Implementation Science to Improve Cardiovascular Health: Leveraging EHR Systems to Advance Care of Resistant Hypertension

项目总结 顽固性高血压(RH)与非顽固性高血压相比，心血管不良结局的风险增加一倍 高血压。定义为尽管使用了至少3种抗高血压药物，但高血压仍未得到控制 至少服用4种药物或控制血压，RH估计至少影响2000万人 美国人。重要的是，真正的RH必须与假阻性高血压(伪RH)区分开来， 当血压因外在因素(如次优用药剂量)而持续升高时， 用药不依从，或白大褂效应。无法区分真RH和伪RH，并量身定做 相应的治疗会增加过度治疗假性RH(例如晕厥、跌倒、急性肾损伤)的风险。 以及未充分治疗真正的RH(例如中风、心肌梗死)。区分真RH和伪RH， 然而，这在临床上是困难的，部分原因是捕捉用药依从性模式涉及的复杂性 确认白大褂效应。因此，我们的总体目标是确定电子健康记录是否 基于电子病历(EHR)的分析和工具可用于填补RH护理方面持续存在的缺口。《公约》的具体目标 研究项目是：(1)开发和验证使用EHR数据识别RH的计算机化算法 并区分真RH和伪RH及其子类型；(2)开发和优化CDS工具 协助临床医生识别和管理表观RH；以及，(3)试点实施 CDS工具，除假性RH及其亚型外，还用于促进RH的护理。这项研究承诺将加强 我们对如何利用卫生信息技术来促进科学发现的理解，同时 也推动了对RH的高价值护理。拟议的工作将作为K23奖励计划的一部分进行， 设计提供了高级研究技能和经验，使PI能够成功开展 独立的学术生涯侧重于：(I)优化护理价值(即以更低的成本提高质量)； (2)利用卫生信息技术和临床生成的数据对疾病有新的认识 (3)利用实施科学原则促进护理提供方面的创新。私人侦探将 在他的指导团队：Teryl Nuckols博士的支持下完成拟议的研究和培训目标 (卫生服务研究和护理的价值)、苏珊·程博士(预防心脏病学、大数据分析和 人口健康)和约书亚·佩夫尼克博士(临床信息学)。这些努力将得到 锡达斯-西奈出色的研究环境和基础设施，包括施密特心脏研究所、 生物统计学和生物信息学研究中心，以及研究信息学和科学计算核心。 鉴于强大的指导、机构和基础设施支持，建议的K奖励计划是 理想的设计目的是提供在独立调查员职业生涯中启动私家侦探所需的经验 和未来心血管结果研究的领导者。

PROJECT SUMMARY Resistant hypertension (RH) doubles the risk for adverse cardiovascular outcomes compared to non-resistant hypertension. Defined as having uncontrolled high blood pressure despite the use of at least 3 antihypertensive medications or controlled blood pressure on at least 4 medications, RH is estimated to affect at least 20 million Americans. Importantly, true RH must be differentiated from pseudo-resistant hypertension (pseudo-RH), occurring when blood pressure remains elevated due to extrinsic factors such as suboptimal medication dosing, medication non-adherence, or white-coat effect. Inability to distinguish true RH from pseudo-RH, and tailor treatment accordingly, compounds the risks of overtreating pseudo-RH (e.g. syncope, falls, acute kidney injury) as well as undertreating true RH (e.g. stroke, myocardial infarction). Distinguishing true from pseudo-RH, however, is clinically difficult, in part given the complexities involved in capturing medication adherence patterns and confirming white-coat effect. Therefore, our overall objective is to determine whether electronic health record (EHR) based analytics and tools can be used to close persistent gaps in care for RH. The specific aims of the research project are to: (1) develop and validate a computerized algorithm that uses EHR data to identify RH and distinguish between true and pseudo-RH including its subtypes; (2) develop and optimize a CDS tool for aiding clinicians in the identification and management of apparent RH; and, (3) pilot the implementation of a CDS tool for facilitating care of RH in addition to pseudo-RH and its subtypes. This research promises to enhance our understanding of how health information technology can be leveraged to inform scientific discovery, while also driving high-value care for RH. The proposed work will be conducted as part of a K23 award program, designed provide the advanced research skills and experience needed for the PI to successfully pursue an independent academic career focused on: (i) optimizing value of care (i.e. improved quality at decreased cost); (ii) leveraging health information technology and clinically generated data to gain new insights into disease states; and, (iii) promoting innovation in care delivery using implementation science principles. The PI will accomplish the proposed research and training aims with the support of his mentoring team: Dr. Teryl Nuckols (health services research and value of care), Dr. Susan Cheng (preventive cardiology, large data analytics, and population health) and, Dr. Joshua Pevnick (clinical informatics). These efforts will be supported by the outstanding research environment and infrastructure of Cedars-Sinai including the Smidt Heart Institute, the Biostatistics and Bioinformatics Research Center, and the Research Informatics and Scientific Computing Core. Given the strong mentoring, institutional, and infrastructure supports in place, the proposed K award program is ideally designed to provide the experience needed to launch the PI in his career as an independent investigator and future leader in cardiovascular outcomes research.