Comparative Effectiveness of Treat-To-Target Approach versus Routine Care in Management of Gout

痛风是一种高度流行的，痛苦的炎症性关节炎，引起的结晶尿酸在关节 组织中痛风发作会导致生活质量和身体功能受损，导致工作日损失、残疾， 高医疗费用。痛风患者患心血管疾病的风险也会增加 (CVD)肾脏疾病和死亡率。以前的研究表明，降低尿酸盐的潜在有益作用 治疗对心血管疾病和肾功能的风险。在过去的十年里，美国流变学学院 和欧洲反风湿联盟建议采用达标治疗（TTT）方法， 血清尿酸（SUA）水平低于6毫克/分升，以减少急性痛风发作，根据上限的水 尿酸在正常生理条件下的溶解度。然而，2016年美国学院 医师（ACP）的痛风管理指南建议使用降尿酸治疗，但支持 不监测SUA的“治疗以避免症状”策略，而不是TTT策略，因为 文献中的“非决定性”证据。来自不同社会的治疗指南的这种差异促使 需要对痛风治疗策略进行比较有效性研究。 利用来自电子健康记录的高质量观察数据进行严格的流行病学研究 (EHR)或保险索赔数据库可以是比较有效性研究的重要工具。的 本3年期提案的主要目标是提供高质量、及时的比较证据， 两种不同的治疗策略，TTT与常规护理，对痛风管理的有效性。我们将 通过利用链接的合作伙伴EHR-医疗保险数据库（2007-2016年）来实现这一目标;这一链接 数据库包括入组Medicare A/B/D部分的所有年龄≥65岁且在一次就诊时有≥1次就诊的患者 合作伙伴医疗保健医院。我们将获得有关患者的纵向、临床重要数据 人口统计学、体重指数、访视记录、实验室检查结果（包括SUA和血清肌酐）以及 住院和门诊就诊、程序和处方药的所有医疗保险索赔。的两个具体 这项为期3年的提案的目的是：1）检查TTT策略对痛风发作风险的影响， 护理和2）评估TTT策略与常规护理相比对肾脏疾病和CVD风险的影响。 考虑到痛风的大量患病率，痛风的次优管理和缺乏可比较的 试验不同的治疗策略，这项拟议的研究将作出直接的，重要的贡献， 痛风的临床治疗这项工作不仅将研究不同治疗的效果， 痛风发作的治疗策略，以及痛风患者常见的合并症，如肾脏和心血管疾病。 此外，这项研究将促进了解如何提高动态的确定 结果和控制时变混杂，利用先进的和创新的流行病学， 以及与医疗保险索赔相关的大型EHR数据库中的生物信息学方法。

Gout is a highly prevalent, painful inflammatory arthritis, caused by crystallization of uric acid in joint tissues. Gout flares cause impaired quality of life and physical function, leading to lost work days, disability, high healthcare costs. Patients with gout are also known to be at an increased risk of cardiovascular diseases (CVD), kidney disease, and mortality. Previous studies suggest a potential beneficial effect of urate-lowering therapy on the risk of CVD and renal function. Over the past decade, the American College of Rheumatology and the European League Against Rheumatism recommend a treat-to-target (TTT) approach that lowers serum uric acid (SUA) level below 6 mg/dl to reduce acute gout attacks, based on the upper limit of water solubility of uric acid under normal physiologic circumstances. However, the 2016 American College of Physicians (ACP)’ guidelines for gout management recommend use of urate-lowering therapy but support a ‘treat-to-avoid symptoms’ strategy without monitoring SUA rather than the TTT strategy because of ‘inconclusive’ evidence in the literature. Such discrepancies in treatment guidelines from various societies urge the need for comparative effectiveness research of the treatment strategies for gout. Rigorous epidemiologic studies utilizing high-quality observational data from electronic health records (EHR) or insurance claims databases can be an important tool for comparative effectiveness research. The primary objective of this 3-year proposal is to provide high-quality, timely evidence on the comparative effectiveness of two different treatment strategies, TTT versus usual care, for management of gout. We will pursue this objective by utilizing the linked Partners’ EHR-Medicare database (2007-2016); this linked database includes all patients aged ≥65 years enrolled in Medicare Parts A/B/D who had ≥1 encounter at one of the Partners Healthcare hospitals. We will have longitudinal, clinically important data on patients’ demographics, body mass index, visit notes, laboratory results including SUA and serum creatinine, as well as all Medicare claims for inpatient and outpatient visits, procedures, and prescription drugs. The two specific aims of this 3-year proposal are: 1) to examine the effect of TTT strategy on the risk of gout flares versus usual care and 2) to assess the effect of TTT strategy on the risk of kidney disease and CVD versus usual care. Given the substantial prevalence of gout, the suboptimal management of gout and lack of comparative trial of different treatment strategies, this proposed study will make an immediate, important contribution to the management of gout in clinical practice. This work will not only investigate the effects of different treatment strategies on gout flares but also on common comorbidities in patients with gout such as kidney and CVD. Furthermore, this proposed study will advance understanding of how to improve ascertainment of dynamic outcomes and control for time-varying confounding by utilizing sophisticated and innovative epidemiologic as well as bioinformatics methods in a large EHR database linked with Medicare claims.