the hospital mortality.
Combined CRASH/IMPACT data (n = 9,153)
Present study (n = 1,066)
Binary regressiona
GCS-P score
n (%)
Mortality at 6 months after TBI (%)
GCS-P score
n (%)
Hospital mortality (%)
Crude OR
(95%CI)
p -value
8
1073 (11.7)
20.0
8
143 (13.4)
16.0
1.0
7
1930 (21.1)
19.2
7
188 (17.6)
12.2
0.72 (0.39–1.35)
0.32
6
1550 (16.9)
25.0
6
185 (17.3)
20.0
1.3 (0.73–2.31)
0.36
5
1136 (12.4)
32.6
5
111 (10.4)
34.2
2.71 (1.5–4.91)
< 0.001
4
1016 (11.1)
39.5
4
80 (7.5)
37.5
3.1 (1.65–5.9)
< 0.0001
3
1178 (12.9)
40.9
3
197 (18.5)
45.7
4.3 (2.59–7.43)
< 0.0001
2
636 (6.9)
64.6
2
112 (10.5)
58.9
7.48 (4.1–13.4)
< 0.0001
1
634 (6.9)
74.4
1
50 (4.7)
86.0
32 (12.83–80)
< 0.0001
Abbreviations: CI, confidence interval, GCS-P, Glasgow coma scale pupil score; OR,
odds ratio; TBI, traumatic brain injury.
Note: a Bivariate binary regression showing the association between GCP-S and the mortality
during hospitalization due to severe TBI in the present study.
The frequency of loss of pupil reactivity increased with decreasing GCS score: 2.07%
at GCS scores 7 to 8 had a bilateral loss of pupil reactivity, 6.75% at GCS scores
5 to 6, and 21.3% at GCS scores 3 to 4. In the patients with the GCS scores 4, 5,
and 6, unilateral loss of pupil reactivity occurred at similar rates: 49, 48, and
46%, respectively. Bilateral pupil reactivity was more frequent among patients with
a GSC score 8 (67.8%) and less frequent in patients with a GCS score 4 (27.8%) (data
not shown).
The relationship between the combined GCS-P and mortality at discharge is shown in
[Table 3 ]. The combined score extended the range over which the differentiation of outcomes
was made, with the highest mortality rate of 50% in the lowest GCS score (score 3)
and rising to 86% in the GCS-P in the present study. The nonmonotonic relationship
between GCS and mortality, where higher mortality was observed for the GCS score of
4 rather than 3 in bivariate analysis, is no longer seen for the GCS-P score. The
same holds for the relationship between the GCS-P and mortality at 6 months since
severe TBI with the CRASH/IMPACT data, where the highest mortality rate increased
from 51.0% to 74.4% ([Table 3 ]).
The GCS-P and its modified version (Modified GCS-P) were compared in terms of simple
arithmetical scores and by adding the clinical and radiological variables shown in
[Table 2 ]. The AUROC was 0.73 (0.70–0.77) for the GCS-P model, 0.74 (95%CI: 0.71–0.77) for
its modified version, and 0.80 (95%CI: 0.77–0.83) for the model that included additional
clinical and radiological variables ([Figure 1 ]). These accuracy findings for mortality of the GCS-P score and the model combining
other variables (GCS, pupil reactivity, age, cranial CT findings) were the same observed
in the subgroup of patients with severe TBI from the CRASH data bank.[15 ]
Figure 1 Comparison between the receiver operator characteristic (ROC) curves and their accuracy
to predict hospital mortality in severe traumatic brain injury by three models: the
Glasgow Coma Scale Pupil (GCS-P) score, modified GCS-P score, and the multivariate
binary regression model including age, blood glucose, Marshall's cranial computed
tomography classification, GCS score, pupil reactivity, and the presence of traumatic
subarachnoid hemorrhage on admission. The area under the ROC was 0.73 (0.70–0.77)
for the GCS-P model, 0.74 (95% confidence interval [CI]: 0.71–0.77) for its modified
version, and 0.80 (95%CI: 0.77–0.83) for the model that included additional clinical
and radiological variables.
DISCUSSION
The present work is the largest prospectively acquired database about severe TBI in
Brazil and the first investigating the GCS-P score accuracy on a population level
in this country. The data collected using a protocol created by the same group of
researchers and the neurosurgical team involved with the patient's care aided the
internal validity of the study. The results obtained align with the current literature,[10 ]
[15 ]
[24 ] and demonstrated that old age, CT findings, GCS, and pupil reactivity at admission
are independently associated with severe TBI patient mortality during hospitalization.
According to the IMPACT and CRASH studies, severe TBI patient mortality was 33.9%
within 6 months of injury.[15 ]
[18 ]
[19 ] This figure is likely higher for the present study patients as they reached 32.8%
mortality already at discharge was 32.8%, although exact data were not available in
the present study because of a limited follow-up period.
Separately, GCS score and pupil response were each related to adverse outcomes in
various studies.[11 ]
[18 ]
[27 ] The mortality at discharge in patients with mydriatic pupils was 11 times higher
than in patients with isochoric pupils in the present study – a result much higher
than most TBI studies that have found about a threefold increase of this risk.[15 ]
[17 ]
[18 ]
[28 ]
[29 ]
The difference may be due to the higher severity of the injuries among the present
study patients, as almost half of them scored 3 or 4 on the GCS. However, the AUROC
comparison for mortality at discharge between the GCS-P score and its modified version
where mydriatic pupils scored 3, showed no statistically significant difference. The
paradox is that patients with GCS score 3 had lower mortality than those with score
4 ([Table 2 ]) have been reported in other studies,[29 ]
[30 ]
[31 ] but the reason for this is unclear. As discussed by Brennan et al.,[15 ] this may result from allocating a score to patients whose responsiveness was depressed
pharmacologically. Smoothing out of the relationship between the score and hospital
mortality due to severe TBI is a further advantage of the GCS-P score.[32 ]
Although survival prognosis based on statistical methods that combine information
about multiple aspects of the condition of the TBI patient have greater accuracy,
these have not found widespread acceptance in clinical practice because of their complexity.[33 ] The multiple binary regression model created for the present study using clinical
and radiological variables ([Table 2 ]) showed slightly better accuracy than the GSC-P score and the modified GSC-P score
([Figure 1 ]). However, simple scoring systems for stratifying the TBI severity have been used
by clinicians because of their simplicity and transparency of the score calculation.[15 ]
[30 ] The GSC-P score possesses these qualities and can be applied in clinical practice
with an accuracy of 73% ([Figure 1 ]). The proportion of deaths predicted by the GCS-P score applied to the IMPACT/CRASH
data was equivalent to that of the present study ([Table 3 ]), thus suggesting that this method may be suitable for predicting severe TBI mortality.
Also, the GCS-P maintained an inverse relationship between the GCS-P and adverse outcomes
across the complete range of all possible scores.
To conclude, the present study supports other external validation studies by showing
that the GCS-P score has greater accuracy for predicting hospital mortality among
severe TBI patients than GCS or pupil reactivity evaluation alone, and only slightly
inferior accuracy than more complex predictive models.[15 ] The role of the GCS-P in predicting long-term functional outcomes, including psychiatric
symptoms, cognitive performance, and quality of life, deserves further investigation.
