Phenotyping the Spectrum of Traumatic Brain Injury: A Review and Pathway to Standardization

Abstract

It is widely appreciated that the spectrum of traumatic brain injury (TBI), mild through severe, contains distinct clinical presentations, variably referred to as subtypes, phenotypes, and/or clinical profiles. As part of the Brain Trauma Blueprint TBI State of the Science, we review the current literature on TBI phenotyping with an emphasis on unsupervised methodological approaches, and describe five phenotypes that appear similar across reports. However, we also find the literature contains divergent analysis strategies, inclusion criteria, findings, and use of terms. Further, while some studies delineate phenotypes within a specific severity of TBI, others derive phenotypes across the full spectrum of severity. Together, these facts confound direct synthesis of findings. To overcome this, we introduce PhenoBench, a freely available code repository for the standardization and evaluation of raw phenotyping data. With this review and toolset, we provide a pathway towards robust, data-driven phenotypes that can capture the heterogeneity of TBI, enabling reproducible insights and targeted care.

Roadmap for Advancing Pre-Clinical Science in Traumatic Brain Injury

“…This may provide therapeutic targets of specific neuroinflammatory responses across broad therapeutic windows of opportunity. For clinical translation, development of new tools and identification of appropriate patient populations (see Pugh and colleagues, 71 this issue) will all be needed in order to effectively translate research findings in TBI-related neuroinflammation to the clinic. In sum, a number of studies have supported the use of pre-clinical animal models to define and target the chronic inflammatory consequences of TBI from single or multiple injuries, and this area merits considerable additional exploration.…”

Genomic Biomarkers Can Provide a Deeper Understanding of Recurrent Pressure Injuries

“…Phenotyping heterogeneous populations, such as persons with chronic SCI, is complex and challenging to undertake in standard research studies or the clinical point-of-care setting 43 . However, in the present study, the authors used computational phenotyping to derive research-grade phenotypes from clinical data using computer-executable algorithms applied to the complete electronic health records of a moderate population of veterans with SCI 42,44 . By undertaking complete genotypic profiling, they were able to discover interactions between the genetic background and impact of SCI on the phenotypic occurrence of recurrent PIs.…”

Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium

“…However, a post-hoc analysis of converted scores found that the average percentage difference in scores across AVLTs was just 0.052%. This study considered only a limited binary interpretation of lifetime history of TBI, which exists along a spectrum of severity and has distinct phenotypes (37). For example, late effects of TBI are highly variable with injury severity a major factor (e.g chronic memory/learning deficits are expected with severe injury but are uncommon with mild TBI).…”