Renal Division, Shanxi Medical University Second Hospital, Shanxi Kidney Disease Institute, Taiyuan, Shanxi, China.
Renal Division, Shanxi Medical University Second Hospital, Shanxi Kidney Disease Institute, Taiyuan, Shanxi, China
BMJ Open. 2023 Feb 8;13(2):e059096. doi: 10.1136/bmjopen-2021-059096.
Hyperuricaemia has been implicated in the development of kidney function in populations with chronic kidney disease; however, the benefits of urate-lowering therapy (ULT) remain uncertain in different clinical studies. The different kidney functions of enrolled populations and distinct pharmacokinetic characteristics of ULT might be of the essence for the contrasting results. In this study, we will synthesise all available data from randomised controlled trials (RCTs) and cohort studies, then evaluate the outcomes of ULT in patients stratified by different estimated glomerular filtration rate (eGFR) stratifications. Furthermore, we will attempt to explore a relatively optimal ULT regimen using a Bayesian network meta-analysis in different eGFRs.
We searched published and unpublished data from MEDLINE, EMBASE, the Cochrane Central Register of Controlled trials and ClinicalTrials.gov website (before March 2022) for RCTs and cohort studies without language restriction. In the pairwise meta-analysis, all regimens of ULT will be pooled as a whole and compared with controls in different eGFRs. The random-effects model will be applied to generate the summary values using the software Stata V.12.0 (StataCorp). Network meta-analysis within a Bayesian framework will be conducted to explore the relative efficacy profiles of different ULTs and to find optimal ULT in different eGFRs. The software of WinBUGS V.1.4.3 and R2WinBUGS package of R V.3.1.1 will be used in the network meta-analysis. Primary outcomes will be the occurrence of major cardiovascular events and kidney failure events. Secondary outcomes will include the rate of change in eGFR per year, all-cause death, changes in serum uric acid level and major adverse events. Two authors will independently review study selection, data extraction and quality assessment.
The meta-analysis does not require ethical certification. The results will be disseminated through publication in a peer-reviewed journal and through presentations at academic conferences.
CRD42021226163.
高尿酸血症与慢性肾脏病患者的肾功能下降有关;然而,不同临床研究中降尿酸治疗(ULT)的获益仍不确定。不同研究人群的肾功能以及 ULT 的不同药代动力学特征可能是导致结果不一致的关键因素。在本研究中,我们将综合所有随机对照试验(RCT)和队列研究的数据,然后评估根据不同估计肾小球滤过率(eGFR)分层的 ULT 对患者结局的影响。此外,我们将尝试通过贝叶斯网络meta 分析在不同 eGFR 中探索相对较优的 ULT 方案。
我们检索了 MEDLINE、EMBASE、Cochrane 中心对照试验注册库和 ClinicalTrials.gov 网站(截至 2022 年 3 月)中已发表和未发表的数据,纳入 RCT 和队列研究,无语言限制。在两两meta 分析中,我们将所有 ULT 方案作为一个整体与不同 eGFR 中的对照组进行比较。使用 Stata V.12.0(StataCorp)软件采用随机效应模型生成汇总值。将在贝叶斯框架内进行网络 meta 分析,以探索不同 ULT 的相对疗效特征,并找到不同 eGFR 中最优的 ULT。网络 meta 分析将使用 WinBUGS V.1.4.3 软件和 R V.3.1.1 的 R2WinBUGS 包。两位作者将独立审查研究选择、数据提取和质量评估。
meta 分析不需要伦理认证。研究结果将通过发表在同行评议的期刊和学术会议上的报告进行传播。
PROSPERO 注册号:CRD42021226163。