A Configurational Analysis of Risk Patterns for Predicting the Outcome After Traumatic Brain Injury

Exploring relationships between admission variables and outcome using regression models has been the focus of Traumatic Brain Injury (TBI) research. Although practical and well established, these approaches do not evaluate interactions between predictors. We therefore applied a set-theoretic logical analysis to the Corticosteroid Randomization after Significant Head Injury (CRASH) trial database. Complete data analysis of 6945 patients demonstrated 9 different configurations of admission variables were sufficient for favorable outcome in 87.5% of all cases and explained 57% of favorable outcomes (moderate disability or good outcome). We also evaluated the contrasting configurations for unfavorable versus favorable outcome. Results are largely in line with findings of previous studies however the influence of age fell behind GCS components, which is unexpected. Specifying a combination of admission parameters that are likely to translate into a given clinical outcome is appealing from a clinician's perspective therefore our results have considerable translational value.