自动化生成抗菌药物耐药监测报告：涉及七个国家七家医院的概念验证研究。
Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries.
机构信息
Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom.
出版信息
J Med Internet Res. 2020 Oct 2;22(10):e19762. doi: 10.2196/19762.
BACKGROUND
Reporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national, and global levels. However, analyzing data and generating a report are time consuming and often require trained personnel.
OBJECTIVE
This study aimed to develop and test an application that can support a local hospital to analyze routinely collected electronic data independently and generate AMR surveillance reports rapidly.
METHODS
An offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language (R Project for Statistical Computing). The application can be run by double clicking on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested on Microsoft Windows 10 and 7 using open access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People's Democratic Republic, Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam.
RESULTS
We developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyze their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and CSV formats). The data files could be those exported from WHONET or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from https://www.amass.website/. The participating hospitals tested the application and deposited their AMR surveillance reports in an open access data repository.
CONCLUSIONS
The AMASS is a useful tool to support the generation and sharing of AMR surveillance reports.
背景
定期报告累积抗菌药物敏感性测试数据对于在地方、国家和全球各级制定抗菌药物耐药性（AMR）行动计划至关重要。然而，分析数据和生成报告既费时又费力，通常需要经过培训的人员。
目的
本研究旨在开发和测试一种应用程序，该程序可以支持当地医院独立分析常规收集的电子数据并快速生成 AMR 监测报告。
方法
我们使用 R 编程语言（R 项目统计计算）编写了一个离线应用程序，用于从常规可用的微生物学和医院数据文件中生成标准化的 AMR 监测报告。该应用程序可以通过双击应用程序文件运行，无需用户进一步输入。数据分析程序和报告内容是根据世界卫生组织全球抗菌药物耐药性监测系统（WHO GLASS）的建议制定的。我们在 Microsoft Windows 10 和 7 上使用开放访问示例数据集对该应用程序进行了测试。然后，我们在柬埔寨、老挝人民民主共和国、缅甸、尼泊尔、泰国、英国和越南的七家医院独立测试了该应用程序。
结果
我们开发了 AutoMated tool for Antimicrobial resistance Surveillance System（AMASS），它可以支持临床微生物学实验室在现场分析其微生物学和医院数据文件（以 CSV 或 Excel 格式），并迅速生成 AMR 监测报告（以 PDF 和 CSV 格式）。数据文件可以是从 WHONET 或其他实验室信息系统导出的文件。自动生成的报告仅包含摘要数据，没有患者标识符。AMASS 应用程序可从 https://www.amass.website/ 下载。参与的医院测试了该应用程序，并将其 AMR 监测报告存入开放获取数据存储库。
结论
AMASS 是支持生成和共享 AMR 监测报告的有用工具。
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