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

痛风是一种非常普遍的、疼痛的炎症性关节炎，由关节内尿酸结晶引起。 纸巾。痛风发作会损害生活质量和身体功能，导致工作日减少、残疾、 高昂的医疗成本。众所周知，痛风患者患心血管疾病的风险也会增加。 (心血管疾病)、肾脏疾病和死亡率。先前的研究表明，降低尿酸有潜在的有益作用。 治疗对心血管疾病风险和肾功能的影响。在过去的十年里，美国风湿病学会 欧洲抗风湿病联盟建议采用治疗靶向(TTT)的方法，降低 基于水的上限，血尿酸水平低于6毫克/分升可减少急性痛风发作 尿酸在正常生理条件下的溶解度。然而，2016年美国大学 医生(ACP)的痛风治疗指南建议使用降尿酸治疗，但支持 在不监测血尿酸而不是TTT策略的情况下，由于 文献中的“不确定”证据。来自不同社会的治疗指南中的这种差异敦促 有必要对痛风的治疗策略进行疗效比较研究。 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成为比较有效性研究的重要工具。这个 这份为期3年的建议书的主要目标是提供高质量、及时的证据，说明 痛风管理的两种不同治疗策略的有效性，TTT和常规护理。我们会 通过利用关联合作伙伴的EHR-Medicare数据库(2007-2016)实现这一目标； 数据库包括参加联邦医疗保险A/B/D部分的所有65岁的≥患者，他们在一次中遇到了≥1 合作伙伴医疗保健医院。我们将拥有关于患者的纵向、临床上重要的数据 人口统计数据，体重指数，就诊记录，实验室结果，包括血尿酸和血清肌酐，以及 所有住院和门诊就诊、手术和处方药的医疗保险索赔。这两个具体的 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策略与常规护理对肾脏疾病和心血管疾病风险的影响。 鉴于痛风的广泛流行，痛风的次优管理和缺乏可比性 不同治疗策略的试验，这项拟议的研究将立即对 痛风在临床中的处理。这项工作不仅将调查不同处理的效果 痛风发作的治疗策略，以及痛风患者常见的并发症，如肾脏和心血管疾病。 此外，这项拟议的研究将促进对如何提高动态确定的理解 利用复杂和创新的流行病学AS对时变混淆的结果和控制 以及与医疗保险索赔相关的大型电子病历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.