Gout and Cardiovascular Comorbidities: Disentangling the Interplay with Advanced Methods to Address Measurement Issues

吉田，K-痛风和心血管合并症：用先进的方法解决测量问题的相互作用。--2019/11/01 项目总结 应聘者：我是副流行病学家和临床科学科的医学讲师 布里格姆妇女医院(BWH)和哈佛医学院的风湿病学、炎症和免疫学 学校(HMS)。我的长期目标是发展独立的研究事业，成为一名以患者为中心的研究人员 为风湿病临床研究的方法学进步作出贡献。我在以下领域有过往记录 严格的量化方法的应用、发展和传播。我的背景包括 临床风湿学、药物流行病学和生物统计学。这一有指导的研究经验将整合 我从事痛风和心血管(CV)合并症的临床研究的方法论研究技能。我 将接受丹尼尔·H·所罗门博士的指导，他是一位久负盛名的患者结局研究员，在 风湿性疾病，包括痛风，以及由Robert J.Glynn博士组成的专家共同导师团队 (方法学)，Brendan M.Everett(CV流行病学)和Hyon K.Choi(痛风流行病学)。 环境：我得到了BWH我的部门和部门的大力支持，这确保了85%的保护时间 在5-10年内为与拟议项目有关的研究和职业发展活动规定的项目 年度奖励期。我有经济支持，以补充我的工资和研究相关的费用从司- 锡安，部门，和我的主要导师的资助。我可以访问高性能计算资源 通过BWH和HMS，以及获得Canakinumab抗炎性血栓形成结果研究 (诗篇)数据通过格林博士的支持。合作伙伴研究患者数据注册中心支持Medicare 患者调查中痛风患者的索赔联系和有针对性的登记。 研究：该项目的首要目标是应用、开发和传播最先进的量化 解决风湿病，尤其是痛风中经常遇到的测量问题的方法。 从痛风和并发症的相互作用中得出了三个相关的临床重要问题；这三个目标都是 评估测量问题的影响。目标1“痛风和心血管合并症的横断面关联” 批判性地评估错误分类在理解痛风和心血管疾病人口负担方面的影响- 双胞胎。目标2《痛风和心血管事件的因果调查》严格量化了错误测量的影响- 痛风发作和炎性生物标志物在血清潜在因果关系分析中的作用 尿酸和随后的心血管事件。目标3：“以患者为中心考虑痛风和心血管疾病负担” 正式评估现有多发病指数在衡量患者生活经验方面的有效性 痛风，通过患者报告的结果表现出来。对于我的第一个R01，我计划开发和验证一个自动化的 电子健康档案中日常护理数据的多发病指数估计器，利用知识和技能 在AIMS 1和AIMS 2中获得的错误测量校正以及来自链接数据集的初步数据(例程 数据和患者调查)内置在AIM 3中。 1

Yoshida, K. - Gout and Cardiovascular Comorbidities: Disentangling the Interplay with Advanced Methods to Address Measurement Issues. -- 2019-11-01 PROJECT SUMMARY Candidate: I am an Associate Epidemiologist and Instructor in Medicine in the Section of Clinical Sciences, Division of Rheumatology, Inflammation, and Immunity at Brigham and Women’s Hospital (BWH) and Harvard Medical School (HMS). My long-term goal is to develop an independent research career as a patient-oriented researcher contributing to the methodological advancement of clinical research in rheumatic diseases. I have a track record in the application, development, and dissemination of rigorous quantitative methodologies. My background includes clinical rheumatology, pharmacoepidemiology, and biostatistics. This mentored research experience will integrate my methodological research skills into clinical research focusing on gout and cardiovascular (CV) comorbidities. I will receive mentorship from Dr. Daniel H. Solomon, an established patient outcomes researcher with expertise in rheumatic diseases, including gout, as well as a team of expert co-mentors consisting of Drs. Robert J. Glynn (methodology), Brendan M. Everett (CV epidemiology), and Hyon K. Choi (gout epidemiology). Environment: I have strong support from my Department and Division at BWH, which ensures 85% protected time for the stated project for research and career development activities related to the proposed project during the 5- year award period. I have financial support to supplement my salary and research-related expenses from the Divi- sion, the Section, and my primary mentor’s funding. I have access to high-performance computing resources through BWH and HMS, as well as access to the Canakinumab Anti-inflammatory Thrombosis Outcome Study (CANTOS) data through Dr. Glynn’s support. The Partners Research Patient Data Registry enables Medicare claims linkage and targeted enrollment of gout patients for patient surveys. Research: The overarching goal of the project is to apply, develop, and disseminate state-of-the-art quantitative methods to address measurement issues frequently encountered in rheumatology, and more specifically in gout. Three related clinically important questions are drawn from the interplay of gout and comorbidities; all three aims assess the impact of measurement issues. Aim 1 on “the cross-sectional association of gout and CV comorbidities” critically assesses the impact of misclassification in understanding the population burden of gout and CV comor- bidities. Aim 2 on “causal investigation of gout and CV events” rigorously quantifies the influence of mismeasure- ment of gout flares and inflammatory biomarkers in mediation analysis of the potential causal link between serum uric acid and subsequent CV events. Aim 3 on “patient-centered consideration of gout and CV disease burden” formally evaluates the validity of existing multimorbidity indices in measuring the lived experience of patients with gout, represented through patient-reported outcomes. For my first R01, I plan to develop and validate an automated multimorbidity index estimator for routine care data in the electronic health record, utilizing the knowledge and skills in mismeasurement correction obtained in Aims 1 and 2 as well as preliminary data from linked datasets (routine data and patient survey) built in Aim 3. 1