Advancing Personalized Hypertension Care through Big Data Science

通过大数据科学推进个性化高血压护理

• 批准号: 10229379
• 负责人: Steven Michael Smith
• 金额: $ 15.23万
• 依托单位: UNIVERSITY OF FLORIDA
• 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/10229379
• 关键词: Accounting; Achievement; Adoption; Adverse event; Affect; American; Angiotensin-Converting Enzyme Inhibitors; Antihypertensive Agents; Biometry; Blood Pressure; Caring; Cessation of life; Characteristics; Chronic; Chronic Disease; Clinical; Clinical Data; Cohort Studies; Data; Data Set; Development; Diuretics; Drug Prescriptions; Drug Side Effects; Drug Utilization; Effectiveness; Electronic Health Record; Ensure; Environment; Florida; Foundations; Funding; Future; Goals; Health; Health system; Heterogeneity; Hypertension; Individual; Infrastructure; Knowledge; Lead; Leadership; Measurement; Measures; Mentors; Mentorship; Methods; Modeling; National Heart, Lung, and Blood Institute; Observation in research; Observational Study; Outcome; Patient-Focused Outcomes; Patients; Pattern; Personal Satisfaction; Pharmaceutical Preparations; Pharmacoepidemiology; Pharmacotherapy; Pharmacy facility; Population; Public Health; Recommendation; Research; Research Design; Research Methodology; Research Personnel; Resources; Risk; Risk Factors; Safety; Scientist; Selection for Treatments; Standardization; Statistical Models; Testing; Thiazide Diuretics; Training; Trust; Uncertainty; Universities; Validation; Variant; Work; base; big-data science; biomedical informatics; blood pressure regulation; care outcomes; career development; clinical data repository; clinical decision support; clinically actionable; clinically relevant; design; experience; hypertension control; hypertension treatment; improved; individual patient; individual response; multidisciplinary; multilevel analysis; optimal treatments; patient response; personalized approach; personalized care; population health; predicting response; predictive modeling; programs; repository; response; routine practice; skills; sound; support tools; treatment effect; treatment response; unnecessary treatment

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.

项目摘要。当前高血压（HTN）治疗的试错药物选择范式 在近半个世纪里基本保持不变。个性化护理一直具有挑战性 因为患者和临床医生往往缺乏足够的证据来告知个人护理决策。但是， 电子健康记录（EHR）的广泛采用为使用可靠收集的临床数据创造了机会。 数据来提供证据。应用因果推理的原则，这些数据可用于识别临床 影响观察到的治疗反应变化的因素，反过来，将这些因素纳入 用于预测个体未来治疗反应的统计模型。本次NHLBI K 01的统一主题 建议是指导的职业发展博士史蒂芬M。史密斯这项提议将加速他的 过渡到一个独立的研究人员，并建立基础，实现他的长期目标，使用 合理收集临床数据，通过个性化治疗显著改善患者的健康和福祉 在乎史密斯博士的目标是更好地了解抗高血压药物的真实的世界使用， 影响对这些药物的反应的因素，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。具体研究目标 包括表征真实的世界抗高血压药物处方模式及其决定因素（目标1）， 确定两种常用降压药的有效性和安全性的治疗效果修饰剂 类，血管紧张素转换酶抑制剂（ACE-Is）和噻嗪类利尿剂（Aim 2）， 预测ACE-Is和噻嗪类利尿剂反应的模型，以最大化抗高血压疗效（目标3）。 这项工作将利用观测研究方法与OneFlorida数据信托，全州范围内 超过800万佛罗里达人的纵向EHR数据库。史密斯医生的临床训练和经验 药学、公共/人口健康和HTN护理确保了该项目的临床相关性。他以前 临床HTN研究经验和应用生物统计学背景，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 这项工作的可行性和他的进一步发展。佛罗里达大学资源和基础设施， 包括UF CTSI，生物医学信息学计划和OneFlorida研究联盟，提供 一个理想的环境，以实现拟议的目标和史密斯博士的长期目标。朗达医生 Cooper-DeHoff将领导一个由药物流行病学专家组成的多学科导师团队 (Dr. Almut Winterstein）、生物统计学（Matthew Gurka博士）、生物医学信息学（Bill Hogan博士）、临床HTN (Dr.卡尔·佩平）和领导力（安妮·利比博士）。综合指导研究经验和培训 将允许史密斯博士竞争R 01资金，并成为一个独立的临床科学家使用 大规模EHR数据的观察性研究方法，以改善个体的药物治疗选择。