Characterization of response to lipid-modifying regimens for atherosclerotic cardiovascular disease using electronic health records

使用电子健康记录表征动脉粥样硬化性心血管疾病调脂方案的反应

• 批准号: 10200134
• 负责人: Akinyemi Oni-Orisan
• 金额: $ 17.75万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10200134
• 关键词: Achievement; Adult; Adverse effects; Affect; Aging; Algorithms; Atherosclerosis; Biological; Biomedical Research; Biometry; California; Cardiovascular Diseases; Cardiovascular system; Clinical Management; Collaborations; Complement; Complex; Data; Development; Disease Progression; Dose; Electronic Health Record; Environment; Epidemic; Epidemiology; Event; Genetic Markers; Genetic Polymorphism; Genetic study; Genomics; Goals; Grant; Health; High Density Lipoproteins; Integrated Delivery of Health Care; LDL Cholesterol Lipoproteins; Life Style; Lipids; Medical Genetics; Mentors; Methodology; Modeling; Myopathy; Nature; Participant; Pathway interactions; Patient-Focused Outcomes; Patients; Pharmaceutical Cares; Pharmaceutical Preparations; Pharmacogenomics; Pharmacology; Play; Population; Populations at Risk; Prevention; Principal Investigator; Public Health; Randomized Controlled Trials; Recommendation; Regimen; Research; Research Personnel; Research Training; Resources; Role; Safety; Sample Size; San Francisco; Source; Subgroup; System; Therapeutic; Time; Translational Research; United States; United States National Institutes of Health; Universities; Variant; Work; atherosclerosis risk; cardiovascular health; career; clinical efficacy; clinical practice; cohort; comorbidity; comparative; cost; epidemiology study; evidence base; experience; follow-up; genetic epidemiology; genetic predictors; genetic variant; genome wide association study; genome-wide; health record; improved; improved outcome; individual patient; individual response; innovation; mathematical model; member; mortality; novel marker; personalized care; precision medicine; predicting response; predictive marker; responders and non-responders; response; risk benefit ratio; translational impact; whole genome

PROJECT SUMMARY/ABSTRACT Recent pharmacological advancements have reduced the burden of atherosclerotic cardiovascular disease (ASCVD), the leading cause of mortality in the United States, but more work is necessary to further improve patient outcomes. In particular, we do not thoroughly understand how individual patients respond to lipid lowering therapies over time including the demographic, clinical and genetic variables which modify response. Data from randomized controlled trials (RCTs) have largely shaped the evidence base for the clinical management of lipid-modifying therapeutic regimens, but answer narrow questions in participants who may not be representative of the broader population at risk for ASCVD. Thus, we cannot rely on RCTs to comprehensively guide pharmaceutical care. Electronic health records (EHRs) are a valuable and efficient resource for evaluating lipid-modifying therapies. Results from EHRs can complement RCT findings to provide more comprehensive treatment recommendations. The overall objective of this proposal is to advance our understanding of how patients respond to lipid-modifying agents for the prevention of ASCVD. We anticipate that novel markers can identify responders, non-responders, and adverse-effect responders to different lipid-modifying regimens. We will use EHRs from Kaiser Permanente Northern California (KPNC) to characterize therapy response. Members of KPNC (~3.5 million members) represent a broad and diverse background of patients with ASCVD or at risk for ASCVD. In addition, KPNC EHRs represent health records from an integrated healthcare delivery system with an exceptionally long period of follow-up (1996-present), allowing for the application of innovative methodologies to evaluate therapy response. In Aim 1, Dr. Akinyemi Oni-Orisan (Principal Investigator) will model longitudinal non-HDL-C levels for the development of a lipid-modifying drug dosing algorithm using non- linear mixed effects modeling. In Aim 2, Dr. Oni-Orisan will characterize the efficacy and safety of lipid- modifying regimens for ASCVD using Cox regression. In Aim 3, Dr. Oni-Orisan will identify genetic predictors of statin response using genome wide association studies. Findings from this proposal will (1) identify predictors of response to lipid-modifying drug regimens, (2) uncover key biological pathways important to lipid-modifying drug response, and (3) aid clinicians in providing individualized care that will reduce the public health burden of ASCVD. Dr. Oni-Orisan will be mentored by a team of experienced researchers with expertise in mathematical modeling, biostatistics, epidemiology, genomics, and lipidology. The University of California, San Francisco is one of the leading biomedical research centers in the world. Dr. Oni-Orisan will take advantage of the rich resources within this research environment to complete the proposal. Overall, the research, training, and institutional environment described in this proposal will aid Dr. Oni-Orisan in his long-term career goals of (1) conducting high-impact translational research that advances pharmaceutical care and directly benefits the cardiovascular health of the nation and (2) becoming a successful independent investigator.

项目摘要/摘要 最近的药理学进步减轻了动脉粥样硬化心血管疾病的负担 （ASCVD）是美国死亡率的主要原因，但需要更多的工作才能进一步改善 患者的结果。特别是，我们不完全了解单个患者对脂质的反应 随着时间的流逝，降低疗法，包括改变反应的人口，临床和遗传变量。 来自随机对照试验（RCT）的数据在很大程度上塑造了临床的证据基础 管理脂质改良治疗方案的管理，但在参与者中回答狭窄的问题 代表更广泛的ASCVD风险的人口。因此，我们不能依靠RCT全面 指导药物护理。电子健康记录（EHRS）是评估的宝贵而有效的资源 脂质修饰疗法。 EHR的结果可以补充RCT的发现以提供更全面的 治疗建议。该提议的总体目标是提高我们对如何的理解 患者应对预防ASCVD的脂质改良剂反应。我们预计新颖的标记可以 确定对不同脂质修饰方案的响应者，非反应者和不良效应的反应者。我们 将使用北加州凯撒（Kaiser Permanente）（KPNC）的EHR来表征治疗反应。成员 KPNC（约350万成员）代表ASCVD患者的广泛而多样的背景 对于ASCVD。此外，KPNC EHR代表了综合医疗保健提供系统中的健康记录 随着时间的随访时间非常长（1996年至今），允许应用创新 评估治疗反应的方法。在AIM 1中，Akinyemi Oni-Orisan博士（首席研究员）将 模拟纵向非HDL-C水平，以开发使用非 - 线性混合效应建模。在AIM 2中，Oni-Orisan博士将表征脂质的功效和安全性 使用COX回归修改ASCVD方案。在AIM 3中，Oni-Orisan博士将确定遗传预测因子 使用基因组广泛关联研究的他汀类药物反应。该提案的发现将（1）确定预测因子 对脂质改良药物方案的反应，（2）发现对脂质修饰很重要的关键生物学途径 药物反应，以及（3）帮助临床医生提供个性化护理，以减轻公共卫生负担 ASCVD。 Oni-Orisan博士将由一支具有数学专业知识的经验丰富的研究人员指导 建模，生物统计学，流行病学，基因组学和脂质学。加利福尼亚大学旧金山是 世界领先的生物医学研究中心之一。 Oni-Orisan博士将利用富人 在本研究环境中的资源来完成该建议。总体而言，研究，培训和 本提案中描述的机构环境将有助于Oni-Orisan博士的长期职业目标（1） 进行高影响的转化研究，以进步药物，并直接受益于 国家的心血管健康和（2）成为成功的独立研究者。