Advancing Personalized Hypertension Care through Big Data Science

项目总结。当前高血压(HTN)治疗范式的试错式药物选择 在近半个世纪里基本保持不变。个性化护理一直是具有挑战性的 因为患者和临床医生往往缺乏足够的证据来告知个人的护理决定。但, 广泛采用电子健康记录(EHR)为使用常规收集的临床记录创造了机会 向证据提供信息的数据。应用因果推理原理，这些数据可以用来识别临床 影响观察到的治疗反应变化的因素，进而将这些因素纳入 用于预测个体未来治疗反应的统计模型。NHLBI K01的统一主题 建议书是史蒂文·M·史密斯博士指导下的职业发展。这项提议将加速他的 过渡到独立研究人员，并为实现他的长期目标奠定基础 常规收集的临床数据，通过个性化显著改善患者的健康和福祉 关心。史密斯博士这个项目的目标是更好地了解现实世界中抗高血压药物的使用情况 影响对这类药物反应的因素，目的是建立用于临床的预测模型 决策支持工具，提供个性化的HTN管理建议。具体的研究目标是 包括描述现实世界抗高血压药物处方模式及其决定因素(目标1)， 确定两种常见降压药的有效性和安全性的疗效调节剂 血管紧张素转换酶抑制剂(ACE-IS)和噻嗪类利尿剂(AIM 2)，正在开发中 预测ACE-IS和噻嗪类利尿剂的反应以最大化降压疗效的模型(目标3)。 这项工作将利用与全州范围内的One佛罗里达数据信托基金的观察性研究方法 关于800万佛罗里达州人的纵向电子病历数据的储存库。史密斯医生在临床上的训练和经验 药学、公共/人口健康和HTN护理确保了该项目的临床相关性。他的前任 应用生物统计学方面的临床HTN研究经验和背景，结合拟议的培训 结合生物医学信息学、药物流行病学、多层次建模和领导力，确保 这项拟议工作的可行性和他的进一步发展。佛罗里达大学资源与基础设施， 包括UF CTSI、生物医学信息学计划和One佛罗里达研究联盟，提供 为实现提出的目标和史密斯博士的长期目标提供理想的环境。朗达博士 库珀-德霍夫将领导一个由药物流行病学专家组成的多学科指导团队 (Almut Winterstein博士)，生物统计学(Matthew Gurka博士)，生物医学信息学(Bill Hogan博士)，临床HTN (卡尔·佩平博士)和领导力(安妮·利比博士)。一体化的导师研究经验和培训 将允许史密斯博士竞争R01资金，并成为一名独立的临床医生-科学家使用 使用大规模EHR数据的观察性研究方法，以改进个人的药物治疗选择。

PROJECT SUMMARY. The current hypertension (HTN) treatment paradigm of trial-and-error drug selection has remained essentially unchanged for nearly half a century. Personalizing care has been challenging because patients and clinicians too often lack adequate evidence to inform individual care decisions. But, broad electronic health record (EHR) adoption has created opportunities for using routinely-collected clinical data to inform evidence. Applying principles of causal inference, such data can be used to identify clinical factors that influence observed variation in treatment response and, in turn, incorporate these factors into statistical models for predicting future treatment response for individuals. The unifying theme of this NHLBI K01 proposal is the mentored career development of Dr. Steven M. Smith. This proposal will accelerate his transition to an independent researcher and establish the foundation for achieving his long-term goal of using routinely-collected clinical data to substantially improve the health and wellbeing of patients by personalizing care. Dr. Smith's objective with this project is to better understand real world use of antihypertensive drugs and factors that influence response to such drugs, with the goal of creating prediction models for use in clinical decision support tools to make personalized HTN management recommendations. The specific research aims include characterizing real world antihypertensive drug prescribing patterns and their determinants (Aim 1), identifying treatment effect modifiers for both effectiveness and safety of two common antihypertensive classes, angiotensin-converting enzyme inhibitors (ACE-Is) and thiazide diuretics (Aim 2) and, developing models for predicting response to ACE-Is and thiazide diuretics to maximize antihypertensive efficacy (Aim 3). This work will leverage observational research methodologies with the OneFlorida Data Trust, a statewide repository of longitudinal EHR data on >8 million Floridians. Dr. Smith's training and experience in clinical pharmacy, public/population health, and HTN care ensure the clinical relevance of the project. His previous clinical HTN research experience and background in applied biostatistics, combined with the proposed training incorporating biomedical informatics, pharmacoepidemiology, multilevel modeling, and leadership, ensure the feasibility of this proposed work and his further development. University of Florida resources and infrastructure, including the UF CTSI, the Biomedical Informatics Program, and the OneFlorida Research Consortium, provide an ideal environment for achieving the proposed objectives and Dr. Smith's long-term goals. Dr. Rhonda Cooper-DeHoff will lead a multidisciplinary mentorship team composed of experts in pharmacoepidemiology (Dr. Almut Winterstein), biostatistics (Dr. Matthew Gurka), biomedical informatics (Dr. Bill Hogan), clinical HTN (Dr. Carl Pepine), and leadership (Dr. Anne Libby). The integrated mentored research experience and training will allow Dr. Smith to compete for R01 funding and become an independent clinician-scientist using observational research methods with large-scale EHR data to improve drug therapy selection for individuals.