Toward new classification criteria for mild and moderate TBI by a data-inclusive cross-study analysis using FITBIR

项目总结/摘要 创伤性脑损伤（TBI）是美国死亡和残疾的主要原因之一， 国际吧在美国，每年估计有170万例TBI，导致52，000人死亡， 275，000次住院治疗和1，365，000次急诊。这些TBI导致大量 对许多TBI患者产生负面影响。目前，还没有治疗方法可以在 急性损伤后的时间段，已被证明可以改善患者的预后。有一个 无可否认，需要有效的治疗TBI患者谁可能发展长期的TBI后， 赤字TBI领域中的主要缺点是不能根据TBI患者的特征对TBI患者进行准确分类。 患者期望的结果。目前的分类系统使用广泛的标准将患者归类为“轻度”， “中度”或“重度”TBI类别。然而，这些标准通常允许非常不同的患者 一个人，有一个人，有一个人， 症状，被归类为相同的TBI组。目前的分类结果是， 相同，例如“轻度”TBI，具有实质上可变的结果。我们的研究是对 FITBIR中包含的大型数据集，旨在为以下患者开发更精确的分类系统： 经历了与预期患者结局相关的TBI。为了使分类系统 临床医生和研究人员使用的实用数据，通常在最初的患者 将进行评估。可能预测患者结局的因素，因此将考虑 包括在细化分类系统有关的神经系统症状后立即TBI，结果 在初始医学评价时，既往TBI的存在和特征，病史，神经病学，和 精神障碍、TBI的机制和患者的社会人口统计学。更精确的TBI分类 这项研究将产生一个系统，该系统将告知临床医生如何积极地开康复处方 治疗，将允许更准确地说明患者的结果，并将有助于确定纳入 以及未来TBI治疗临床试验的排除标准。

Project Summary/Abstract Traumatic brain injury (TBI) is amongst the leading causes of death and disability in the United States and Worldwide. Each year in the United States, there are an estimated 1.7 million TBIs, resulting in 52,000 deaths, 275,000 hospitalizations, and 1,365,000 Emergency Department visits. These TBIs result in substantial negative impact to many individuals with TBI. Currently, there are not treatments that can be delivered in the acute post-injury time period that have been shown to result in improved patient outcomes. There is an undeniable need for effective treatments for patients with TBI who are likely to develop prolonged post-TBI deficits. A major shortcoming in the TBI field is the inability to accurately classify a patient with TBI according to that patients expected outcomes. Current classification systems use broad criteria to assign patients to “mild”, “moderate” or “severe” TBI categories. However, these criteria often allow for patients who are very different from one another, who have had very different injuries, and who have very different post-injury signs and symptoms, to be classified into the same TBI group. Current classification results in patients who are classified the same, e.g. as “mild” TBI, to have substantially variable outcomes. Our research, a secondary analysis of large datasets contained within FITBIR, aims to develop a more precise classification system for patients who have experienced a TBI that correlates with expected patient outcomes. To make the classification system practical for use by clinicians and researchers, data that are typically available at the time of the initial patient evaluation will be utilized. Factors that might be predictive of patient outcomes and will thus be considered for inclusion in the refined classification system relate to neurologic symptoms immediately following TBI, findings at the initial medical evaluation, presence and characteristics of prior TBIs, history of medical, neurologic, and psychiatric disorders, mechanism of TBI, and patient socio-demographics. The more precise TBI classification system that will result from this research will inform clinicians on how aggressively to prescribe rehabilitative therapies, will allow for more accurate prognostication of patient outcomes, and will help to determine inclusion and exclusion criteria for future clinical trials of TBI therapies.