Institute of Clinical Research and Evidence Based Medicine, Gansu Provincial Hospital, Lanzhou, Gansu 730000, P.R. China.
Institute of Clinical Research and Evidence Based Medicine, Gansu Provincial Hospital, Lanzhou, Gansu 730000, P.R. China; Chinese Medicine Faculty of Hong Kong Baptist University, Kowloon Tong, Hong Kong, P.R. China; Evidence-Based Medicine Center, School of Basic Medical Science, Lanzhou University, Lanzhou, Gansu 730000, P.R. China.
J Clin Epidemiol. 2019 May;109:20-29. doi: 10.1016/j.jclinepi.2018.12.013. Epub 2018 Dec 21.
The aims of the article were to assess the methodological quality of robotic surgical meta-analyses (MAs) using A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) and to explore the factors of methodological quality.
Robotic surgical MAs published between 2015 and 2018 were identified through a systematical search in PubMed, EMBASE, Cochrane library, and Web of Science databases. The methodological quality of eligible MAs was evaluated by AMSTAR-2. Data extraction and the methodological quality of MAs assessment were double checked by four trained reviewers. The intraclass correlation coefficient (ICC) was used to assess the consistency of quantitative measurements, and the ICC for overall score and score of critical domains were 0.952 and 0.912, respectively. Multivariate regression analysis was used to identify potential factors affecting methodological quality.
A total of 123 MAs focused on 18 surgical locations were included. The findings showed that, regarding quality, only two (1.6%) of 123 MAs were high, two (1.6%) were moderate, two (1.6%) were low, and the remainder 117 (95.1%) were critical low. Multiple linear regression analysis revealed that publishing year and journal rank independently associated with methodological quality of MAs; origin region (P > 0.05), Preferred Reporting Items for Systematic Reviews and Meta-Analyses (P = 0.421), randomized controlled trial enrollment (P = 0.304), and funding support (P = 0.958) did not influence the quality of the MAs. Registration (item 2) and funding reported for individual studies (item 10) showed the poorest adherence in the MAs.
Our study showed that the previously published robotic surgical MAs lack good scientific quality, especially in those published in Q2- to Q4-rated journals. Potential solutions to improve the quality of future robotic surgical MAs include preregistration and funding reported for individual studies.
本文旨在使用评估系统评价的测量工具(AMSTAR-2)评估机器人手术荟萃分析(MA)的方法学质量,并探讨方法学质量的影响因素。
通过在 PubMed、EMBASE、Cochrane 图书馆和 Web of Science 数据库中进行系统检索,确定了 2015 年至 2018 年间发表的机器人手术 MA。使用 AMSTAR-2 评估合格 MA 的方法学质量。四名经过培训的评审员对数据提取和 MA 评估的方法学质量进行了双重检查。使用组内相关系数(ICC)评估定量测量的一致性,总体评分和关键领域评分的 ICC 分别为 0.952 和 0.912。多变量回归分析用于识别影响方法学质量的潜在因素。
共纳入 123 项针对 18 个手术部位的 MA。结果表明,在质量方面,123 项 MA 中仅有 2 项(1.6%)为高,2 项(1.6%)为中,2 项(1.6%)为低,其余 117 项(95.1%)为极低。多变量线性回归分析显示,出版年份和期刊排名独立与 MA 的方法学质量相关;起源地区(P>0.05)、系统评价和荟萃分析的首选报告项目(P=0.421)、随机对照试验纳入(P=0.304)和资助支持(P=0.958)并不影响 MA 的质量。注册(项目 2)和单独研究的资助报告(项目 10)在 MA 中的遵循情况最差。
我们的研究表明,先前发表的机器人手术 MA 缺乏良好的科学质量,尤其是在 Q2 至 Q4 评级期刊发表的 MA。提高未来机器人手术 MA 质量的潜在解决方案包括单独研究的预注册和资助报告。