Despite many (valid) criticisms, the initial post-resuscitation Glasgow Coma Scale (GCS) score remains the most widely used and perhaps best replicated scale employed in for the assessment of head trauma. Problems with this type of scale is that it is an ordinal scale that is non parametric (i.e. does not represent precise measurements of discrete quantities), it is non-linear, and it is not an interval scale, so that for example, a decrease of 2 points in one parameter is not necessarily equal to a decrease in 2 points of another 1).
Thus, performing mathematical manipulations (e.g. adding components, or calculating mean values), while often done, is not statistically sound 2).
There are a number schemes to stratify the severity. Any such categorization is arbitrary and will be imperfect.TBI patients are often influenced by ethanol, which in itself can attenuate the level of consciousness.
A simple system based only on GCS score is as follows:
● GCS 14–15 = Mild traumatic brain injury.
● GCS 9–13 = Moderate traumatic brain injury.
Classifying TBI into these three subgroups has substantial limitations: within each of the three sub-groups there exists a large amount of heterogeneity in patient and injury characteristics and wide variability in post-TBI patient outcomes. For example, a patient who had loss of consciousness for 20 minutes, amnesia for 20 hours, and a GCS score of 13 is categorized as having had a mild TBI (mTBI). Similarly, a patient who had no loss of consciousness, no amnesia, and a GCS score of 15 but had a few minutes of blurred vision and nausea following the head injury is also considered to have had a mTBI. Despite these two patients both being classified as having had “mTBI”, one might expect that the severity and outcomes associated with their injuries may be different. A more refined sub-classification structure for defining the severity of TBI would be useful if the new sub-categories of TBI severity correlated with patient outcomes. Furthermore, a more refined sub-classification of TBI would be most clinically useful if information available at the time of the initial patient evaluation were sufficient for determining an individual’s sub-group. A classification structure with these characteristics could guide patient management decisions and inform appropriate counseling with respect to prognosis 5).
The classification of head injured patients is more difficult than that for most other disease processes. The quantum of data to be embedded into each patient's grading code depends on the purpose to which that grading is used. If the information is merely required for broad epidemiological surveys, it may be confined to a double rubric which represents the most significant diagnostic component and an arbitrary index of associated severity. For this purpose, diagnostic severity grading is possible provided the task is delegated to experienced members of the neurosurgical team. If the grading is to be used in attempts to compare one patient group with another or for predictions of complications or outcome, a more detailed data-set is required. This may be accomplished with the use of multiple ICD diagnostic codes but assignations of severity to each diagnostic component requires very subjective judgement. Such an approach is unlikely to be successful and the only alternative is to define a data-set of “pure” information which includes all the relevant clinical, radiological, and operative findings without resorting to artificial data compression by using potentially misinterpretable deduced codes 6).
Adult traumatic brain injury
Acute traumatic brain injury
Chronic traumatic brain injury
CENTER-TBI should provide novel multidimensional approaches to TBI characterization and classification, evidence to support treatment recommendations, and benchmarks for quality of care. Data and sample repositories will ensure opportunities for legacy research.
Comparative Effectiveness Research provides an alternative to reductionistic clinical trials in restricted patient populations by exploiting differences in biology, care and outcome to support optimal personalized patient management 7).