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Core outcome set for peripheral regional anesthesia research: a systematic review and Delphi study

德尔菲法 医学 结果(博弈论) 芯(光纤) 德尔菲 集合(抽象数据类型) 系统回顾 相关性(法律) 梅德林 计算机科学 人工智能 电信 操作系统 数理经济学 数学 程序设计语言 法学 政治学
作者
Jeremy Hill,T Ashken,Simeon J. West,Alan Macfarlane,Kariem El‐Boghdadly,Éric Albrecht,Ki Jinn Chin,Ben Fox,Ashwani Gupta,Stephen C. Haskins,Nat Haslam,Rosemary Hogg,Anil Hormis,David F. Johnston,Edward R. Mariano,Peter Merjavy,Timothy Moll,James M. Parry,Amit Pawa,K. Russon,Maria Paz Sebastian,Lloyd Turbitt,Jonathan Womack,Maria Chazapis
出处
期刊:Regional Anesthesia and Pain Medicine [BMJ]
卷期号:47 (11): 691-697 被引量:10
标识
DOI:10.1136/rapm-2022-103751
摘要

There is heterogeneity among the outcomes used in regional anesthesia research.We aimed to produce a core outcome set for regional anesthesia research.We conducted a systematic review and Delphi study to develop this core outcome set. A systematic review of the literature from January 2015 to December 2019 was undertaken to generate a long list of potential outcomes to be included in the core outcome set. For each outcome found, the parameters such as the measurement scale, timing and definitions, were compiled. Regional anesthesia experts were then recruited to participate in a three-round electronic modified Delphi process with incremental thresholds to generate a core outcome set. Once the core outcomes were decided, a final Delphi survey and video conference vote was used to reach a consensus on the outcome parameters.Two hundred and six papers were generated following the systematic review, producing a long list of 224 unique outcomes. Twenty-one international regional anesthesia experts participated in the study. Ten core outcomes were selected after three Delphi survey rounds with 13 outcome parameters reaching consensus after a final Delphi survey and video conference.We present the first core outcome set for regional anesthesia derived by international expert consensus. These are proposed not to limit the outcomes examined in future studies, but rather to serve as a minimum core set. If adopted, this may increase the relevance of outcomes being studied, reduce selective reporting bias and increase the availability and suitability of data for meta-analysis in this area.
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