TY - JOUR AU - Grizzle, Amy J AU - Horn, John AU - Collins, Carol AU - Schneider, Jodi AU - Malone, Daniel C AU - Stottlemyer, Britney AU - Boyce, Richard David PY - 2019 DA - 2019/01/04 TI -识别药物相互作用专家用于寻找潜在药物相互作用证据的常用方法:基于网络的调查JO - J Med Internet Res SP - e11182 VL - 21 IS - 1 KW -药物相互作用KW -药物相互作用专家KW -潜在的药物相互作用KW -调查AB -背景:预防药物相互作用是使患者从药物中获益最大化的重要目标。总结潜在的药物-药物相互作用(PDDI)以支持临床决策是具有挑战性的，并且PDDI证据没有单一的存储库。此外，compendia和其他来源之间的不一致性已经得到了很好的记录。迄今为止，关于pddi的完整和当前证据的标准搜索策略尚未开发或验证。目的:本研究旨在确定常规评估此类证据的专家使用的进行PDDI文献检索的常用方法。方法:我们通过电子邮件邀请70名药物信息专家，包括药典编辑、知识库供应商和临床医生，完成对PDDI证据识别的调查。我们创建了一个基于网络的调查，其中包括以下问题:(1)开发和执行搜索;(2)使用的资源，例如数据库、汇编、搜索引擎等;(3)用于搜索特定PDDI信息的关键字类型; (4) study types included and excluded in searches; and (5) search terms used. Search strategy questions focused on 6 topics of the PDDI information—(1) that a PDDI exists; (2) seriousness; (3) clinical consequences; (4) management options; (5) mechanism; and (6) health outcomes. Results: Twenty participants (response rate, 20/70, 29%) completed the survey. The majority (17/20, 85%) were drug information specialists, drug interaction researchers, compendia editors, or clinical pharmacists, with 60% (12/20) having >10 years’ experience. Over half (11/20, 55%) worked for clinical solutions vendors or knowledge-base vendors. Most participants developed (18/20, 90%) and conducted (19/20, 95%) search strategies without librarian assistance. PubMed (20/20, 100%) and Google Scholar (11/20, 55%) were most commonly searched for papers, followed by Google Web Search (7/20, 35%) and EMBASE (3/20, 15%). No respondents reported using Scopus. A variety of subscription and open-access databases were used, most commonly Lexicomp (9/20, 45%), Micromedex (8/20, 40%), Drugs@FDA (17/20, 85%), and DailyMed (13/20, 65%). Facts and Comparisons was the most commonly used compendia (8/20, 40%). Across the 6 attributes of interest, generic drug name was the most common keyword used. Respondents reported using more types of keywords when searching to identify the existence of PDDIs and determine their mechanism than when searching for the other 4 attributes (seriousness, consequences, management, and health outcomes). Regarding the types of evidence useful for evaluating a PDDI, clinical trials, case reports, and systematic reviews were considered relevant, while animal and in vitro data studies were not. Conclusions: This study suggests that drug interaction experts use various keyword strategies and various database and Web resources depending on the PDDI evidence they are seeking. Greater automation and standardization across search strategies could improve one’s ability to identify PDDI evidence. Hence, future research focused on enhancing the existing search tools and designing recommended standards is needed. SN - 1438-8871 UR - //www.mybigtv.com/2019/1/e11182/ UR - https://doi.org/10.2196/11182 UR - http://www.ncbi.nlm.nih.gov/pubmed/30609981 DO - 10.2196/11182 ID - info:doi/10.2196/11182 ER -