Toward a New Multi-Dimensional Classification of Traumatic Brain Injury: A Collaborative European NeuroTrauma Effectiveness Research for Traumatic Brain Injury Study

Abstract

Traumatic brain injury (TBI) is currently classified as mild, moderate or severe TBI by trichotomizing the Glasgow coma scale (GCS). We aimed to explore directions for a more refined multidimensional classification system. For that purpose, we performed a hypothesis-free cluster analysis in the CENTER-TBI database: a European all-severity TBI cohort (n=4509). The first building block consisted of key imaging characteristics, summarized using principal component analysis from 12 imaging characteristics. The other building blocks were demographics, clinical severity, secondary insults, and cause of injury. With these building blocks, the patients were clustered into four groups. We applied bootstrap resampling with replacement to study the stability of cluster allocation. The characteristics which predominantly defined the clusters were injury cause, major extracranial injury, and GCS. The clusters consisted of 1451, 1534, 1006, and 518 patients, respectively. The clustering method was quite stable: the proportion of patients staying in one cluster after resampling and reclustering was 97.4% (95% CI:85.6%-99.9%). These clusters characterised groups of patients with different functional outcomes: from mild to severe, 12%, 19%, 36%, and 58% of patients had unfavourable sixmonth outcome. Compared to the mild and the upper intermediate cluster, the lower intermediate and the severe cluster received more key interventions. To conclude, four types of TBI patients may be defined by injury mechanism, presence of major extracranial injury and GCS. Describing patients according to these three characteristics could potentially capture differences in etiology and care pathways better than with GCS only.

Epidemiology of Chronic Effects of Traumatic Brain Injury

“…While each approach has demonstrated strengths, they also have weaknesses 169 . For instance, an analysis of the CENTER-TBI European cohort study 170 used a clustering approach with injury information and physiological data from the first 2 weeks after injury, and found four severity-focused clusters that were informed not only by the GCS, but also by the mechanism of injury and presence of extracranial injury. Thus, more work is needed to use the diverse existing data available to develop a taxonomy of TBI that incorporates injury mechanisms and early biomarkers to develop standard phenotypes and improve symptom trajectories (see Pugh part of our roadmap four trackable opportunities for next steps that include specific action items (Table 1).…”

Assessing the Severity of Traumatic Brain Injury—Time for a Change?

“…Current international efforts within the InTBIR (International Traumatic Brain Injury Research Initiative) collaboration [43], such as CENTER-TBI and TRACK-TBI, will undoubtedly improve assessment of acute TBIs and the contribution of different factors that affect clinical outcome, as already published [44]. Yet, the challenges outlined in this paper will likely face us for many years to come due to a variety of factors including competing interests across different sectors of society (medical, ethical, legal, political), nomenclature biases including entrenchment of use of certain severity labels, as well as the complexity of TBI and the lack of widely available reliable biomarker panels and MRI techniques.…”

Proceedings of the First Curing Coma Campaign NIH Symposium: Challenging the Future of Research for Coma and Disorders of Consciousness

“…• Practical considerations: the group raised the issue of feasibility, relevance, and implementation of endotypes in the clinical setting and in the coma science community. • There is a nonstatic nature of features within endotypes, in part, because they combine different biomarkers [24,25] or biological processes into a single determinant factor (e.g., seizure activity, covert consciousness). To adjust for this dynamism when endotyping patients, researchers should implement clustering algorithms and be aware that a patient's changing status may lead to endotype shifts [10,21,[26][27][28][29][30].…”