A Cross-Study Analysis for Reproducible Sub-classification of Traumatic Brain Injury

Objective: To identify reproducible sub-classes of traumatic brain injury (TBI) that correlate with patient outcomes. Methods: Two TBI datasets from the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System were utilized, Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot and Citicoline Brain Injury Treatment Trial (COBRIT). Patients included in these analyses had closed head injuries with Glasgow Comas Scale (GCS) scores of 13–15 at arrival at the Emergency Department (ED). Sparse hiearchical clustering was applied to identify TBI sub-classes within each dataset. The reproducibility of the sub-classes was evaluated by investigating similarities in clinical variable profiles and patient outcomes in each sub-class between the two datasets, as well as by using a statistical metric called in-group proportion (IGP). Results: Seven TBI sub-classes were identified in the first dataset. There were between-class differences in patient outcomes at 90 days (Glasgow Outcome Scale Extended (GOSE): p < 0.001) and 180 days (Trail Making Test (TMT): p = 0.03). Four of seven sub-classes were reproducible in the second dataset with very high IGPs (94, 100, 99, 97%). Seven TBI sub-classes were also identified in the second dataset. There were significant between-class differences in patient outcomes at 180 days (GOSE: p = 0.024; Brief Symptom Inventory (BSI) p = 0.007; TMT: p < 0.001). Three of seven sub-classes were reproducible in the second dataset with very high IGPs (100% for all). Conclusions: Reproducible TBI sub-classes were identified across two independent datasets, suggesting that these sub-classes exist in a general population. Differences in patient outcomes according to sub-class assignment suggest that this sub-classification could be used to guide post-TBI prognosis.