Luz Christian F, Berends Matthijs S, Zhou Xuewei, Lokate Mariëtte, Friedrich Alex W, Sinha Bhanu, Glasner Corinna
Department of Medical Microbiology and Infection Prevention, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, Netherlands.
Department of Medical Epidemiology, Certe Medical Diagnostics and Advice Foundation, Groningen, The Netherlands.
JAC Antimicrob Resist. 2023 Jan 18;5(1):dlac143. doi: 10.1093/jacamr/dlac143. eCollection 2023 Feb.
Insights about local antimicrobial resistance (AMR) levels and epidemiology are essential to guide decision-making processes in antimicrobial use. However, dedicated tools for reliable and reproducible AMR data analysis and reporting are often lacking. We aimed to compare traditional data analysis and reporting versus a new approach for reliable and reproducible AMR data analysis in a clinical setting.
Ten professionals who routinely work with AMR data were provided with blood culture test results including antimicrobial susceptibility results. Participants were asked to perform a detailed AMR data analysis in a two-round process: first using their software of choice and next using our newly developed software tool. Accuracy of the results and time spent were compared between both rounds. Finally, participants rated the usability using the System Usability Scale (SUS).
The mean time spent on creating the AMR report reduced from 93.7 to 22.4 min ( < 0.001). Average task completion per round changed from 56% to 96% ( < 0.05). The proportion of correct answers in the available results increased from 37.9% in the first to 97.9% in the second round ( < 0.001). Usability of the new tools was rated with a median of 83.8 (out of 100) on the SUS.
This study demonstrated the significant improvement in efficiency and accuracy in standard AMR data analysis and reporting workflows through open-source software. Integrating these tools in clinical settings can democratize the access to fast and reliable insights about local microbial epidemiology and associated AMR levels. Thereby, our approach can support evidence-based decision-making processes in the use of antimicrobials.
了解当地抗菌药物耐药性（AMR）水平及流行病学情况对于指导抗菌药物使用的决策过程至关重要。然而，可靠且可重复的AMR数据分析和报告的专用工具往往匮乏。我们旨在比较传统的数据分析和报告与一种用于临床环境中可靠且可重复的AMR数据分析的新方法。
向十名经常处理AMR数据的专业人员提供血培养检测结果，包括抗菌药物敏感性结果。要求参与者通过两轮流程进行详细的AMR数据分析：首先使用他们选择的软件，然后使用我们新开发的软件工具。比较两轮结果的准确性和所花费的时间。最后，参与者使用系统可用性量表（SUS）对可用性进行评分。
创建AMR报告所花费的平均时间从93.7分钟减少到22.4分钟（<0.001）。每轮的平均任务完成率从56%变为96%（<0.05）。可用结果中正确答案的比例从第一轮的37.9%增加到第二轮的97.9%（<0.001）。新工具的可用性在SUS上的评分为中位数83.8（满分100）。
本研究表明，通过开源软件，标准AMR数据分析和报告工作流程的效率和准确性有了显著提高。将这些工具整合到临床环境中可以使获取有关当地微生物流行病学和相关AMR水平的快速可靠见解变得更加普及。因此，我们的方法可以支持抗菌药物使用中的循证决策过程。
