Abstract

Background: The Glasgow Coma Scale (GCS) is widely used in assessing level of consciousness. The GCS verbal component may be misleading in acute stroke: a focal neurological deficit leading to dysphasia could affect the score, independently of level of consciousness.

Aim: To investigate the relationship, in all strokes and in dysphasic patients, between stroke outcome and total GCS (with and without the verbal score) and its components, to assess their relative values.

Study design: Retrospective analysis following prospective data collection in an acute stroke unit and follow‐up.

Methods: Outcomes studied were 2‐week mortality and 3‐month recovery (survival, subject living at home). We used area under the receiver operating characteristic curve (AUC) to compare versions of the GCS and multivariate logistic regression to identify which subset of GCS components best predicted outcome.

Results: Of 1517 patients with acute stroke, 1217 had complete clinical and follow‐up data; 349 were dysphasic. Total GCS had greater AUC than the GCS without the verbal score, for mortality (all patients 0.78 vs. 0.76, p=0.021; dysphasics 0.72 vs. 0.71, p=0.52) and recovery (all patients 0.71 vs. 0.67, p=0.0001; dysphasics 0.74 vs. 0.70, p=0.055). Verbal and eye scores independently provided prognostic information, for each patient group and outcome measure.

Conclusions: The GCS contains valuable predictive information. Regardless of whether dysphasia is present, the verbal score should be assessed since it adds prognostic information to that from the eye component, and has greater value than the motor score.

Introduction

The Glasgow Coma Scale (GCS) was developed to describe consciousness level in head‐injured patients.1 It measures the best eye, motor and verbal responses, and is a widely used and accepted prognostic score2 for both traumatic3 and non‐traumatic altered consciousness levels.4 The score has been validated for its inter‐observer reliability,5 which improves with training and experience.6

The assessment of consciousness level in acute stroke is important for clinical management and as an indicator of prognosis. Presence or absence of coma is also used in selecting patients for clinical trials because of its relationship with outcome. As stroke may cause localized motor, speech or language deficits, the accuracy of the GCS as a measure of consciousness level may be affected. In turn, its prognostic value may be impaired. Conversely, in patients with a language disorder, the verbal score may reflect stroke severity in addition to its measurement of consciousness level, and for that reason it may retain useful prognostic information.

In head injury, the verbal component was shown to be inaccurate in up to 10% of patients due to injury or intubation and provided no additional prognostic information to motor and pupil responses.3 A recent evaluation of the admission GCS in 275 acute stroke patients compared three alternative strategies for verbal scoring in intubated or dysphasic patients.7 The total GCS score predicted acute mortality with 88% accuracy, and the verbal component could be excluded from the total GCS score without loss of predictive value.

We sought to determine the prognostic value of the total GCS score and its individual components in a large cohort of patients with acute stroke. In particular, we assessed the contribution of the verbal component to outcome prediction in patients with and without dysphasia. We also aimed to identify the subset of the eye, verbal and motor components which best predicted outcome in all strokes and in patients with a language disorder.

Methods

We studied patients admitted to our acute stroke unit between July 1990 and March 1995. We included individuals with acute stroke who had not previously been entered in the study. In all patients, CT scanning was performed to exclude any non‐vascular cause of the neurological deficit. Medical staff prospectively recorded demographic data and clinical features of patients on admission, including measurement of the GCS eye, motor and verbal scores. The stroke unit admission policy aims for patients to be admitted and assessed within 48 h of stroke onset. Medical residents and a qualified speech and language therapist tested for the presence of dysphasia.

Outcome follow‐up by record linkage8 to death and hospital discharge records established patient survival and placement. Death records were obtained from the Registrar General of Scotland. Record linkage uses a probability‐matching algorithm that gives low rates of false‐positive and false‐negative links (about 1%).8 Admissions to non‐National Health Service hospitals and institutions outside Scotland are not detected. Placement was used as a surrogate marker of survival and functional outcome combined: patients who were alive and living at home or with relatives were defined as having a good outcome; patients who had died or were resident in institutional care were considered to have a poor outcome. Patient survival and placement were recorded, blind to the GCS score, at several time points. Two‐week survival and placement at 3 months were selected to represent the clinically relevant outcomes of early mortality and medium‐term recovery, respectively.

Statistical methods

Receiver operating characteristic (ROC) curves were plotted to assess the prognostic value, for 2‐week mortality and 3‐month placement, of the total GCS score, the total GCS excluding the verbal component, and the eye, verbal and motor components individually. For 2‐week mortality, we defined sensitivity and specificity for a cut‐point on a score as the respective proportions of deaths and survivors that were correctly predicted. Mortality was predicted in patients with a score below the cut‐point; survival was predicted in patients having a score greater than or equal to the cut‐point. We used an analogous interpretation of the cut‐point for 3‐month placement. We also calculated the positive predictive value (proportion of patients predicted to die or have a poor outcome who actually died or had a poor outcome) and the negative predictive value (proportion of patients predicted to survive or have a good outcome who actually survived or had a good outcome).

For each outcome measure and combination of GCS components, we identified the optimal cut‐point which maximized the sum of sensitivity and specificity. The area under the curve (AUC) and its standard error were calculated9 to measure the prognostic information provided by each combination of GCS components. The AUCs were compared using a test appropriate for correlated samples.10 The correlations arose since several combinations of GCS components were being compared on the same group of subjects.

Forward stepwise logistic regression modelling determined the subset of the GCS eye, motor and verbal components that best predicted 2‐week mortality and 3‐month placement. Effect sizes in the logistic regression were expressed as odds ratios and their 95%CIs. ROC curves were used to illustrate the performance of the model with the optimal subset of GCS components in each case. All analyses were repeated in the subgroup of patients with dysphasia.

The ROC analysis was carried out by Non‐parametric Receiver Operating Characteristic Analysis software (version 2.5)11 and the stepwise logistic regression modelling used SAS version 8.2 (SAS Institute) on a desktop PC.

Results

During the study, 1859 individuals were admitted to the acute stroke unit, of whom 1517 had a diagnosis of acute stroke. All components of the GCS were recorded for 1232 patients; outcome data were available for 1217 (99%). Overall, 349 patients (29%) were dysphasic. Table 1 shows the demographic and clinical features of the patients.

Some 235 patients (19%) died after 2 weeks; 537 (44%) had a poor outcome at 3 months. The corresponding figures for dysphasic patients were 95 deaths (27%) and 196 poor outcomes (56%).

Each of the GCS components was strongly related to outcome (Table 2). The verbal component gave the greatest AUC for each patient group and outcome measure, except for 2‐week mortality in dysphasic patients. Differences among components were statistically significant only for 3‐month placement in all patients. Figure 1 gives ROC curves for the eye, verbal and motor components for prediction of mortality and placement in each patient group.

For 2‐week mortality in the whole patient group, the areas under the ROC curve for the summed GCS scores with and without the verbal component, were 0.78 and 0.76, respectively (p=0.021). The difference in area had similar magnitude for dysphasic patients (AUCs 0.72 and 0.71; p=0.52). For the 3‐month placement endpoint in the whole patient group, the AUCs for the GCS scores with and without the verbal score, were 0.71 and 0.67, respectively (p=0.0001). The corresponding areas for dysphasic patients were 0.74 and 0.70 (p=0.055). Figure 2 compares the ROC curves for the summed GCS scores, including and excluding the verbal component.

In the whole patient group, stepwise logistic regression identified first the verbal and then the eye components as independent predictors of 2‐week mortality and 3‐month placement (Table 3). In dysphasic patients, the eye component entered the model for 2‐week mortality first, followed by the verbal score. The motor component, while itself associated with outcome, did not add statistically significant predictive information to these models. The motor score only provided prognostic information independently of the verbal and eye components in the prediction of placement in dysphasic patients (Table 3).

Further ROC curve analysis compared the versions of the GCS obtained at each step of the logistic regression modelling. This identified whether the statistically significant term added to the model at each step substantially improved predictive accuracy. The AUC became progressively greater as components were added to the score, for all patient groups and outcome measures (Figure 3, Table 4). In prediction of 3‐month placement in dysphasic patients, the AUC of 0.74 after addition of the eye component (Figure 3d) was not substantially greater than the AUC of 0.73 for the total verbal and motor score, although the difference was statistically significant (p=0.037). Table 4 shows the sensitivity, specificity, and positive and negative predictive values for the optimal cut‐point on each score. For the whole patient group the optimal cut‐points were the maximum scores (5 for the verbal component and 9 for the sum of verbal and eye). Use of the verbal component on its own provided good specificity but low sensitivity to poor outcome and mortality. Sensitivity for 2‐week mortality and poor outcome at 3 months was greatly improved when a combined verbal and eye score was used, although specificity was reduced. Optimal cut‐points were lower in the dysphasic subgroup, although the patterns of predictive accuracy were similar. Inclusion of the motor component did not substantially improve the prediction of 3‐month placement in dysphasic patients, despite its statistical significance in logistic regression modelling.

Table 1 

Patient characteristics

Variable
 
n (%)
 
Age* 71 (62‐79) 
Male sex 595 (49) 
Side of symptoms  
Left 563 (48) 
Right 578 (50) 
Bilateral 20 (2) 
Oxfordshire Community Stroke Project clinical classification12  
Total anterior circulation syndrome 309 (25) 
Partial anterior circulation syndrome 402 (33) 
Posterior circulation syndrome 143 (12) 
Lacunar syndrome 348 (29) 
Other 11 (1) 
Nature of acute cerebrovascular event  
Haemorrhagic stroke 158 (13) 
Ischaemic stroke 1059 (87) 
Variable
 
n (%)
 
Age* 71 (62‐79) 
Male sex 595 (49) 
Side of symptoms  
Left 563 (48) 
Right 578 (50) 
Bilateral 20 (2) 
Oxfordshire Community Stroke Project clinical classification12  
Total anterior circulation syndrome 309 (25) 
Partial anterior circulation syndrome 402 (33) 
Posterior circulation syndrome 143 (12) 
Lacunar syndrome 348 (29) 
Other 11 (1) 
Nature of acute cerebrovascular event  
Haemorrhagic stroke 158 (13) 
Ischaemic stroke 1059 (87) 

*Median (IQR).

Table 2 

AUCs for individual GCS component ROC curves

Endpoint
 
Patient group
 
Eye
 
Verbal
 
Motor
 
p*
 
2‐week mortality All 0.71 0.74 0.71 0.09 
 Dysphasic 0.66 0.66 0.67 0.92 
3‐month placement All 0.63 0.68 0.64 0.0001 
 Dysphasic 0.64 0.69 0.67 0.20 
Endpoint
 
Patient group
 
Eye
 
Verbal
 
Motor
 
p*
 
2‐week mortality All 0.71 0.74 0.71 0.09 
 Dysphasic 0.66 0.66 0.67 0.92 
3‐month placement All 0.63 0.68 0.64 0.0001 
 Dysphasic 0.64 0.69 0.67 0.20 

*Tests whether or not all three AUCs are equal.

Figure 1. 

ROC curves for the individual GCS components. a 2‐week mortality [all patients], b 2‐week mortality [dysphasic patients], c 3‐month placement [all patients], d 3‐month placement [dysphasic patients].

Figure 1. 

ROC curves for the individual GCS components. a 2‐week mortality [all patients], b 2‐week mortality [dysphasic patients], c 3‐month placement [all patients], d 3‐month placement [dysphasic patients].

Figure 2. 

ROC curves for total GCS scores with and without the verbal component. a 2‐week mortality [all patients], b 2‐week mortality [dysphasic patients], c 3‐month placement [all patients], d 3‐month placement [dysphasic patients].

Figure 2. 

ROC curves for total GCS scores with and without the verbal component. a 2‐week mortality [all patients], b 2‐week mortality [dysphasic patients], c 3‐month placement [all patients], d 3‐month placement [dysphasic patients].

Table 3 

Multivariate stepwise logistic regression analysis using individual GCS components

Logistic regression model
 
Component
 
Order of entry to the model
 
OR (95%CI)*
 
2‐week mortality    
All patients Verbal 1.52 (1.37–1.68) 
 Eye 1.62 (1.37–1.92) 
Dysphasic patients Eye 1.58 (1.18–2.11) 
 Verbal 1.30 (1.11–1.52) 
3‐month placement    
All patients Verbal 1.46 (1.33–1.60) 
 Eye 1.64 (1.34–2.01) 
Dysphasic patients Verbal 1.29 (1.12–1.49) 
 Motor 1.45 (1.09–1.92) 
 Eye 1.77 (1.02–3.06) 
Logistic regression model
 
Component
 
Order of entry to the model
 
OR (95%CI)*
 
2‐week mortality    
All patients Verbal 1.52 (1.37–1.68) 
 Eye 1.62 (1.37–1.92) 
Dysphasic patients Eye 1.58 (1.18–2.11) 
 Verbal 1.30 (1.11–1.52) 
3‐month placement    
All patients Verbal 1.46 (1.33–1.60) 
 Eye 1.64 (1.34–2.01) 
Dysphasic patients Verbal 1.29 (1.12–1.49) 
 Motor 1.45 (1.09–1.92) 
 Eye 1.77 (1.02–3.06) 

*Odds ratio for survival (2‐week mortality) or good outcome (3‐month placement) per additional point on the score.

Figure 3. 

ROC curves for combinations of GCS components at each stage of the stepwise logistic regression modelling. a 2‐week mortality [all patients], b 2‐week mortality [dysphasic patients], c 3‐month placement [all patients], d 3‐month placement [dysphasic patients]. The numbers adjacent to the ROC curves show the best cut‐point for each GCS version.

Figure 3. 

ROC curves for combinations of GCS components at each stage of the stepwise logistic regression modelling. a 2‐week mortality [all patients], b 2‐week mortality [dysphasic patients], c 3‐month placement [all patients], d 3‐month placement [dysphasic patients]. The numbers adjacent to the ROC curves show the best cut‐point for each GCS version.

Table 4 

Predictive accuracy of the best cut‐point for various GCS scores for 2‐week mortality and 3‐month placement

Patient group
 
Prevalence of mortality/poor outcome
 
Score (optimal cut‐point)
 
AUC
 
Sensitivity (95%CI)
 
Specificity (95%CI)
 
PPV (95%CI)
 
NPV (95%CI)
 
2‐week mortality 
All patients 0.19 V (5) 0.74 0.66 (0.60–0.72) 0.78 (0.76–0.81) 0.43 (0.38–0.48) 0.90 (0.88–0.92) 
 0.19 E+V (9) 0.78 0.74 (0.68–0.79) 0.76 (0.73–0.79) 0.42 (0.38–0.47) 0.92 (0.90–0.94) 
Dysphasic patients 0.27 E (4) 0.66 0.44 (0.35–0.54) 0.87 (0.83–0.91) 0.57 (0.45–0.67) 0.81 (0.76–0.85) 
0.27 E+V (7) 0.70 0.68 (0.59–0.77) 0.65 (0.59–0.71) 0.42 (0.35–0.50) 0.85 (0.79–0.89) 
3‐month placement 
All patients 0.44 V (5) 0.68 0.49 (0.45–0.53) 0.86 (0.83–0.88) 0.73 (0.68–0.77) 0.68 (0.65–0.71) 
 0.44 E+V (9) 0.70 0.55 (0.51–0.59) 0.84 (0.81–0.86) 0.73 (0.68–0.77) 0.70 (0.67–0.73) 
Dysphasic patients 0.56 V (3) 0.69 0.58 (0.51–0.65) 0.76 (0.68–0.82) 0.76 (0.68–0.82) 0.59 (0.52–0.65) 
0.56 V+M (9) 0.73 0.62 (0.55–0.69) 0.74 (0.66–0.80) 0.75 (0.68–0.81) 0.60 (0.53–0.67) 
 0.56 E+V+M (14) 0.74 0.69 (0.62–0.75) 0.67 (0.60–0.74) 0.73 (0.66–0.79) 0.63 (0.55–0.70) 
Patient group
 
Prevalence of mortality/poor outcome
 
Score (optimal cut‐point)
 
AUC
 
Sensitivity (95%CI)
 
Specificity (95%CI)
 
PPV (95%CI)
 
NPV (95%CI)
 
2‐week mortality 
All patients 0.19 V (5) 0.74 0.66 (0.60–0.72) 0.78 (0.76–0.81) 0.43 (0.38–0.48) 0.90 (0.88–0.92) 
 0.19 E+V (9) 0.78 0.74 (0.68–0.79) 0.76 (0.73–0.79) 0.42 (0.38–0.47) 0.92 (0.90–0.94) 
Dysphasic patients 0.27 E (4) 0.66 0.44 (0.35–0.54) 0.87 (0.83–0.91) 0.57 (0.45–0.67) 0.81 (0.76–0.85) 
0.27 E+V (7) 0.70 0.68 (0.59–0.77) 0.65 (0.59–0.71) 0.42 (0.35–0.50) 0.85 (0.79–0.89) 
3‐month placement 
All patients 0.44 V (5) 0.68 0.49 (0.45–0.53) 0.86 (0.83–0.88) 0.73 (0.68–0.77) 0.68 (0.65–0.71) 
 0.44 E+V (9) 0.70 0.55 (0.51–0.59) 0.84 (0.81–0.86) 0.73 (0.68–0.77) 0.70 (0.67–0.73) 
Dysphasic patients 0.56 V (3) 0.69 0.58 (0.51–0.65) 0.76 (0.68–0.82) 0.76 (0.68–0.82) 0.59 (0.52–0.65) 
0.56 V+M (9) 0.73 0.62 (0.55–0.69) 0.74 (0.66–0.80) 0.75 (0.68–0.81) 0.60 (0.53–0.67) 
 0.56 E+V+M (14) 0.74 0.69 (0.62–0.75) 0.67 (0.60–0.74) 0.73 (0.66–0.79) 0.63 (0.55–0.70) 

AUC, area under curve; E, eye component of GCS; E+V, sum of eye and verbal GCS components; V+M, sum of verbal and motor GCS components; E+V+M, sum of all GCS components; PPV, positive predictive value; NPV, negative predictive value.

Discussion

Prognostic indicators have an important role in clinical management. It is important to identify patients with very poor prognosis who are unlikely to benefit from an effective treatment: patients who would otherwise have died may benefit only by surviving, but may be completely dependent and have a poor quality of life. Ideally, any method of predicting outcome should be simple, accurate and reproducible. The focal nature of acute stroke brings into question the validity of the GCS as a measure of level of consciousness and a predictor of outcome. Can information from the GCS be employed to determine the likely outcome from acute stroke?

Various methods exist for assessing level of consciousness in acute stroke clinical trials. Some trials have used adaptations of the level of consciousness assessments in the NIH,13,14 Scandinavian15 and European16 stroke scales, while another defined coma as inadequate motor response to painful stimuli.17 The GCS was developed because of a similar difficulty in defining level of consciousness in head injury, and has subsequently been shown to predict outcome well following head injury.

Teasdale recommends that the GCS components should not be summed, as they are not equivalent.18 Jennett and Teasdale (1977) noted, however, that all of the combinations of eye, verbal and motor findings which lead to a total GCS score of <8 meet the recognized definition of coma.19 Our ROC curve analysis in all patients shows that the total GCS score contains valuable prognostic information following acute stroke, and predicts early mortality more accurately than 3‐month outcome. A cut‐point of 15 was best, giving a simple interpretation: any impairment of consciousness level predicts poorer outcome. Predictive accuracy was also good in the dysphasic subgroup.

In the whole patient group, multivariate analysis of the individual GCS components identified the verbal and eye components as the best predictors of clinically relevant outcomes. Addition of the eye to the verbal component improved sensitivity although specificity was reduced. The motor score did not add prognostic information to the regression models. In contrast, previous stroke studies found that the verbal component was of little value and the motor component predicted outcome more accurately.7,20 Both of these studies were relatively small and one7 measured outcome at hospital discharge or transfer, rather than at a fixed time point after the stroke.

Focal deficits in acute stroke may affect component scores without reflecting the true consciousness level. As with speech, the motor score may be affected by focal deficit: severe or posterior circulation strokes may cause bilateral limb weakness. Another possible confounding factor is the erroneous recording of the motor score from the paretic limb, rather than observing the best response. Because of the effect of focal deficits, it would superficially seem reasonable to exclude the verbal score in dysphasic stroke patients.

Our analysis for the dysphasic subgroup showed that the verbal component provided additional prognostic information to the combined eye and motor scores. Our results suggest that when a language disorder is absent, the verbal score contributes prognostic information by measuring level of consciousness or by acting as a marker for confusion. In dysphasic patients, the verbal component may reflect stroke severity and hence predicts outcome. The motor component, whilst itself a prognostic indicator, did not add substantially to the predictive accuracy of the combined eye and verbal scores in the ROC analysis. This finding differs from the results in head injury.3

The GCS score predicts both 2‐week mortality and 3‐month placement after stroke. The verbal score contains valuable prognostic information and should be recorded even for dysphasic patients. We have shown that the maximum verbal score of 5, corresponding to an orientated verbal response, accurately predicts 2‐week survival and good 3‐month outcome. A score below 5 is an acceptable predictor of 2‐week mortality, but is not as accurate in predicting poor 3‐month outcome. A simple test for an orientated verbal response would be effective in selecting, prior to clinical trial entry, patients likely to survive the early effects of the stroke, since the negative predictive value lies between 0.88 and 0.92. Summing the verbal and eye scores and using a cut‐point of 9 (unimpaired vs. impaired consciousness) improves sensitivity to mortality and poor outcome but slightly reduces specificity to survival and good outcome. Using all three of the GCS components is helpful in describing the clinical status of a patient, but unnecessary for prognostic purposes.

The optimal cut‐points we identified require confirmation in other populations, since positive and negative predictive values depend on the mortality and poor outcome rates in a patient population. The 3‐month outcome measure also has limitations, as placement may be influenced by factors unrelated to functional outcome. Availability of a family carer may enable a patient to continue living at home. Placement in institutional care may be a less relevant outcome in countries other than the UK; for example, in Southern Europe, care for a disabled relative conventionally takes place within the family home.

We have demonstrated a strong relationship between the verbal and eye GCS scores and outcome in the acute stroke population. However, the positive predictive values for 2‐week mortality and negative predictive values for 3‐month placement range from 0.42 to 0.70 and are not sufficiently high to be used as the sole basis for clinical decision‐making on the individual patient. It would thus be preferable to combine GCS data in a model with other stroke prognostic factors if they were to be used in patient management.

Address correspondence to Dr C.J. Weir, Department of Medicine and Therapeutics, University of Glasgow, Gardiner Institute, Western Infirmary, Glasgow G11 6NT. e‐mail: c.j.weir@clinmed.gla.ac.uk

Dr Weir was supported in this work by a Special Training Fellowship in Health Services Research (Medical Research Council, UK). Chris Povey of the National Health Service (Scotland) Information and Statistics Division performed the record linkage analysis. The authors are grateful to Professor J.L. Reid, Dr G.T. McInnes and Dr P.F. Semple for permission to use data from patients under their care.

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