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: 8725658
• 负责人: Benjamin Alan Goldstein
• 金额: $ 3.17万
• 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 2014-09-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8725658
• 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博士提供必要的保护时间和额外的培训，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，这些事件往往被证明是致命的。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所做的工作很少。拟议的研究建议使用通过合作透析中心DaVita Inc提供的现有电子健康记录（EHR）数据来推导这样的预测模型。电子病历包含患者健康史（如合并症、药物）和健康状况演变（如健康变化）的详细信息。DaVita EHR系统的一个特别独特的方面是可用性