From the Department of Computer Science, Vanderbilt University, Nashville, Tennessee.
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas.
J Patient Saf. 2024 Aug 1;20(5):e45-e58. doi: 10.1097/PTS.0000000000001220. Epub 2024 Mar 12.
This article aims to assess the reproducibility of Manufacturer and User Facility Device Experience (MAUDE) data-driven studies by analyzing the data queries used in their research processes.
Studies using MAUDE data were sourced from PubMed by searching for "MAUDE" or "Manufacturer and User Facility Device Experience" in titles or abstracts. We manually chose articles with executable queries. The reproducibility of each query was assessed by replicating it in the MAUDE Application Programming Interface. The reproducibility of a query is determined by a reproducibility coefficient that ranges from 0.95 to 1.05. This coefficient is calculated by comparing the number of medical device reports (MDRs) returned by the reproduced queries to the number of reported MDRs in the original studies. We also computed the reproducibility ratio, which is the fraction of reproducible queries in subgroups divided by the query complexity, the device category, and the presence of a data processing flow.
As of August 8, 2022, we identified 523 articles from which 336 contained queries, and 60 of these were executable. Among these, 14 queries were reproducible. Queries using a single field like product code, product class, or brand name showed higher reproducibility (50%, 33.3%, 31.3%) compared with other fields (8.3%, P = 0.037). Single-category device queries exhibited a higher reproducibility ratio than multicategory ones, but without statistical significance (27.1% versus 8.3%, P = 0.321). Studies including a data processing flow had a higher reproducibility ratio than those without, although this difference was not statistically significant (42.9% versus 17.4%, P = 0.107).
Our findings indicate that the reproducibility of queries in MAUDE data-driven studies is limited. Enhancing this requires the development of more effective MAUDE data query strategies and improved application programming interfaces.
本文旨在通过分析研究过程中使用的数据查询来评估制造商和用户设施设备体验(MAUDE)数据驱动研究的可重复性。
通过在标题或摘要中搜索“MAUDE”或“Manufacturer and User Facility Device Experience”,从 PubMed 中获取使用 MAUDE 数据的研究。我们手动选择具有可执行查询的文章。通过在 MAUDE 应用程序编程接口中复制每个查询来评估每个查询的可重复性。查询的可重复性通过再现系数来确定,该系数范围在 0.95 到 1.05 之间。该系数通过比较复制查询返回的医疗器械报告 (MDR) 数量与原始研究中报告的 MDR 数量来计算。我们还计算了可重复性比率,这是亚组中可重复查询的分数除以查询复杂性、设备类别和数据处理流程的存在。
截至 2022 年 8 月 8 日,我们从 523 篇文章中确定了 336 篇包含查询的文章,其中 60 篇可执行。在这些文章中,有 14 个查询是可重复的。与其他字段相比,使用产品代码、产品类别或品牌名称等单个字段的查询具有更高的可重复性(50%、33.3%、31.3%)(P=0.037)。单类别设备查询的可重复性比率高于多类别查询,但无统计学意义(27.1%比 8.3%,P=0.321)。包含数据处理流程的研究比没有数据处理流程的研究具有更高的可重复性比率,尽管这种差异无统计学意义(42.9%比 17.4%,P=0.107)。
我们的研究结果表明,MAUDE 数据驱动研究中查询的可重复性有限。提高这一点需要开发更有效的 MAUDE 数据查询策略和改进应用程序编程接口。