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 (CVST) 是一种中风亚型，发病率为每百万人中 15 人年。 CVST 主要影响儿童和年轻人，尤其是育龄年轻女性和那些有高凝风险的人。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，会出现局灶性缺陷、精神状态抑郁并进展为可能会发生昏迷。全身抗凝是治疗的主要手段，用于预防血栓同时促进再通。相当一部分患者可能会进一步恶化，并且至少 20% 死亡或致残，其中死亡率最高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求高危工作年龄 CVST 人群。使用新型抗凝剂和/或静脉抗血小板药物等替代疗法是有希望的在疾病过程早期为难治性患者提供选择。然而，考虑到潜在的不利影响影响和增加的成本，临床决策工具的优化，使我们能够准确地分层高风险 CVST 患者，代表了强制性的第一步。为了改善这个选择过程，研究工作已经专注于识别临床和放射学预测因素，以建立可靠的预测模型。不幸的是，最初的分层分数未能证明足够的准确性来实施由于模型构建和选择过程中的一些概念限制，临床实践。详细描述先前研究的严谨性将在本提案的重要性部分中介绍。在对爱荷华大学的 CVST 队列进行彻底的可行性分析后，我们渴望提出 2- 年研究开发和验证一个新的、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型临床和放射学生物标志物的基础上来自两个大型 CVST 队列的早期病理生理阶段。我们还将利用创新的统计机器学习方法来优化我们的模型选择和规模预测性能。最后，我们将在独立的患者子集中评估我们的模型，以进一步验证、完善和推广。该项目的成功完成将优化高危CVST患者在二次治疗前的早期选择伤害扩大并持续。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代表第一个必要的关键步骤，以打开比较新产品的机会之窗通过 RTC 对大量患有其他疾病的年轻患者进行针对当前临床实践的治疗惨淡的结果。