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.

项目总结/摘要 最近的药理学进展已经减少了动脉粥样硬化性心血管疾病的负担 （ASCVD），在美国死亡率的主要原因，但更多的工作是必要的，以进一步改善 患者结局。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治疗，包括改变反应的人口统计学、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研究的证据基础。 管理调脂治疗方案，但回答参与者的狭窄问题， 代表更广泛的ASCVD风险人群。因此，我们不能依赖RCT全面地 指导药学护理。电子健康档案（EHR）是一种宝贵而有效的资源， 调脂疗法EHR的结果可以补充RCT的结果， 治疗建议。本提案的总体目标是促进我们对如何 患者对用于预防ASCVD的调脂剂有反应。我们预计，新的标记物可以 鉴定对不同调脂方案的应答者、无应答者和副作用应答者。我们 将使用Kaiser Permanente北方加州（KPNC）的EHR来表征治疗反应。成员 的KPNC（约350万成员）代表了ASCVD或有风险患者的广泛和多样化背景 ASCVD此外，KPNC EHR代表来自集成医疗保健提供系统的健康记录 有一个非常长的后续行动期（1996年至今），允许应用创新的 评估治疗反应的方法。在目标1中，Akinyemi Oni-Orisan博士（主要研究者）将 模型纵向非HDL-C水平，用于开发使用非HDL-C的调脂药物给药算法。 线性混合效应模型在目标2中，Oni-Orisan博士将描述脂质体的有效性和安全性， 使用考克斯回归修改ASCVD的方案。在目标3中，Oni-Orisan博士将确定遗传预测因子 他汀类药物反应的基因组关联研究。本提案的调查结果将（1）确定预测因素 对调脂药物治疗方案的反应，（2）揭示调脂药物治疗的重要生物学途径。 药物反应，（3）帮助临床医生提供个性化护理，减少公共卫生负担 ASCVD。Oni-Orisan博士将由一组经验丰富的研究人员指导，他们具有数学方面的专业知识。 建模、生物统计学、流行病学、基因组学和脂质学。加州大学弗朗西斯科分校是 世界领先的生物医学研究中心之一奥尼-奥里桑博士会利用富人 在这个研究环境中的资源，以完成建议。总的来说，研究、培训和 本提案中描述的机构环境将有助于Oni-Orisan博士实现其长期职业目标（1） 开展高影响力的转化研究，促进药学服务，并直接造福于 国家的心血管健康和（2）成为一个成功的独立调查员。