Sun Yujia, Scherbakov Dmitry, Fronk Gaylen, Ridings Leigh, Alekseyenko Alexander V, Lenert Leslie A
Res Sq. 2025 Aug 5:rs.3.rs-7272352. doi: 10.21203/rs.3.rs-7272352/v1.
Depression poses a significant global public health challenge, necessitating innovative research to understand its epidemiology and management. Electronic health record (EHR) research networks offer a powerful tool to study depression at scale, yet remain underutilized. This scoping review summarizes the extent of depression research ongoing in EHR networks. Following the Arksey and O'Malley framework and PRISMA guidelines, we searched PubMed, Scopus, EBSCOHost, and Google Scholar in September 2024, identifying 166 studies from 1211 records. Included studies used EHR networks like TriNetX, All of Us, and the Million Veteran Program (MVP) to investigate depression, defined broadly to include various depressive disorders. Covidence with custom large language model (LLM) plugin was used to aid screening and extraction processes. Depression research in EHR networks is limited, with TriNetX (36 studies) and All of Us (24 studies) the most utilized platforms. Populations studied were predominantly from the United States (125 studies), followed by Canada (5) and European countries (15 combined). Common predictors analyzed included age (58 studies), gender/sex (56 studies), and race/ethnicity (45 studies). EHR networks hold vast real-world data for advancing depression research, but underutilization highlights the need for better accessibility to enhance future studies.
抑郁症构成了一项重大的全球公共卫生挑战，因此需要开展创新性研究来了解其流行病学情况和管理方法。电子健康记录（EHR）研究网络为大规模研究抑郁症提供了一个强大的工具，但仍未得到充分利用。本范围综述总结了EHR网络中正在进行的抑郁症研究的程度。按照阿克斯和奥马利框架以及PRISMA指南，我们于2024年9月搜索了PubMed、Scopus、EBSCOHost和谷歌学术，从1211条记录中识别出166项研究。纳入的研究使用了TriNetX、“我们所有人”计划和百万退伍军人计划（MVP）等EHR网络来调查抑郁症，这里的抑郁症定义宽泛，包括各种抑郁障碍。使用带有定制大语言模型（LLM）插件的Covidence来辅助筛选和提取过程。EHR网络中的抑郁症研究有限，TriNetX（36项研究）和“我们所有人”计划（24项研究）是使用最多的平台。所研究的人群主要来自美国（125项研究），其次是加拿大（5项）和欧洲国家（共15项）。分析的常见预测因素包括年龄（58项研究）、性别（56项研究）和种族/民族（45项研究）。EHR网络拥有大量用于推进抑郁症研究的真实世界数据，但利用不足凸显了提高数据可及性以加强未来研究的必要性。
