Identifying Long COVID Definitions, Predictors, and Risk Factors in the United States: A Scoping Review of Data Sources Utilizing Electronic Health Records

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Informatics Pub Date : 2024-06-14 DOI:10.3390/informatics11020041
Rayanne A. Luke, George Shaw, G. Saarunya, Abolfazl Mollalo
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Abstract

This scoping review explores the potential of electronic health records (EHR)-based studies to characterize long COVID. We screened all peer-reviewed publications in the English language from PubMed/MEDLINE, Scopus, and Web of Science databases until 14 September 2023, to identify the studies that defined or characterized long COVID based on data sources that utilized EHR in the United States, regardless of study design. We identified only 17 articles meeting the inclusion criteria. Respiratory conditions were consistently significant in all studies, followed by poor well-being features (n = 14, 82%) and cardiovascular conditions (n = 12, 71%). Some articles (n = 7, 41%) used a long COVID-specific marker to define the study population, relying mainly on ICD-10 codes and clinical visits for post-COVID-19 conditions. Among studies exploring plausible long COVID (n = 10, 59%), the most common methods were RT-PCR and antigen tests. The time delay for EHR data extraction post-test varied, ranging from four weeks to more than three months; however, most studies considering plausible long COVID used a waiting period of 28 to 31 days. Our findings suggest a limited utilization of EHR-derived data sources in defining long COVID, with only 59% of these studies incorporating a validation step.
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确定美国的长期 COVID 定义、预测因素和风险因素:利用电子健康记录的数据源范围审查
本范围界定综述探讨了基于电子健康记录 (EHR) 的研究在描述长 COVID 特征方面的潜力。我们从 PubMed/MEDLINE、Scopus 和 Web of Science 数据库中筛选了截至 2023 年 9 月 14 日的所有同行评审的英文出版物,以确定基于美国使用电子病历的数据源定义或描述长 COVID 的研究,无论研究设计如何。我们只发现了 17 篇符合纳入标准的文章。在所有研究中,呼吸系统状况一直都很重要,其次是健康状况差(14 篇,82%)和心血管状况(12 篇,71%)。一些文章(n = 7,41%)使用了长 COVID 特异性标记来定义研究人群,主要依赖于 ICD-10 编码和针对后 COVID-19 病症的临床访问。在探讨可信长 COVID 的研究中(n = 10,59%),最常见的方法是 RT-PCR 和抗原检测。测试后提取电子病历数据的延迟时间各不相同,从四周到三个多月不等;然而,大多数考虑可信的长 COVID 的研究都使用了 28-31 天的等待时间。我们的研究结果表明,在定义长 COVID 时对电子病历数据源的利用有限,其中只有 59% 的研究采用了验证步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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