Data Science and Evaluation, American Heart Association, Dallas, TX (C.B., L.W., V.M., P.M., H.H., H.P., K.T., J.L.H., J.Z., X.F.).
Lillehei Heart Institute, University of Minnesota, Minneapolis (J.L.H.).
Circ Cardiovasc Qual Outcomes. 2024 Sep;17(9):e010967. doi: 10.1161/CIRCOUTCOMES.124.010967. Epub 2024 Aug 22.
The American Heart Association's Get With The Guidelines (GWTG) has emerged as a vital resource in advancing the standards and practices of inpatient care across stroke, heart failure, coronary artery disease, atrial fibrillation, and resuscitation focus areas. The GWTG registry data have also created new opportunities for secondary use of real-world clinical data in biomedical research. Our goal was to implement a scalable database with an integrated user interface (UI) to improve GWTG data management and accessibility.
The curation of registry data begins by going through a data processing and quality control pipeline programmed in Python. This pipeline includes data cleaning and record exclusion, variable derivation and unit harmonization, limited data set preparation, and documentation generation of the registry data. The database was built using PostgreSQL, and integrations between the database and the UI were built using the Django Web Framework in Python. Smaller subsets of data were created using SQLite database files for distribution purposes. Use cases of these tools are provided in the article.
We implemented an automated data curation pipeline, centralized database, and UI application for the American Heart Association GWTG registry data. The database and the UI are accessible through a Precision Medicine Platform workspace. As of March 2024, the database contains over 13.2 million cleaned GWTG patient records. The SQLite subsets benefit researchers by optimizing data extraction and manipulation using Structured Query Language. The UI improves accessibility for nontechnical researchers by presenting data in a user-friendly tabular format with intuitive filtering options.
With the implementation of the GWTG database and UI application, we addressed data management and accessibility concerns despite its growing scale. We have launched tools to provide streamlined access and accessibility of GWTG registry data to all researchers, regardless of familiarity or experience in coding.
美国心脏协会的 Get With The Guidelines（GWTG）已经成为推进中风、心力衰竭、冠状动脉疾病、心房颤动和复苏重点领域住院护理标准和实践的重要资源。GWTG 注册数据也为在生物医学研究中二次利用真实世界临床数据创造了新的机会。我们的目标是实施一个具有集成用户界面（UI）的可扩展数据库，以改善 GWTG 数据管理和可访问性。
注册数据的整理首先要经过一个用 Python 编写的数据处理和质量控制管道。该管道包括数据清理和记录排除、变量推导和单位统一、有限数据集准备以及注册数据的文档生成。数据库使用 PostgreSQL 构建，数据库和 UI 之间的集成使用 Python 中的 Django Web 框架构建。较小的数据子集使用 SQLite 数据库文件创建，用于分发目的。本文提供了这些工具的使用案例。
我们为美国心脏协会 GWTG 注册数据实施了一个自动化数据整理管道、集中式数据库和 UI 应用程序。该数据库和 UI 可通过精准医学平台工作区访问。截至 2024 年 3 月，该数据库包含超过 1320 万条经过清理的 GWTG 患者记录。SQLite 子集通过使用结构化查询语言优化数据提取和操作，使研究人员受益。UI 通过使用直观的筛选选项以用户友好的表格格式呈现数据，提高了非技术研究人员的可访问性。
通过实施 GWTG 数据库和 UI 应用程序，我们解决了数据管理和可访问性问题，尽管其规模不断扩大。我们已经推出了工具，为所有研究人员提供了简化的 GWTG 注册数据访问和可访问性，无论他们是否熟悉或有编码经验。
