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

项目摘要 与非抵抗性高血压相比，抵抗性高血压（RH）使不良心血管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，但高血压仍不受控制 至少4种药物治疗或控制血压，RH估计影响至少2000万人 美国人重要的是，真正的RH必须与假性抵抗性高血压（假性RH）区分开来， 当血压由于外在因素如次优的药物剂量而保持升高时发生， 药物不依从或白大衣效应。无法区分真RH和假RH， 因此，治疗加重了过度治疗假性RH的风险（例如晕厥、福尔斯、急性肾损伤） 以及对真正的RH治疗不足（例如中风、心肌梗死）。区分真RH和伪RH， 然而，在临床上是困难的，部分原因是捕获药物依从性模式的复杂性 并确认白大衣效应。因此，我们的总体目标是确定电子健康记录是否 (EHR)基于分析和工具可以用来缩小RH护理方面的持续差距。的具体目标 本研究计画的主要目的是：（1）发展并验证一套电脑化的演算法，利用电子健康记录资料来辨识生殖健康 并区分真RH和假RH及其亚型;（2）开发和优化CDS工具， 帮助临床医生识别和管理明显的RH;（3）试点实施 除了假性RH及其亚型之外，CDS工具还用于促进RH的护理。这项研究有望提高 我们对如何利用卫生信息技术为科学发现提供信息的理解， 也推动了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， 旨在提供PI成功追求所需的先进研究技能和经验， 独立的学术生涯侧重于：㈠优化护理价值（即以降低成本提高质量）; (ii)利用卫生信息技术和临床生成的数据，获得对疾病的新见解 国家; ㈢利用实施科学原则促进保健服务的创新。这些努力将 由雪松西奈半岛的优秀的研究环境和基础设施，包括科学院的支持 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到位，这个奖项是 理想的设计是提供在其职业生涯中作为独立研究者启动PI所需的经验 心血管预后研究的未来领导者这一补充建议包括计划的连续性 这些研究的目的是在一个关键的生活事件的PI。这需要增加新的人员， 协助数据验证以及与外部合作伙伴的协调。PI的机构提供了 PI和新工作人员临床时间表的灵活性，以确保该提案的成功。此外，本发明还 该提案概述了PI将以同等机会取代错过的培训的机制。

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. 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, this award 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. This addended proposal includes plans for continuity of these research aims during a critical life event for the PI. This calls for the addition of new personnel who will assist with data validation, as well as coordination with external partners. The PI’s institution has provided flexibility for the clinical schedules for both the PI and new personnel to ensure success of this proposal. Further, this proposal outlines mechanisms by which the PI will replace missed training with equivalent opportunities.