Traumatic brain injury (TBI) has the highest incidence of all neurological disorders, and is a major source of death and long-term disability worldwide. Yet the seemingly simple act of assessing TBI severity remains a major, unresolved challenge. Central to this problem is that TBI encompasses a broad class of heterogeneous injuries superimposed on the intrinsic complexity of the mammalian brain. From a biological perspective, TBI impacts multiple scales of analysis from molecular and cellular pathways to neural networks, and from behavior to functional outcomes. These domains span temporal scales that vary from milliseconds to years. Acute assessment of clinical TBI is shifting from reliance on crude categorization of ‘mild’, ‘moderate’, and ‘severe’ TBI to a more nuanced multimodal classification that integrates Clinical assessments, Biomarkers, Imaging, and injury Modifiers (CBI-M) into a single framework. It is critical that the preclinical field adopts a similar approach for experimental TBI in order to improve preclinical fidelity and translational potential. In preclinical TBI, there are unrealized opportunities to synthesize data from a constellation of early functional scores, biomarkers, imaging, and other features to more accurately determine injury severity. We review historic and ongoing efforts to define injury severity in preclinical TBI and discuss the need for a data-driven integrative injury severity index with an eye toward bench-to-bedside translation.
Ferguson et al. (Tue,) studied this question.