Patient identification and data preparation

A total of 1131 consecutive patients with all grades of traumatic head injury, admitted to a regional neurosurgical centre from 1 January 1989 until 16 July 1996, comprised the head injury registry data base. For these patients, we attempted to retrieve all baseline admission CT scans from the regional hospitals' radiology departments.

A consultant neuroradiologist read the retrieved CT scans blind to all clinical information, and noted (table 1⇓) the time of the scan; the number, site, and size of any lesions (extradural haematoma (EDH), subdural haematoma (SDH), subarachnoid haemorrhage (SAH), parenchymal contusions, white matter lesions (low or high density), basal ganglia lesions); the presence of a depressed fracture or intracranial air; the presence and amount of any midline shift; whether the ventricles and basal cisterns were compressed, dilated, or normal; the TCDB classification; and finally a simple code to describe the overall appearance of the scan. This code included the following options (table 1⇓): normal appearance; mild focal lesion (for example, small contusion in one area of brain only but the rest of the brain normal); medium focal lesion (for example, several contusions in one or two immediately adjacent areas of brain, or small EDH or small SDH but the rest of the brain normal); mild or moderate diffuse injury (several small contusions or haematomas in several non-adjacent areas of the brain but normal appearance elsewhere); massive focal injury (for example, large EDH or SDH or massive contusions or parenchymal haematoma in one area of the brain); or massive diffuse injury (severe generalised swelling of the brain or many contusions or haematomas in multiple areas). If a follow up scan had been done, and any changes on it were also noted—for example, evidence of evacuation of a mass lesion. Survival at 1 year¹² was used as the clinical outcome measure in the present study, classified according to the Glasgow outcome scale (GOS), where 1=dead, 2=permanent vegetative state, 3=severely disabled, 4=moderately disabled, 5=independent.

Data analyses

We extracted the baseline demographic details of the patients whose scans had been retrieved and reread from the original head injury database.¹² To assess the generalisiblity of our results we compared the demographics of patients whose CT had been found with those of the patients whose CT could not be found using χ² analysis for categorical variables, or two sample t tests for continuous variables, to test for any systematic bias. χ² tests were used to investigate any associations between grade of injury or GCS total score on admission with each of the extra features on the reread CT scans. Grade of injury was categorised as severely injured (defined as GCS 3–8 plus individual component contributions of eye score equal to 1, motor score at most 5, and verbal score at most 2) and minor or moderate injuries (defined as GCS 13–15 and GCS 9–12 or GCS 8 if eye score was>1, motor score>5, and verbal score>2). The GCS was divided into four groups, as is standard practice; GCS 3–5, 6–8, 9–12, and 13–15.

Logistic regression modelling was used to assess the prognostic significance of the features on the reread CT scans and clinical variables. Candidate predictors in the model included baseline demographic variables (age, ISS, GCS, pupil reaction, cause of injury, sex, alcohol status) and simple CT features (haematoma or no haematoma on CT according to the modified Marshall CT classification as recorded in the original head injury database⁵), as well as the additional features available from the reread CT scan. Each variable was initially fitted in a univariate model to screen for prognostic importance, and to decide on the functional form of the relation. Multivariate logistic regression modelling was subsequently used to investigate which of these variables independently predicted survival at 12 months. Standard procedures were used to assess global goodness of fit for final models.¹³

Systematic literature search

We searched MEDLINE and EMBASE from 1966 and 1980 respectively to August 1999 for studies of the classification and association of CT appearance after acute head injury with clinical outcome. We used search terms: (1) computer assisted tomography/or CT; (2) 1 and classification. mp (mp = title, abstract, heading word, trade name, manufacturer's name); (3) 2 and (head injury or brain injury or traumatic head injury or traumatic brain injury). mp; (4) limit 3 to English; (5) 3 and outcome. mp; (6) limit 5 to English language. We also checked references in identified papers. Relevant papers were examined by two people and data extracted on the sample size, year of study, CT variables sought, blinding to clinical baseline variables, outcome measure and timing and result of any analysis on the association of CT with outcome, and whether there had been any validation of any scoring system used to read the CT scan. Studies where the sample size was less than 100 were excluded as these would have limited power to identify valid independent prognostic variables.

Relation between the appearance of the CT scan and injury at baseline

Table 1⇑ summarises the features visible on the 425 reread CT scans. The association between the CT features and GCS (categorised as GCS 3–5, GCS 6–8, GCS 9–12, and GCS 13–15) was very similar to the association of CT features with grade of injury and therefore the detailed results are reported for GCS only (table 2⇓).

Patients with an SDH or SAH, or midline shift, and an abnormal third ventricle had significantly lower GCS scores (and were more severely injured). The TCDB classification was considered as a full six category variable, as well as a more simple focal or diffuse categorisation. The “overall CT appearance” was also considered as a full categorisation (normal; mild focal; mild/moderate diffuse plus minor focal+diffuse; massive focal (assuming some diffuse also) and massive diffuse±focal), as well as a simplified normal/mild/massive and simple normal/abnormal categorisations. All were significantly associated with GCS: a focal or the more severe diffuse injuries (diffuse II, III, and IV) for the TCDB classification; and the mild/moderate diffuse, or massive focal and massive diffuse for the “overall CT appearance” had significantly lower GCS scores. The presence of an EDH was not associated with GCS total or the severity of injury on admission.

Relation between the appearance of the baseline CT scan and clinical outcome: univariate analysis

There were 20 of the 425 patients whose GOS was not recorded, or were lost to follow up at 12 months, of whom nine did have a 24 month outcome recorded and therefore their 12 month survival status could be inferred, resulting in 414 reread patients available for the regression analysis. Survival or death are the only outcomes for which this back projection method can be applied, as for all other states (grades of disability) patients could change between time points.

In the univariate analyses, age, total GCS on admission, and ISS were significant prognostic variables. Both total GCS on admission and ISS were linearly related to outcome (linearly increasing trend for GCS, and a linearly decreasing trend for ISS). Age was modelled as a covariate (zero up to age 50, then equal to age minus 50 thereafter) as in the previously validated model.¹² Significant positive associations were found between survival at 12 months and pupil score; the presence of an EDH; and focal versus diffuse injury on the TCDB classification. Negative associations were found with grade of injury (classified as severe or minor/moderate); whether or not the patient had been receiving medication; the presence of an SDH or SAH; midline shift; abnormal third ventricle; diffuse categories III and IV v a focal injury on the TCDB classification; and “overall appearance” of the scan (normal/mild/moderate versus massive focal or massive diffuse injury).

The patients' sex, alcohol status, cause of injury, and modified Marshall CT classification (haematoma versus no haematoma, previously extracted from the radiologist's report) were not significant prognostic factors for survival at 12 months in the univariate analysis.

Multivariate analysis to identify prognostic variables

A multivariate model was then constructed to determine what factors independently predicted whether the patients would survive to 12 months after injury. This model took account of the results of the univariate analyses as well as important biological considerations to keep the model as simple yet as effective to use in clinical practice as possible.¹² Patients with missing values for any of the variables considered were omitted from this analysis (391 of the potential 425 reread patients were therefore available). The contribution to the reduction in deviance of each variable added sequentially to a model containing other independently significant predictors was assessed and a decision of whether to retain that variable in the model made on this basis, as well as its individual p value. Age, the admission total GCS, and pupil reaction were rarely missing and remained as independent variables in the model. Each of the variables from the reread CT scan were then considered to determine what they could add to this model.

The final best predictive model included age, GCS, pupil reaction, the presence of subarachnoid blood, and the “overall appearance” of the CT scan. The odds ratios (and associated 95% confidence intervals (95% CIs)) for predicting survival at 12 months for each significant prognostic variable are summarised in table 3⇓. These were significant independent positive associations between survival and GCS and pupil reaction; and significant negative associations between survival and increasing age, the presence of subarachnoid blood, and the “overall appearance” of the CT scan (as massive focal or diffuse injury v normal/mild/moderate injury).

Systematic literature search

The systematic literature search of EMBASE 1980–99 identified 72 references on CT, classification, and head injury (52 English language) and 19 references on CT, classification, head injury, and outcome (13 English language): the corresponding figures for MEDLINE were 26 (17) and nine (5). Of these, four publications (of n>100) described a novel CT feature recognition or classification^5–,⁸ and two described further details of the TCDB classification (table 4⇓).^9,10 There were nine other publications but they either had sample size less than 100,^3,14–,¹⁶ or made no reference to long term outcome,¹⁷ or were concerned with only one feature of trauma,² or considered only patients with very mild head injury,¹⁸ or was a duplicate publication,¹⁹ or had mixed clinical and CT variables in one classification,¹ and were therefore excluded. The four studies included had a mean sample size of 418, none stated whether the CT readers were blind to the clinical data, only two used multivariate analyses to correct for association between CT appearance and clinical baseline variables (to test the independent predictive value of CT), and none of the individual classifications described have been validated in a relevant, independent data set. All the included studies only contained patients with an admission GCS of 8 or less, and therefore are not applicable to the broader range of patients with head injury admitted to hospital. For example, although the patients for whom we were able to find and reread the CT scans for the present study were more severely injured than the generality of the patients in our head injury database, there were still 38% with CGS 9–15 (compared with 43% of the whole head injury database). Thus our “overall appearance” classification is applicable to all patients with head injury admitted to hospital, not just those of GCS of 8 or less.