INTEGRATIVE DATA APPROACHES FOR RESISTANT HYPERTENSION IDENTIFICATION AND PREDICTION

项目摘要 这份K 01提案将促进我的职业发展，并推进我成为独立工作者的目标。 研究人员专注于发现和预测与心血管疾病风险相关的因素， 药物反应我的研究将通过调查来实现这一点，包括生物医学“大数据”， 多个来源，如基于电子健康记录（EHR）的数据、基于索赔的数据和基因组学， 其他“经济学数据”此应用程序的目标是利用大型数据集来识别特征 难治性高血压（RHTN）的预测。RHTN描述了高血压（HTN）个体的一个子集， 尽管使用多种抗HTN药物，但血压（BP）仍升高。根据目前的估计， 在HTN成人中RHTN的患病率，超过1200万美国人可能患有RHTN。虽然这些人的 血压仍然不受控制，他们患心血管不良后果的风险增加27%。中央 假设RHTN患病率的差异可以通过临床因素、生化因素 经济因素和药物依从性。为了检验中心假设，我将完成以下具体的 目的：1）通过手动EHR图表审查，在OneFlorida中验证RHTN可计算表型，2） 在基于EHR的数据中识别真实世界人群中RHTN的特征和预测因子，3）估计 真实世界RHTN人群中抗HTN依从性的水平，以及4）量化 估计的抗HTN药物依从性解释了预测RHTN。为了建立在我强大的 在人类遗传学和药物基因组学方面的专业知识和背景，我还将进行探索性目标： 将“组学”数据与基于EHR的数据相结合，以表征不良HTN结果的“组学特征”。我会 利用来自OneFlorida和ADVANCE的数据，这两个临床数据研究网络是国家 以患者为中心的临床研究网络或PCORnet，使我能够访问基于EHR的纵向数据， 多达1400万人。这项拟议中的研究意义重大，因为它将确定临床，生化， 与RHTN相关的“组学”和依从性特征，使HTN患者具有更高的RHTN风险 或不依从的情况更快地被发现，并针对精确的治疗方案。成功 进行这项工作，我需要在1）可计算表型的验证，2） 使用大型数据集的预测模型，3）与EHR数据集成相关的复杂性， 基于索赔的来源，4）与EHR和基于“组学”的数据形式集成相关的复杂性 5）临床决策支持。这个培训计划是与我强大的指导团队一起设计的 （William Hogan，医学博士，理学硕士; Rhonda Cooper-DeHoff，药学博士，理学硕士;乔治迈克尔，哲学博士; Dana Crawford， 和Francois Modave，PhD）。最后，佛罗里达大学丰富的培训环境， 凭借我以前的培训和经验，创新的研究计划，高质量的培训计划，以及出色的 指导团队给了我成功过渡到独立研究的最高可能性。

PROJECT SUMMARY This K01 proposal will facilitate my career development and advance my goal of becoming an independent investigator focused on discovery and prediction of factors associated with cardiovascular disease risk and drug response. My research will accomplish this through investigations that include biomedical “Big Data” from multiple sources, such as electronic health record (EHR) based data, claims based data, and genomics and other `omics data. The objective for this application is to utilize large datasets to identify characteristics predictive of resistant hypertension (RHTN). RHTN describes a subset of hypertensive (HTN) individuals with elevated blood pressure (BP) despite use of multiple anti-HTN medications. Based on current estimates of the prevalence of RHTN among HTN adults, over 12 million Americans could have RHTN. While these individuals' BP remains uncontrolled, they are at a 27% increased risk for adverse cardiovascular outcomes. The central hypothesis is that variance in the prevalence of RHTN can be explained by clinical factors, biochemical factors, `omic factors, and medication adherence. To test the central hypothesis, I will complete the following Specific Aims: 1) Validate the RHTN computable phenotype within OneFlorida through manual EHR chart review, 2) Identify characteristics and predictors of RHTN in the real-world population within EHR based data, 3) Estimate the level of anti-HTN adherence within a real-world RHTN population, and 4) Quantify the variability that estimated anti-HTN medication adherence explains in predicting RHTN. In order to build on my strong expertise and background in human genetics and pharmacogenomics, I will also conduct an Exploratory Aim: Integrate `omics data with EHR based data to characterize `omic signatures of adverse HTN outcomes. I will utilize data from OneFlorida and ADVANCE, two of the Clinical Data Research Networks within the National Patient Centered Clinical Research Network or PCORnet, giving me access to longitudinal EHR-based data on up to ~14 million individuals. The proposed study is significant because it will identify clinical, biochemical, `omic, and adherence characteristics associated with RHTN, allowing HTN patients with a higher risk for RHTN or non-adherence to be identified sooner, and targeted to precision treatment regimens. To successfully conduct this work, I requires specific training in 1) the validation of computable phenotypes, 2) the refinement of prediction models using large datasets, 3) the complexities associated with integration of data from EHR and claims based sources, 4) the complexities associated with integration of data form EHR and `omics based sources, and 5) clinical decision support. This training plan was designed with my strong mentoring team (William Hogan, MD, MS; Rhonda Cooper-DeHoff, PharmD, MS, George Michailidis, PhD; Dana Crawford, PhD, and Francois Modave, PhD). Finally, the rich training environment at the University of Florida, coupled with my previous training and experience, innovative research plan, high-quality training plan, and outstanding mentoring team give me the highest likelihood of successful transition to research independence.