Tang Hongzhi, Xu Guixing, Zheng Qianhua, Cheng Ying, Zheng Hui, Li Juan, Yin Zihan, Liang Fanrong, Chen Jiao
Outpatient department of Sichuan orthopedic hospital.
The Acupuncture and Tuina School, The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine.
Medicine (Baltimore). 2020 Apr;99(14):e19668. doi: 10.1097/MD.0000000000019668.
The current evidence confirms the effectiveness and safety of several drug interventions in the treatment of acute flares of gout, however, the most preferred drugs are still unclear. We, therefore, seek to conduct a network meta-analysis that can systematically compare non-steroidal anti-inflammatory drugs (NSAIDs), COXIBs, colchicine, hormones, or IL-1 receptor antagonists, etc. for acute gout based on the latest evidence.
Nine online databases are searched with inception to September 1, 2019; there will be no language restrictions on the included trials. Randomized controlled trials that include patients with acute flares of gout receiving drug therapy versus a control group will be included. The selection of studies, risk of bias assessment and data extraction will be conducted by 2 independent researchers. Bayesian network meta-analysis is applied using the Markov chain Monte Carlo method with Stata or R. The dichotomous data will be presented as risk ratios with 95% CIs and the continuous data will be presented as weighted mean differences or standardized mean differences with 95% CIs. Evidence quality will be evaluated using the GRADE system.
This network meta-analysis will not involve private information from personal or imperil their rights, so, ethical approval is not required. The results of this network meta-analysis may be published in a journal or publicized in concerned conferences.
目前的证据证实了几种药物干预措施在治疗痛风急性发作方面的有效性和安全性,然而,最优选的药物仍不明确。因此,我们试图进行一项网状Meta分析,以根据最新证据系统地比较非甾体抗炎药(NSAIDs)、环氧化酶抑制剂(COXIBs)、秋水仙碱、激素或白细胞介素-1受体拮抗剂等用于急性痛风的疗效。
检索9个在线数据库,检索时间从建库至2019年9月1日;纳入的试验将不受语言限制。纳入痛风急性发作患者接受药物治疗与对照组比较的随机对照试验。研究的选择、偏倚风险评估和数据提取将由2名独立研究人员进行。使用马尔可夫链蒙特卡罗方法,通过Stata或R软件进行贝叶斯网状Meta分析。二分数据将以风险比及95%置信区间呈现,连续数据将以加权均数差或标准化均数差及95%置信区间呈现。证据质量将使用GRADE系统进行评估。
本网状Meta分析不会涉及个人隐私信息或损害其权利,因此无需伦理批准。本网状Meta分析的结果可能会发表在期刊上或在相关会议上公布。