INCF (International Neuroinformatics Coordinating Facility) coordinated the establishment of an informatics platform for acquisition, storage and analysis of CDE-based clinical data. The goal was to develop a next generation open standards-based platform to support advanced large-scale analytics and model building. Such a platform also provides a model for future clinical studies on brain diseases and disorders. This common open international platform is a resource for future TBI studies. The resulting analytical environment provides large-scale data access, enabling ‘big data’ science that supports this and other work packages, and allowing large-scale data analysis and modelling to be leveraged for TBI questions in ways that have not previously been possible. Comprehensive computational models for TBI were derived from this approach.
Several studies report genetic associations with TBI outcome, but their relevance is limited by small sample size and by unsophisticated analysis, which takes little account of predicted outcome. Further conventional common disease-common variant (CDCV) approaches have problems and serve TBI poorly, since the admission phenotype is heavily influenced by injury exposure, rather than host response (which is the substrate for genetic modulation). Finally, such approaches take no account of pre-test probabilities of significant gene-phenotype relationships based on known disease biology, clustering of gene effects in biological pathways or rich serial measures of phenotype routinely recorded in intensively monitored TBI subjects.
Conventional MRI is widely thought to be superior to CT for characterising TBI pathology. However, a recent study only compared late (~2 week) MRI with CT performed upon initial presentation, and rigorous comparisons of contemporaneously collected CT and MRI are lacking. Such studies are essential for establishing the role of MRI in routine imaging, especially of mild TBI, in particular as there is good evidence that MR visibility of lesions in TBI changes with time. Advanced MRI (Diffusion Tensor Imaging [DTI], Resting state fMRI [rs fMRI], and susceptibility weighted imaging [SWI]) offer opportunities for better detection, characterization and quantification of injury. SWI and DTI abnormalities consistent with traumatic axonal injury occur in 30% of patients without abnormalities on CT and DTI imaging provides exquisite characterisation of lesion evolution, and delivers important pathophysiological insights. In the subacute phase MR imaging offers a great potential for understanding and tracking disease processes, prognosticating cognitive recovery, and mapping covert cognition in disorders of consciousness. In the chronic phase, the ability to relate neuronal loss and white matter injury to cognitive measures, to vulnerability for depression and to functional outcome may be key to understanding impairment following injury and to the development of strategies for rehabilitation which may be tailored to patients’ needs. Past studies have poorly addressed the changes in structural and functional connectivity that underpins these deficits, and the full potential of advanced MR imaging in TBI has not been ascertained due to small sample sizes (the largest study involves less than 150 patients), selection bias and inconsistent use of advanced imaging.
Blood biomarkers have recently received much attention for a possible role in diagnosing mild TBI, tracking disease progression, and predicting outcome in TBI, but studies have generally involved small patient numbers, with no rigorous assessment of the added diagnostic or predictive power over and above that of established techniques/models in appropriately large patient numbers with good quality acute data collection and outcome documentation. Further, there is no evidence that these approaches can make the key transition from technically demanding research tools to robust clinical assets that can be used in everyday practice. There is a substantial need for well-established laboratory markers that can be used for diagnosis, disease monitoring and prediction in TBI, replicating (for example) the use of troponin in acute myocardial infarction. Candidate biomarkers which we will initially target include GFAP, S100, NSE and UCHL-1.
Comparative effectiveness research provides a promising framework to identify best practices and improve outcome after TBI. CER is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care. The purpose of CER is to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve health care at both the individual and population levels.
Until recently randomized controlled trials (RCTs) were considered the gold standard for investigating benefit of interventions. RCTs generally employ strict enrolment criteria in order to study the investigational intervention in the "cleanest" setting. The downside of this approach is that results are only valid in such selected subpopulations and that generalizability to the real world context is limited. Moreover, trials are expensive and in the heterogeneous field of TBI results have been disappointing. A basic concept of CER is to study differences in care and outcome in observational studies, thus turning natural variability into an asset. In CENTER-TBI, we will exploit the existing heterogeneity in structure, process and outcome to compare treatments and interventions that are standard practice in some centres and countries but not in others. Natural links exist between CER and individualized approaches, since CER aims to identify the best treatment for the individual patient, with a specific type of injury, severity, co-morbidities and other aspects that determine optimal treatment. We see a great potential for CER in TBI because of various unique features: First, there are large between-centre and between-country differences in both outcome and management. Second, robust risk adjustment models have been developed specifically for TBI, providing the possibility to adjust for patient characteristics that affect outcome. Third, advanced statistical models, including random effect models, are available to analyze differences between centres.
Outcome after TBI is complex and multidimensional, and includes functional status, generic and disease-specific health related quality of life (HRQoL), cognitive performance, and emotional and psychosocial adjustment. CENTER-TBI examined the time course, correlated predictors and moderators of different aspects of outcome, explored interdependencies and tailor outcome assessment after TBI. Specific topics addressed include the relative applicability to different (severity) strata, the incidence and development of PTSD in the three strata, relationships between outcome and TBI characterization (MRI, biomarkers, sociodemographic and psycho-social data, etc.). We aimed to develop a multidimensional outcome classification.
Full assessments, including neuropsychological testing, were performed as a cross-sectional study at the primary outcome time points (3 months for ER stratum, 6 months for admission and ICU strata). Furthermore, all patients undergoing longitudinal MRI studies received repeated neuropsychological testing. Longitudinal follow up across multiple time points is based upon the use of an abbreviated subset of assessments, which could be administered by postal questionnaire or web-based completion.