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

改善心血管健康的实施科学：利用 EHR 系统推进难治性高血压的护理

• 批准号: 10653690
• 负责人: Joseph Ebinger
• 金额: $ 17.61万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10653690
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Adoption; Affect; Algorithms; Ambulatory Blood Pressure Monitoring; American; Antihypertensive Agents; Automobile Driving; Bioinformatics; Biometry; Blood Pressure; Cardiology; Cardiovascular Diseases; Cardiovascular system; Caring; Clinical; Clinical Informatics; Complex; Data; Data Analytics; Data Set; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Management; Dose; Electronic Health Record; Environment; Evaluation; Event; Explosion; Focus Groups; Future; Guidelines; Health Services Research; Health Status; Health system; Healthcare Systems; Heart; Home Blood Pressure Monitoring; Hypertension; Individual; Informatics; Infrastructure; Institution; Intervention; Interview; Intuition; K-Series Research Career Programs; Manuals; Mentored Patient-Oriented Research Career Development Award; Mentors; Methods; Morbidity - disease rate; Myocardial Infarction; Notification; Outcome; Outcomes Research; Outpatients; Patient Care; Patients; Pattern; Patterns of Care; Performance; Pharmaceutical Preparations; Prevalence; Preventive; Primary Care; Production; Provider; Quality of Care; Research; Research Personnel; Research Project Grants; Research Proposals; Resistance; Resistant Hypertension; Risk; Specialist; Stroke; Structure; Surveys; Syncope; System; Technology; Testing; Therapeutic; Training; Training Programs; United States; Visit; Work; adjudication; blood pressure control; blood pressure elevation; blood pressure reduction; cardiovascular health; cardiovascular risk factor; care delivery; career; clinical decision support; clinical practice; clinical research site; computerized; computerized data processing; cost; design; electronic health record system; evidence base; experience; falls; health data; health information technology; hypertension treatment; implementation science; improved; individualized medicine; innovation; insight; medication compliance; medication nonadherence; mortality; pilot test; population health; practice setting; pragmatic intervention; programs; prospective; prototype; scientific computing; skills; support tools; tool; usability

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.

项目摘要 耐药性高血压（RH）使心血管不良结局的风险增加一倍 高血压。尽管使用至少3次降压，但被定义为无法控制的高血压 据估计，对至少4种药物的药物或控制血压至少会影响至少2000万种 美国人。重要的是，必须将真实的RH与抗伪高压（伪RH）区分开 当血压由于外部因素（例如次优药物剂量）引起的血压升高时发生， 药物不遵守或白大衣效应。无法区分真实RH和伪RH和量身定制 因此，治疗使过度治疗伪RH的风险（例如晕厥，瀑布，急性肾脏损伤） 以及未处理的真实RH（例如中风，心肌梗塞）。区分true和pseudo-rh， 但是，在临床上很难，部分鉴于捕获药物依从性模式所涉及的复杂性 并确认了白色大衣效应。因此，我们的总体目标是确定电子健康记录是否 （EHR）基于分析和工具可用于缩小RH护理的持续差距。特定目标 研究项目将要：（1）开发和验证使用EHR数据识别RH的计算机化算法 并区分真实和伪RH，包括其亚型； （2）开发并优化了用于 协助临床医生对明显RH的识别和管理； （3）试行实施 除伪RH及其亚型外，用于促进RH护理的CD工具。这项研究有望增强 我们对如何利用健康信息技术的理解来告知科学发现，而 还为RH推动高价值护理。拟议的工作将作为K23奖项计划的一部分进行， 设计提供了PI成功追求PI所需的高级研究技能和经验 独立的学术职业专注于：（i）优化护理价值（即以降低的成本提高质量）； （ii）利用健康信息技术和临床生成的数据来获得对疾病的新见解 国家（iii）使用实施科学原则促进护理交付的创新。 PI会 在他的指导团队的支持下完成拟议的研究和培训目标：Teryl Nuckols博士 （卫生服务研究和护理价值），Susan Cheng博士（预防性心脏病学，大数据分析和 人口健康）和约书亚·佩夫尼克（Joshua Pevnick）博士（临床信息学）。这些努力将得到 Cedars-Sinai的杰出研究环境和基础设施，包括Smidt Heart Institute， 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。 鉴于有强大的指导，机构和基础设施支持，拟议的K颁奖计划是 理想的设计旨在提供独立调查员职业生涯中PI发起PI所需的经验 以及心血管结局研究的未来领导者。

项目成果

Blood pressure variability supersedes heart rate variability as a real-world measure of dementia risk.

• DOI: 10.1038/s41598-024-52406-8
• 发表时间: 2024-01-22
• 期刊: Scientific reports
• 影响因子: 4.6

Improving Efficiency of the Barbershop Model of Hypertension Care for Black Men With Virtual Visits.

• DOI: 10.1161/jaha.120.020796
• 发表时间: 2021-07-06
• 期刊: Journal of the American Heart Association
• 影响因子: 5.4
• 作者: Blyler CA;Ebinger J;Rashid M;Moy NP;Cheng S;Albert CM;Rader F
• 通讯作者: Rader F

Sex Differences in Myocardial and Vascular Aging.

• DOI: 10.1161/circresaha.121.319902
• 发表时间: 2022-02-18
• 期刊: Circulation research
• 影响因子: 20.1
• 作者: Ji H;Kwan AC;Chen MT;Ouyang D;Ebinger JE;Bell SP;Niiranen TJ;Bello NA;Cheng S
• 通讯作者: Cheng S

Prior COVID-19 Infection and Antibody Response to Single Versus Double Dose mRNA SARS-CoV-2 Vaccination.

• DOI: 10.1101/2021.02.23.21252230
• 发表时间: 2021-02-26
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Ebinger, Joseph E;Fert-Bober, Justyna;Sobhani, Kimia
• 通讯作者: Sobhani, Kimia

Sex-, Race- and Ethnicity-Based Differences in Thromboembolic Events Among Adults Hospitalized With COVID-19.

• DOI: 10.1161/jaha.121.022829
• 发表时间: 2021-12-07
• 期刊: JOURNAL OF THE AMERICAN HEART ASSOCIATION
• 影响因子: 5.4
• 作者: Ilyas, Sadia;Henkin, Stanislav;Martinez-Camblor, Pablo;Suckow, Bjoern D.;Beach, Jocelyn M.;Stone, David H.;Goodney, Philip P.;Ebinger, Joseph E.;Creager, Mark A.;Columbo, Jesse A.
• 通讯作者: Columbo, Jesse A.