Cerebral fractional anisotropy score in trauma patients: A new indicator of white matter injury after trauma

OBJECTIVE. Previous studies evaluating quantitative cerebral white matter diffusion anisotropy indexes have shown alteration in patients after trauma. To date, no clinically applicable scale exists by which to gauge and test the relevance of these findings. We propose the cerebral fractional anisotropy score in trauma (C-FAST) as an index of white matter injury, and we correlate C-FAST with several predictor and outcome variables. MATERIALS AND METHODS. Fifteen patients were randomly selected from the trauma surgery service. Thirty control patients were randomly selected from the emergency department. All patients were subjected to MRI evaluation, including a diffusion-weighted sequence. Data extracted from the record of each subject included Glasgow Coma Scale, revised trauma score, Abbreviated Injury Scale, initial head CT results, patient disposition, length of hospital stay, and length of stay in intensive care unit. Region of interest measurements were made in fractional anisotropy maps in each of 12 white matter regions. Univariate statistics and a two-tailed t test were performed on the raw fractional anisotropy data. Data were then dichotomized using thresholds from univariate statistics. A C-FAST score was devised from the dichotomized data. Logistic regression analyses were performed among the C-FAST, outcome, and predictor data. RESULTS. Good correlation was noted between the C-FAST and death, hospital stay greater than 10 days, and intensive care unit stay greater than 5 days. Correlation with discharge to rehabilitation facility was good when adjusted for age and sex. Glasgow Coma Scale, revised trauma score, and Abbreviated Injury Scale show good correlation as predictors of a critical C-FAST. CONCLUSION. The C-FAST is a promising index derived from MRI diffusion fractional anisotropy measurements that shows successful correlation with outcome and predictor variables. A larger investigation is needed to verify the validity and stability of the correlations.