Classification of Stroke Etiology Using Advanced Computational Approaches

项目摘要/摘要 这份提案是对K23指导患者导向研究职业发展奖的申请 理查·夏尔马博士，医学博士，公共卫生硕士，为独立的、以病人为导向的学术生涯做准备 血管神经学。夏尔马博士是一名职业生涯早期的临床科学家，拥有助理教授 耶鲁大学医学院的预约。她的长期目标是建立一项中风临床研究 应用先进的计算技术更好地为循证医学的实施提供信息的实验室 二级中风预防治疗以及新疗法的发现。为了实现这一目标 目标，Sharma博士试图实现她的培训目标：1)进一步提高她在数据科学方面的技能，以管理和 处理高维数据集；2)利用先进的计算技术获得便利；3)开发 基于生物标记物的临床试验方法学的基础；以及4)获得必要的核心能力 独立领导研究项目。通过这笔培训补助金的机制，夏尔马博士将拥有 有机会完成直接相关的正式课程，掌握基础研究方法，以及 产生一个工作组合，这将是获得独立研究资金的关键。夏尔马医生有 全力支持她的指导团队，其中包括心脏病专家和结局研究人员哈兰·克鲁姆霍尔茨博士 世卫组织专门利用大数据改善医疗保健；共同导师、血管神经学家劳伦·桑辛博士 和一位在中风和生物数据研究方面有专长的翻译科学家；共同导师辛西亚博士 勃兰特是一名急诊内科医生和信息学家，也是电子病历方面的专家 研究；共同导师赵宏宇博士，一位擅长分析来自无数 来源包括健康记录和生物数据；以及共同导师胡曼·卡迈勒博士，一位神经强化专家和 多产的中风临床研究人员。这个指导团队，以及合作者和受人尊敬的顾问 委员会将为夏尔马博士提供实现这些目标所需的指导、资源和支持 并过渡到学术独立。 每年有近80万例缺血性中风，其中20%-40%的根本原因是中风 是未知的。因此，二次中风治疗可能不会特别针对导致中风的病理。 中风，潜在地导致复发的、可能更具破坏性的缺血性中风的绝对风险。 我们的目标是通过一系列分析来解决这种诊断的不确定性。拟议的研究将利用 分析急性缺血性卒中入院患者电子病历数据的新方法 医疗系统(耶鲁-纽黑文医院、马萨诸塞州总医院和退伍军人事务部 美国卫生管理医院)、蛋白质组、神经成像和调查数据 中风患者亚组，临床试验数据达到2个具体目的：1)对非隐源性进行分类 在住院期间使用EHR输入分配给患者的缺血性中风病因；2)预测 通过1年的随访，初步诊断出中风已确诊为特定病因的隐源性患者。

PROJECT SUMMARY/ABSTRACT This proposal is an application for the K23 Mentored Patient-Oriented Research Career Development Award designed to prepare Dr.Richa Sharma, MD, MPH for a career in independent, patient-oriented academic vascular neurology. Dr.Sharma is an early career clinician scientist who holds an Assistant Professor appointment at the Yale School of Medicine. Her long-term goal is to establish a stroke clinical research laboratory that applies advanced computational techniques to better inform implementation of evidence-based secondary stroke prevention treatments as well as discovery of novel therapeutics. In order to accomplish this goal, Dr.Sharma seeks to fulfill her training objectives of: 1) furthering her skills in data science to manage and process high-dimensional datasets; 2) gaining facility with advanced computational techniques; 3) developing a foundation in biomarker-based clinical trial methodology; and 4) acquiring core competencies necessary to independently lead a research program. Through the mechanism of this training grant, Dr.Sharma will have the opportunity to complete directly relevant formal coursework, master fundamental research methodologies, and generate a portfolio of work that will be critical for obtaining independent research funding. Dr.Sharma has the full support of her mentoring team which includes Dr.Harlan Krumholz, a cardiologist and outcomes researcher who specializes in harnessing big data to improve care; co-mentor Dr.Lauren Sansing, a vascular neurologist and a translational scientist with expertise in stroke and investigation of biologic data; co-mentor Dr.Cynthia Brandt, an emergency medicine physician and an informatician who is an expert in electronic medical record research; co-mentor Dr.Hongyu Zhao, an expert biostatistician adept at analysis of data from a myriad of sources including health record and biologic data; and co-mentor Dr.Hooman Kamel, a neurointensivist and a prolific stroke clinical researcher. This mentorship team, along with collaborators and esteemed advisory committee, will provide Dr.Sharma with the guidance, resources, and support necessary to reach these goals and transition to academic independence. There are nearly 800,000 ischemic strokes per year, of which the underlying cause of 20-40% of these strokes is unknown. Consequently, secondary stroke therapies may not specifically target the pathology that led to the stroke, potentially resulting in an unmitigated risk for a recurrent, potentially more devastating ischemic stroke. We aim to address this diagnostic uncertainty through a series of analyses. The proposed research will utilize novel methods to analyze electronic health record data of patients admitted with acute ischemic stroke to 3 healthcare systems (Yale-New Haven Hospital, Massachusetts General Hospital, and the Veterans Affair Health Administration Hospitals in the United States), proteomic, neuroimaging, and survey data from a subgroup of stroke patients, and clinical trial data to achieve 2 specific aims: 1) to classify non-cryptogenic ischemic stroke etiologies assigned to patients during hospitalization using EHR inputs; 2) to predict the diagnosis of initially cryptogenic patients whose strokes are resolved to a specific etiology by 1-year follow-up.