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.

项目概要/摘要 最近的药理学进展减轻了动脉粥样硬化性心血管疾病的负担 （ASCVD）是美国死亡的主要原因，但需要做更多的工作来进一步改善 患者的结果。特别是，我们并不完全了解个体患者对脂质的反应 随着时间的推移降低疗法，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 调脂治疗方案的管理，但回答可能不会的参与者的狭隘问题 代表更广泛的 ASCVD 风险人群。因此，我们不能依靠随机对照试验来全面地 指导药学服务。电子健康记录 (EHR) 是一种宝贵且高效的资源，可用于评估 调脂疗法。 EHR 的结果可以补充 RCT 结果，以提供更全面的信息 治疗建议。该提案的总体目标是加深我们对如何 患者对预防 ASCVD 的调脂药物有反应。我们预计新的标记可以 识别对不同调脂方案有反应者、无反应者和不良反应反应者。我们 将使用北加州凯撒医疗机构 (KPNC) 的 EHR 来描述治疗反应。会员 KPNC（约 350 万会员）代表了 ASCVD 或高危患者的广泛且多样化的背景 用于 ASCVD。此外，KPNC EHR 代表来自综合医疗服务系统的健康记录 具有特别长的随访期（1996年至今），允许应用创新 评估治疗反应的方法。在目标 1 中，Akinyemi Oni-Orisan 博士（首席研究员）将 模型纵向非 HDL-C 水平，用于开发使用非 HDL-C 的调脂药物剂量算法 线性混合效应建模。在目标 2 中，Oni-Orisan 博士将描述脂质- 使用 Cox 回归修改 ASCVD 治疗方案。在目标 3 中，Oni-Orisan 博士将确定遗传预测因素 使用全基因组关联研究来评估他汀类药物的反应。该提案的调查结果将 (1) 确定预测因素 对调脂药物方案的反应，（2）揭示对调脂药物至关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，从而减轻公共卫生负担 ASCVD 的。 Oni-Orisan 博士将接受一组经验丰富且具有数学专业知识的研究人员的指导 建模、生物统计学、流行病学、基因组学和脂质学。加州大学旧金山分校是 世界领先的生物医学研究中心之一。 Oni-Orisan 博士将利用富人的优势 该研究环境中的资源来完成该提案。总体而言，研究、培训和 本提案中描述的制度环境将帮助 Oni-Orisan 博士实现以下长期职业目标：(1) 进行高影响力的转化研究，以推进药物治疗并直接造福于 国家心血管健康；(2) 成为一名成功的独立研究者。