Zoch Michele, Gierschner Christian, Andreeff Anne-Katrin, Henke Elisa, Sedlmayr Martin, Müller Gabriele, Tippmann Jenny, Hebestreit Helge, Choukair Daniela, Hoffmann Georg F, Fritz-Kebede Fleur, Toepfner Nicole, Berner Reinhard, Biergans Stephanie, Verbücheln Raphael, Schaaf Jannik, Fleck Julia, Wirth Felix Nikolaus, Schepers Josef, Prasser Fabian
Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Center for Evidence-based Healthcare, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Digit Health. 2024 Aug 10;10:20552076241265219. doi: 10.1177/20552076241265219. eCollection 2024 Jan-Dec.
Unlocking the potential of routine medical data for clinical research requires the analysis of data from multiple healthcare institutions. However, according to German data protection regulations, data can often not leave the individual institutions and decentralized approaches are needed. Decentralized studies face challenges regarding coordination, technical infrastructure, interoperability and regulatory compliance. Rare diseases are an important prototype research focus for decentralized data analyses, as patients are rare by definition and adequate cohort sizes can only be reached if data from multiple sites is combined.
Within the project "Collaboration on Rare Diseases", decentralized studies focusing on four rare diseases (cystic fibrosis, phenylketonuria, Kawasaki disease, multisystem inflammatory syndrome in children) were conducted at 17 German university hospitals. Therefore, a data management process for decentralized studies was developed by an interdisciplinary team of experts from medicine, public health and data science. Along the process, lessons learned were formulated and discussed.
The process consists of eight steps and includes sub-processes for the definition of medical use cases, script development and data management. The lessons learned include on the one hand the organization and administration of the studies (collaboration of experts, use of standardized forms and publication of project information), and on the other hand the development of scripts and analysis (dependency on the database, use of standards and open source tools, feedback loops, anonymization).
This work captures central challenges and describes possible solutions and can hence serve as a solid basis for the implementation and conduction of similar decentralized studies.
挖掘常规医学数据用于临床研究的潜力需要对来自多个医疗机构的数据进行分析。然而，根据德国数据保护法规，数据通常不能离开各个机构，因此需要采用分散式方法。分散式研究在协调、技术基础设施、互操作性和法规遵从性方面面临挑战。罕见病是分散式数据分析的一个重要原型研究重点，因为从定义上来说患者数量稀少，只有合并多个地点的数据才能达到足够的队列规模。
在“罕见病合作”项目中，德国17所大学医院开展了针对四种罕见病（囊性纤维化、苯丙酮尿症、川崎病、儿童多系统炎症综合征）的分散式研究。因此，一个由医学、公共卫生和数据科学领域的专家组成的跨学科团队开发了一种用于分散式研究的数据管理流程。在此过程中，总结并讨论了经验教训。
该流程包括八个步骤，涵盖了医学用例定义、脚本开发和数据管理等子流程。经验教训一方面包括研究的组织和管理（专家协作、使用标准化表格以及项目信息发布），另一方面包括脚本开发和分析（对数据库的依赖、标准和开源工具的使用、反馈循环、匿名化）。
这项工作抓住了核心挑战并描述了可能的解决方案，因此可为开展类似的分散式研究提供坚实基础。
