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

描述（由申请人提供）：本K25提案的目的是为Benjamin Goldstein博士提供，M.P.H.有必要的保护时间和额外的培训，以发展成为一个独立的，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，这些事件通常证明是致命的。虽然大量研究已查明这些事件的风险因素，但在预测其发生方面所做的工作很少。拟议的研究建议使用现有的电子健康记录（EHR）数据，通过合作透析中心，DaVita公司，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）以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 在HD会话过程中可用的实时健康测量（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，每个人都有100个HD疗程，从而有机会分析数百万个透析疗程。在这些丰富的数据中，将解决两个特定的问题：（1）患者的血流动力学在HD疗程期间和疗程之间如何变化？(2)我们能否推导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治疗期间和整个HD治疗期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推导心脏事件发作的预测模型。最终的目标将是评估在临床环境中应用这些模型的可行性。作为博士作为一名生物统计学家，Goldstein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经确立，但也有足够的空间进行统计调查，并将为方法学研究提供基础。他将由斯坦福大学统计系教授、世界公认的统计方法专家布拉德利埃夫隆博士指导。担任顾问的将是特雷弗·哈斯蒂博士和约翰·约恩尼斯博士，他们分别是统计系的成员和FDA和预测评估专家。Goldstein博士的培训重点将是发展他的临床专业知识。这将通过教学课程，一对一的教程和临床接触的组合进行。Wolfgang Winkelmayer博士是Goldstein博士的临床肾病学家和密切合作者，他将监督Goldstein博士的临床知识发展。他将与心脏病研究专家Mark Hlatky博士一起，他还将为该项目的心脏实质提供指导。将根据需要使用整个医学系的额外顾问。该项目将对戈德斯坦博士的职业前景产生巨大影响。在5年期间结束时，他将开始在EHR数据分析中开发研究计划的过程。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，成本）以及最重要的是在临床中实施预测模型，将有足够的途径进行未来的研究。临床培训期间将为他提供必要的背景，以成功地成为临床生物统计学家，并发展成为该领域的领导者。

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.