Understanding and predicting cardiac events in HD using real-time EHRs

使用实时 EHR 了解和预测 HD 中的心脏事件

• 批准号: 8425985
• 负责人: Benjamin Alan Goldstein
• 金额: $ 16.12万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8425985
• 关键词: Accounting; Acute; Address; Advertising; Algorithms; Biological Markers; Blood Pressure; Breast Cancer Risk Assessment Tool; Cardiac; Cardiovascular Diseases; Cardiovascular system; Caring; Cessation of life; Characteristics; Clinic; Clinical; Collaborations; Comorbidity; Data; Data Analyses; Development; Dialysis procedure; Doctor of Philosophy; Economics; Education; Electronic Health Record; End stage renal failure; Environment; Evaluation; Event; Future; Goals; Grant; Growth; Health; Heart Arrest; Hemodialysis; Hospitalization; Hour; Individual; Infection; Investigation; Kidney; Knowledge; Laboratories; Length; Machine Learning; Master of Public Health; Measurement; Measures; Medical; Medicine; Mentors; Mentorship; Methodology; Methods; Metric; Mind; Modeling; Myocardial Infarction; National Heart, Lung, and Blood Institute; Outcome; Outpatients; Output; Patients; Pattern; Pharmaceutical Preparations; Physiologic pulse; Play; Process; Recording of previous events; Reporting; Research; Risk; Risk Assessment; Risk Factors; Role; Sensitivity and Specificity; Specific qualifier value; System; Technology; Time; Training; Variant; Work; base; cardiovascular disorder risk; career; clinical care; computerized tools; cost; demographics; disorder risk; health information technology; hemodynamics; high risk; improved; innovation; member; professor; programs; public health relevance; randomized trial; skills; social; statistics; tool

DESCRIPTION (provided by applicant): The purpose of this K25 proposal is to provide Dr. Benjamin Goldstein Ph.D., M.P.H., with the necessary protected time and additional training to develop as an independent, clinical biostatistician. This proposal has two key components: (1) an innovative research plan and (2) a comprehensive training plan. It is well recognized that patients undergoing hemodialysis (HD) are at increased risk of cardiac related events which often prove fatal. While substantive research has identified risk factors for these events, little work has been performed on forecasting their occurrence. The proposed research proposes to use existing electronic health record (EHR) data available through a collaborating dialysis center, DaVita Inc, to derive such a prediction model. EHRs contain detailed information on both a patient's health history (e.g. comorbidities, medications) as well as their evolving health statu (i.e. changes in health). A particularly unique aspect of the DaVita EHR system is the availability of real-time measures of health (e.g. blood pressure, pulse) available over the course of an HD session. Through our ongoing collaboration we will have data on 10,000s of individuals each with 100s of HD sessions, presenting the opportunity to analyze millions of dialysis sessions. Within this wealth of data two particular questions will be addressed: (1) How does a patient's hemodynamics vary over the course of and across HD sessions? (2) Can we derive a predictor for the near term onset of a cardiac event? To answer question 1, sophisticated statistical methodology, referred to as functional data analysis (FDA), will be utilized. Patterns of hemodynamic measures will be compared during and across HD sessions with key features extracted. For question 2, machine learning methodology will be used to derive a prediction model for the onset of cardiac events. The final aim will be to assess the feasibility of applying such models within a clinical environment. As a Ph.D. biostatistician, Dr. Goldstein has many of the methodological and computational skills necessary to perform the proposed analyses. The proposed methods, while established, also have ample room for statistical investigation and will provide the basis for methodological research. He will be mentored by Dr. Bradley Efron, professor in the Stanford Department of Statistics, and a world recognized expert in statistical methodology. Serving as a consultant will be Drs. Trevor Hastie and John Ioannidis, fellow members of the department of statistics and experts in FDA and prediction evaluation respectively. The focus of Dr. Goldstein's training will be on developing his clinical expertise. This will be performed through a combination of didactic courses, one-on- one tutorials and clinical exposure. Dr. Wolfgang Winkelmayer, a clinical nephrologist and close collaborator of Dr. Goldstein, will supervise Dr. Goldstein's clinical knowledge development. He will be joined by Dr. Mark Hlatky, a research cardiologist, who will also provide mentorship with regards to the cardiac substance of the project. Additional consultants across the department of medicine will be used as needed. The proposed project will have a tremendous impact on Dr. Goldstein's career prospects. At the end of the 5 year period he will have begun the process of developing a research program in the analysis of EHR data. There will be ample avenues to pursue future studies, through the analysis of other predictor variables (e.g. biomarkers, psycho-social factors), outcomes (e.g. hospitalization, cost) and most importantly, implementation of the prediction models in the clinic. The clinical training period will provide him with the necessary background to succeed as a clinically-oriented biostatistician and develop as a leader in the field.

描述（由申请人提供）：本 K25 提案的目的是为 Benjamin Goldstein 博士、公共卫生硕士提供必要的受保护时间和额外培训，以发展成为一名独立的临床生物统计学家。该提案有两个关键组成部分：（1）创新研究计划和（2）综合培训计划。众所周知，接受血液透析（HD）的患者发生心脏相关事件的风险增加，而这些事件往往是致命的。虽然实质性研究已经确定了这些事件的风险因素，但在预测其发生方面却很少开展工作。拟议的研究建议使用合作透析中心 DaVita Inc 提供的现有电子健康记录 (EHR) 数据来推导出这样的预测模型。 EHR 包含有关患者健康史（例如合并症、药物）及其不断变化的健康状况（即健康变化）的详细信息。 DaVita EHR 系统的一个特别独特的方面是可用性 HD 会话过程中可用的实时健康测量值（例如血压、脉搏）。通过我们持续的合作，我们将获得 10,000 个人的数据，每个人都进行了 100 次 HD 治疗，从而提供了分析数百万次透析治疗的机会。在这些丰富的数据中，我们将解决两个特定问题：（1）患者的血流动力学在 HD 治疗过程中和不同疗程之间有何变化？ (2) 我们能否得出心脏事件近期发作的预测因子？为了回答问题 1，将使用复杂的统计方法，称为功能数据分析 (FDA)。将在 HD 会话期间和之间比较血流动力学测量模式，并提取关键特征。对于问题 2，将使用机器学习方法来推导心脏事件发生的预测模型。最终目标是评估在临床环境中应用此类模型的可行性。作为一名博士。作为生物统计学家，戈尔茨坦博士拥有执行拟议分析所需的许多方法和计算技能。所提出的方法虽然已建立，但也有足够的统计调查空间，并将为方法论研究提供基础。他将受到斯坦福大学统计系教授、世界公认的统计方法专家 Bradley Efron 博士的指导。博士将担任顾问。 Trevor Hastie 和 John Ioannidis 分别是统计部研究员、FDA 和预测评估专家。戈德斯坦博士的培训重点是发展他的临床专业知识。这将通过教学课程、一对一教程和临床接触的结合来进行。 Wolfgang Winkelmayer 博士是一位临床肾脏病专家，也是 Goldstein 博士的密切合作者，他将监督 Goldstein 博士的临床知识发展。心脏病研究专家 Mark Hlatky 博士将加入他的行列，他还将就该项目的心脏物质提供指导。将根据需要使用医学部门的其他顾问。拟议的项目将对戈德斯坦博士的职业前景产生巨大影响。 5 年期结束时，他将开始开发 EHR 数据分析研究计划。通过分析其他预测变量（例如生物标志物、心理社会因素）、结果（例如住院、费用）以及最重要的是在临床中实施预测模型，未来的研究将有充足的途径。临床培训期间将为他提供必要的背景，使他能够成功成为一名面向临床的生物统计学家并发展成为该领域的领导者。