The primary purpose of this research was to describe traumatic brain injury (TBI) severity classification methods utilized in retrospective data in a large sample of patients within a national data bank, the National Trauma Data Bank (NTDB). Two common classification methods, the Glasgow Coma Scale (GCS) and the Abbreviated Injury Scale (AIS) head score, were compared in performance and their differences were analyzed. Secondary objectives were to compare demographic and clinical characteristics of TBI patients classified using the two methods, GCS and AIS of the head.
Methods:
Using correlational and descriptive statistics, this study examined two TBI severity classification methods across a large TBI patients sample (N=56,131), who were treated at level I and level II trauma centers in the United States and included in the 2010 National Sample Program (NSP) of the National Trauma Data Bank (NTDB®).
Results:
The study population was 67% male, 67% non-Hispanic white, treated most often in trauma centers in the South (38%), with blunt trauma (93%) and from non-MVC’s (56%). Observation of the AIS classification system shows that it tends to over-score TBI severity compared to the GCS classification. The methods have a weak inverse correlation that is significant at p<0.001.
Conclusion:
The study addressed the difficulties and inconsistencies associated with categorizing TBI severity when analyzing retrospective data, especially in the moderate TBI population. GCS is the most commonly used variable to classify severity in retrospective data with the head AIS variable used when GCS is missing. However, the relationship between the two scales is relatively unknown. Results show that AIS and GCS are more closely related for severely brain injured patients, however, in cases of mild and moderate injury, AIS is less predictive of GCS. This study reinforced the need for additional TBI severity classification methods, such as neuroimaging techniques and the use of biomarkers in brain injury classification. The study also encourages nurses to take advantage of the vast amount of retrospective, big data sets available to answer our many clinical questions and to ultimately improve the outcomes of our patients. However, the study demonstrates the methodological issues that one can encounter when utilizing such data.