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

急性脑静脉窦血栓形成患者的新风险分层评分

基本信息

批准号：
10592974
负责人：
Santiago Ortega-Gutierrez
金额：
$ 7.79万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10592974
关键词：
Activated Partial Thromboplastin Time measurement Acute Address Admission activity Adverse effects Affect Age Alternative Therapies Anticoagulants Anticoagulation Antiplatelet Drugs Biological Biological Markers Blood Cell Count Cerebral Edema Cerebrum Child Classification Clinical Clinical/Radiologic Coagulation Process Coma Dependence Depressed mood Deterioration Development Disabled Persons Disease Early identification Edema Epidemiology Ethnic Origin Female of child bearing age Glasgow Coma Scale Goals Headache Heparin Hospitals Hour Image Incidence Infarction Injury Intracranial Hemorrhages Intracranial Sinus Thrombosis Intravenous Iowa Laboratories Lasso Left Life Logistic Regressions Lymphocyte Magnetic Resonance Imaging Measures Metabolic Methodology Methods Modeling Morbidity - disease rate Nature Neurologic Examination Outcome Patients Performance Persons Phase Physiological Population Predictive Value Process ROC Curve Randomized, Controlled Trials Refractory Regression Analysis Reproducibility Research Risk Sampling Sampling Studies Seizures Specific qualifier value Specificity Stratification Stroke Symptoms Techniques Testing Therapeutic Thrombophilia Thrombus United States National Institutes of Health Universities Validation Venous Work X-Ray Computed Tomography advanced analytics alternative treatment analytical method clinical biomarkers clinical practice cohort conventional therapy cost cytotoxic design efficacy evaluation follow-up functional outcomes gradient boosting high risk high risk population imaging biomarker inflammatory marker innovation machine learning method mental state model building mortality neuroimaging marker neutrophil novel novel marker novel therapeutics patient subsets predictive modeling prevent productivity loss prognostic prognostic model prospective regression trees response risk stratification sex statistical and machine learning tool validation studies venous sinus young adult young woman

项目摘要

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.

抽象的脑静脉窦血栓形成（CVST）是中风子类型，每百万人为每百万人为15人年。 CVST主要影响儿童和年轻人，尤其是年龄的年轻女性，谁有高凝性的风险。演讲中最常见的临床症状包括头痛（90％）和癫痫发作（40％）。在更严重的情况下，局灶性缺陷，精神状况抑郁和发展为可能会发生昏迷。全身性抗凝治疗是该治疗的中流血栓同时促进重新定性。大量患者可能会进一步恶化，至少可能 20％死亡或残疾，死亡率最高，在疾病的急性阶段发生。这流行病学景观强调了替代性急性治疗方法的潜在需要高风险的工作年龄CVST人群。替代疗法，例如使用新抗凝剂和/或静脉注射剂在疾病过程初期建立的难治性患者的选择。但是，考虑到潜在的不利效果和成本提高，优化临床决策工具，使我们能够准确地分层高风险 CVST患者代表强制性的第一步。为了改善这一选择过程，研究工作已有专注于鉴定临床和放射学预测因子以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精度无法实现临床实践是由于模型构建和选择过程中的几个概念限制而引起的。详细说明该提案的重要性部分将提出先前研究的严谨性。在对爱荷华大学的CVST队列进行了彻底的可行性分析之后，我们渴望提出2-- 一年的研究，以开发和验证一个新的，简单，可再现的预测评分，该评分将立即对CVST进行分层预后差的患者。该量表将建立在出现的新型临床和放射生物标志物上在早期的病理生理阶段，来自两个大型CVST队列。我们还将利用创新的统计机器学习方法可以优化我们的模型选择和规模预测性能。最后，我们会的在独立的患者子集中评估我们的模型，以进一步验证，简化和普遍性。该项目的成功完成将优化次要之前的高风险CVST患者的早期选择伤害扩大并永久存在。在实施之前，在较大的多中心中对量表的外部验证验证研究将代表打开机会窗口以比较新的第一步在一组重要的年轻患者中，通过RTC对当前的临床实践进行治疗令人沮丧的结果。