Novel approaches to identify and quantify the impact of drug-drug interactions in older adults with diabetes, with a focus on multimorbidity and polypharmacy

项目摘要/摘要 本研究科学家发展奖(K01)申请书由Katsiaryna Bykov提交， PPharmd，ScD，对PA-20-190的回应。拜科夫博士是副流行病学家和医学讲师 在布里格姆妇女医院药物流行病学和药物经济学分部 哈佛医学院。她的长期目标是产生临床上可操作的证据，以指导安全和 在多药并存和多病并存的复杂环境中，老年人有效使用药物。她最近 研究的重点是开发药物检测和评价的创新方法。 使用电子医疗数据的相互作用及其临床影响。拜科夫博士的目标是获得 临床老年病学和糖尿病、老年药物流行病学和数据的高级统计方法 挖掘和信号检测，将她的方法论工作转化为临床相关研究。为了实现她的目标 AIMS，Bykov博士提出了一个为期5年的职业发展和指导性研究计划，以 一种有效和高效的药物相互作用检测和评价系统的开发 患有糖尿病的老年人。在该司高效和支持性的研究环境中 拜科夫博士将与一个由导师和合作者组成的跨学科团队合作 来自哈佛大学和宾夕法尼亚大学的机构，他们拥有深厚的专业知识和 在她提议的培训的具体实质性领域中的国家/国际声誉：临床老年病学和 糖尿病，用于数据挖掘和信号检测的高级统计方法，药物-药物相互作用研究， 和药物流行病学。该提案的总体目标是为 检测和评估老年糖尿病患者的临床相关药物-药物相互作用。拜科夫博士 提出了一种新的两阶段筛选，将在电子医疗数据中识别潜在的相互作用的药物 随后将对已确定的相互作用进行严格的假设驱动评估。该方法将使用 医疗保险索赔数据，可在该司获得，并链接到电子健康记录和门诊实验室 针对部分患者的测试结果，并将针对异质性和复杂性进行专门定制 老年人的健康状况，包括不同程度的虚弱和多发病。拟议的工作将 提供高质量和临床相关的证据，帮助老年糖尿病患者及其临床医生 为了更全面地评估在存在其他药物的情况下添加新药的好处和风险 并存疾病。从这项工作中获得的知识将影响到数百万患有糖尿病的老年人 并将为申请者提供成为独立调查员的坚实背景， 最终，在研究老龄化人口的药物使用和结果方面处于领先地位。

PROJECT SUMMARY / ABSTRACT This application for a Mentored Research Scientist Development Award (K01) is submitted by Katsiaryna Bykov, PharmD, ScD, in response to PA-20-190. Dr. Bykov is an Associate Epidemiologist and Instructor in Medicine in the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital and Harvard Medical School. Her long-term goal is to generate clinically actionable evidence to guide the safe and effective use of medications in older adults in the complex milieu of polypharmacy and multimorbidity. Her recent research has focused on developing innovative methods for the detection and evaluation of drug-drug interactions and their clinical impact using electronic healthcare data. Dr. Bykov aims to acquire expertise in clinical geriatrics and diabetology, geriatric pharmacoepidemiology, and advanced statistical methods for data mining and signal detection to translate her methodological work into clinically relevant research. To achieve her aims, Dr. Bykov proposes a 5-year program of career development and mentored research centered on the development of a valid and efficient system for the detection and evaluation of drug-drug interactions in older adults with diabetes. Within the highly productive and supportive research environment of the Division of Pharmacoepidemiology, Dr. Bykov will work with an interdisciplinary team of mentors and collaborators drawn from across institutions at Harvard and University of Pennsylvania who have deep expertise and national/international reputations in the specific substantive areas of her proposed training: clinical geriatrics and diabetology, advanced statistical methods for data mining and signal detection, drug-drug interactions research, and pharmacoepidemiology. The overarching objective of the proposal is to develop a framework for the detection and evaluation of clinically relevant drug-drug interactions in older adults with diabetes. Dr. Bykov proposes a novel 2-stage screening that will identify potentially interacting drugs in electronic healthcare data and will be followed by a rigorous hypothesis-driven evaluation of identified interactions. The approach will use Medicare claims data, available in the Division and linked to electronic health records and outpatient laboratory test results for a subset of patients, and will be specifically tailored to address the heterogeneity and complexity of health status in older adults, including varying degrees of frailty and multimorbidity. The proposed work will provide high quality and clinically relevant evidence that will help older adults with diabetes and their clinicians to more fully assess the benefits and risks of adding a new medication in the presence of other medications and co-morbidities. The knowledge gained from this work will impact several million older adults with diabetes in the US and will provide the applicant with a solid background to become an independent investigator and, ultimately, a leader in the study of medication use and outcomes in aging populations.