Novel risk stratification score for patients presenting with acute Cerebral Venous Sinus Thrombosis

摘要 脑静脉窦血栓形成(CVST)是一种卒中亚型，每百万人中有15人发生 年。CVST主要影响儿童和年轻人，特别是育龄年轻妇女和 他们有高凝的危险。最常见的临床症状包括头痛。 (90%)和癫痫(40%)。在更严重的情况下，局灶性缺陷、抑郁的精神状态和进展为 可能会发生昏迷。全身抗凝是治疗的主要手段，用于预防 血栓形成，同时促进再通。相当一部分患者可能会进一步恶化，至少 20%的人死亡或致残，其中最高的死亡率发生在疾病的急性期。这 流行病学形势强调了替代急性治疗方法的潜在需要来帮助这一点 高危劳动年龄CVST人群。 替代疗法，如使用新的抗凝剂和/或静脉抗血小板药物是有希望的 难治性患者在疾病过程的早期建立的选择。然而，考虑到潜在的不利因素 效果和成本上升，这是临床决策工具的优化，使我们能够准确地对高风险进行分层 CVST患者，代表着强制性的第一步。为了改善这一遴选过程，研究工作已经 重点是识别临床和放射学预测因素，构建可靠的预测模型。 不幸的是，初始分层得分未能证明足够的准确性，无法应用到 临床实践由于模型建立和选择过程中的几个概念性限制。详细描述 将在本提案的意义部分介绍先前研究的严谨性。 在对我们在爱荷华大学的CVST队列进行了彻底的可行性分析后，我们渴望提出一个2- 一年的研究，以开发和验证一种新的、简单的、可重复的预测评分，该评分将迅速对CVST进行分层 预后不佳的患者。该量表将建立在出现的新的临床和放射生物标记物上 在两个大型CVST队列的早期病理生理阶段。我们还将利用创新的统计方法 机器学习方法，以优化我们的模型选择和规模预测性能。最后，我们会 在一个独立的患者子集中评估我们的模型，以进一步验证、改进和推广。 该项目的成功完成将优化继发前高危CVST患者的早期选择 伤痛会扩大并延续下去。在实施之前，在更大的多中心对规模进行外部验证 验证研究将是打开机会之窗比较新产品的第一个必要关键步骤 通过RTCS对大量患有其他疾病的年轻患者进行的针对当前临床实践的治疗 惨淡的结局。

Abstract Cerebral Venous Sinus Thrombosis (CVST) is a stroke subtype with an incidence of 15 people per million per year. CVST primarily affects children and young adults, especially young women of child-bearing age and those who are at risk for hypercoagulability. The most common clinical symptoms at presentation include headaches (90%) and seizures (40%). In more severe cases, focal deficits, depressed mental status and progression to coma might occur. Systemic anticoagulation is the mainstay of the treatment, which is used for preventing thrombus while facilitating recanalization. A substantial subset of patients may further deteriorate and at least 20% die or become disabled, with the highest mortality occurring during the acute phase of the illness. This epidemiological landscape emphasizes the potential need of alternative acute therapeutic approaches to aid this high-risk working-age CVST population. Alternative therapies such as the use of new anticoagulants and/or Intravenous antiplatelet agents are promising options in refractory patients when instituted early in the disease process. However, given the potential adverse effects and elevated cost, an optimization of clinical decision tools that permit us to accurately stratify high-risk CVST patients, represents the mandatory first step. To ameliorate this selection process, research efforts have focused in the identification of clinical and radiological predictors to build a reliable prediction model. Unfortunately, initial stratification scores have failed to demonstrate enough accuracy to be implemented into clinical practice due to several conceptual constraints during model building and selection. A detail description of the rigor of previous research will be presented in the significance section of this proposal. After a thorough feasibility analysis of our CVST cohort at the University of Iowa, we are eager to propose a 2- year study to develop and validate a new, simple, reproducible predictive score that will promptly stratify CVST patients with poor outcome. The scale will be built on novel clinical and radiological biomarkers that appear during early pathophysiological stages from two large CVST cohorts. We will also utilize innovative statistical machine learning methodology to optimize our model selection and scale predictive performance. Finally, we will evaluate our model in an independent subset of patients for further validation, refinement and generalizability. Successful completion of this project will optimize the early selection of high-risk CVST patients before secondary injury expands and perpetuates. Prior to implementation, an external validation of the scale in a larger multicenter validation study will represent the first necessary critical step to open a window of opportunity to compare new therapies against current clinical practice through RTCs in a significant group of young patients with otherwise dismal outcome.