BrainIT: a trans-national head injury monitoring research network

Chambers, I. R.; Barnes, J.; Piper, Ian; Citerio, Giuseppe; Enblad, Per; Howells, Timothy; Kiening, Karl; Mattern, Julia; Nilsson, Pelle; Ragauskas, Arminas; Sahuquillo, Juan; Yau, Y. H.

doi:10.1007/3-211-30714-1_2

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…One form of this database may grow from the NINDS effort to define a common set of data elements (www.nindscommondataelements.org/CommonForms.aspx) with additional TBI-specific elements added using a critical evaluation of existing core datasets and the recently established BrainIT group (Chambers et al, 2006; Piper et al, 2003). If these items were to be collected from every future clinical trial in TBI and placed in a common data repository, a contemporaneous equivalent to the IMPACT database could be created.…”

Section: Roundtable Discussionmentioning

confidence: 99%

Classification of Traumatic Brain Injury for Targeted Therapies

Saatman

Duhaime

Bullock

et al. 2008

Journal of Neurotrauma

943

684

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The heterogeneity of traumatic brain injury (TBI) is considered one of the most significant barriers to finding effective therapeutic interventions. In October, 2007, the National Institute of Neurological Disorders and Stroke, with support from the Brain Injury Association of America, the Defense and Veterans Brain Injury Center, and the National Institute of Disability and Rehabilitation Research, convened a workshop to outline the steps needed to develop a reliable, efficient and valid classification system for TBI that could be used to link specific patterns of brain and neurovascular injury with appropriate therapeutic interventions. Currently, the Glasgow Coma Scale (GCS) is the primary selection criterion for inclusion in most TBI clinical trials. While the GCS is extremely useful in the clinical management and prognosis of TBI, it does not provide specific information about the pathophysiologic mechanisms which are responsible for neurological deficits and targeted by interventions. On the premise that brain injuries with similar pathoanatomic features are likely to share common pathophysiologic mechanisms, participants proposed that a new, multidimensional classification system should be developed for TBI clinical trials. It was agreed that preclinical models were vital in establishing pathophysiologic mechanisms relevant to specific pathoanatomic types of TBI and verifying that a given therapeutic approach improves outcome in these targeted TBI types. In a clinical trial, patients with the targeted pathoanatomic injury type would be selected using an initial diagnostic entry criterion, including their severity of injury. Coexisting brain injury types would be identified and multivariate prognostic modeling used for refinement of inclusion/exclusion criteria and patient stratification. Outcome assessment would utilize endpoints relevant to the targeted injury type. Advantages and disadvantages of currently available diagnostic, monitoring, and assessment tools were discussed. Recommendations were made for enhancing the utility of available or emerging tools in order to facilitate implementation of a pathoanatomic classification approach for clinical trials.

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Section: Roundtable Discussionmentioning

confidence: 99%

Classification of Traumatic Brain Injury for Targeted Therapies

Saatman

Duhaime

Bullock

et al. 2008

Journal of Neurotrauma

943

684

View full text Add to dashboard Cite

show abstract

“…Use of data standards is essential for any successful implementation of a data collection system designed to reuse patient data. Many systems allow reuse of patient care data, 6,7,10,12,13,18,[27][28][29][30][31][32][33][34][35][36] but with rare exceptions 30,36 most examples consist of institution-specific approaches that do not use standards and thus lack the broad interoperability required for multicenter trials. Multicenter trials require data from different sites to be submitted to a central data center, with whom the site's relationship may exist for only a single trial; for reuse of patient care data to be feasible, data collection methods must be easy to implement and use, and must minimize disruption at the clinical site.…”

Section: Introductionmentioning

confidence: 99%