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58 Utilizing VA Data to Define Long COVID and Identify Patients at Risk

Published online by Cambridge University Press:  24 April 2023

Peter L. Elkin
Affiliation:
Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo Department of Veterans Affairs, VA Western New York Healthcare System and VA Research Service Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo Faculty of Engineering, University of Southern Denmark
Skyler Resendez
Affiliation:
Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo Department of Veterans Affairs, VA Western New York Healthcare System and VA Research Service
H. Sebastian Ruiz
Affiliation:
Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo
Wilmon McCray
Affiliation:
Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo Department of Veterans Affairs, VA Western New York Healthcare System and VA Research Service
Steven H. Brown
Affiliation:
Office of Health Informatics, Department of Veterans Affairs
Jonathan Nebeker
Affiliation:
Office of Health Informatics, Department of Veterans Affairs
Diane Montella
Affiliation:
Office of Health Informatics, Department of Veterans Affairs
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Abstract

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OBJECTIVES/GOALS: To determine the signs, symptoms, and diagnoses that are significantly upregulated in cases of long COVID while identifying risk factors and demographics that increase one’s likelihood of developing long COVID. METHODS/STUDY POPULATION: This is a retrospective, big data science study. Data from Veterans Affairs (VA) medical centers across the United States between the start of 2020 and the end of 2022 were utilized. Our cohort consists of 316,782 individuals with positive COVID-19 tests recorded in the VA EHR with a history of ICD10-CM diagnosis codes in the record for case-control comparison. We looked at all new diagnoses that were not present in the six months before COVID diagnosis but were present in the time period from one month after COVID through seven months after. We determined which were significantly enriched and calculated odds ratios for each, organized by long COVID subtypes by medical specialty / affected organ system. Demographic analyses were also performed for long COVID patients and patients without any new long COVID ICD10-CM codes. RESULTS/ANTICIPATED RESULTS: This profile shows disorders that are highly upregulated in the post-COVID population and provides strong evidence for a broad definition of long COVID. By breaking this into subtypes by medical specialty, we define cardiac long COVID, neurological long COVID, pulmonary long COVID, and eight others. The long COVID cohort was older with more comorbidities than their non-long COVID counterparts. We also noted any differences regarding sex, race, ethnicity, severity of acute COVID-19 symptoms, vaccination status, as well as some analysis regarding medications taken. DISCUSSION/SIGNIFICANCE: This profile can be utilized to decisively define long COVID as a clinical diagnosis and will lead to consistence in future research. Elucidating an actionable model for long COVID will help clinicians identify those in their care that may be experiencing long COVID, allowing them to be admitted into more intensive monitoring and treatment programs.

Type
Biostatistics, Epidemiology, and Research Design
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2023. The Association for Clinical and Translational Science