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Review

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

by
Rayanne A. Luke
1,
George Shaw, Jr.
2,
Geetha Saarunya
3 and
Abolfazl Mollalo
4,*
1
Department of Mathematical Sciences, George Mason University, Fairfax, VA 22030, USA
2
Department of Public Health Science, School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
3
Department of Surgery, University of Minnesota, Twin Cities, MN 55455, USA
4
Biomedical Informatics Center, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
*
Author to whom correspondence should be addressed.
Informatics 2024, 11(2), 41; https://doi.org/10.3390/informatics11020041
Submission received: 6 February 2024 / Revised: 17 May 2024 / Accepted: 10 June 2024 / Published: 14 June 2024

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.
Keywords: electronic health records; long COVID; phenotypes; post-acute COVID-19 electronic health records; long COVID; phenotypes; post-acute COVID-19

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MDPI and ACS Style

Luke, R.A.; Shaw, G., Jr.; Saarunya, G.; Mollalo, A. Identifying Long COVID Definitions, Predictors, and Risk Factors in the United States: A Scoping Review of Data Sources Utilizing Electronic Health Records. Informatics 2024, 11, 41. https://doi.org/10.3390/informatics11020041

AMA Style

Luke RA, Shaw G Jr., Saarunya G, Mollalo A. Identifying Long COVID Definitions, Predictors, and Risk Factors in the United States: A Scoping Review of Data Sources Utilizing Electronic Health Records. Informatics. 2024; 11(2):41. https://doi.org/10.3390/informatics11020041

Chicago/Turabian Style

Luke, Rayanne A., George Shaw, Jr., Geetha Saarunya, and Abolfazl Mollalo. 2024. "Identifying Long COVID Definitions, Predictors, and Risk Factors in the United States: A Scoping Review of Data Sources Utilizing Electronic Health Records" Informatics 11, no. 2: 41. https://doi.org/10.3390/informatics11020041

APA Style

Luke, R. A., Shaw, G., Jr., Saarunya, G., & Mollalo, A. (2024). Identifying Long COVID Definitions, Predictors, and Risk Factors in the United States: A Scoping Review of Data Sources Utilizing Electronic Health Records. Informatics, 11(2), 41. https://doi.org/10.3390/informatics11020041

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