58 Utilizing VA Data to Define Long COVID and Identify Patients at Risk
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 stu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2023-04-01
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| Series: | Journal of Clinical and Translational Science |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2059866123001462/type/journal_article |
| _version_ | 1797840477721985024 |
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| author | Peter L. Elkin Skyler Resendez H. Sebastian Ruiz Wilmon McCray Steven H. Brown Jonathan Nebeker Diane Montella |
| author_facet | Peter L. Elkin Skyler Resendez H. Sebastian Ruiz Wilmon McCray Steven H. Brown Jonathan Nebeker Diane Montella |
| author_sort | Peter L. Elkin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T16:15:58Z |
| format | Article |
| id | doaj.art-60e156375104481a9b9ccf20caa9d8c1 |
| institution | Directory Open Access Journal |
| issn | 2059-8661 |
| language | English |
| last_indexed | 2024-04-09T16:15:58Z |
| publishDate | 2023-04-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Clinical and Translational Science |
| spelling | doaj.art-60e156375104481a9b9ccf20caa9d8c12023-04-24T05:55:54ZengCambridge University PressJournal of Clinical and Translational Science2059-86612023-04-017151610.1017/cts.2023.14658 Utilizing VA Data to Define Long COVID and Identify Patients at RiskPeter L. Elkin0Skyler Resendez1H. Sebastian Ruiz2Wilmon McCray3Steven H. Brown4Jonathan Nebeker5Diane Montella6Department 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 DenmarkDepartment 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 ServiceDepartment of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at BuffaloDepartment 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 ServiceOffice of Health Informatics, Department of Veterans AffairsOffice of Health Informatics, Department of Veterans AffairsOffice of Health Informatics, Department of Veterans AffairsOBJECTIVES/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.https://www.cambridge.org/core/product/identifier/S2059866123001462/type/journal_article |
| spellingShingle | Peter L. Elkin Skyler Resendez H. Sebastian Ruiz Wilmon McCray Steven H. Brown Jonathan Nebeker Diane Montella 58 Utilizing VA Data to Define Long COVID and Identify Patients at Risk Journal of Clinical and Translational Science |
| title | 58 Utilizing VA Data to Define Long COVID and Identify Patients at Risk |
| title_full | 58 Utilizing VA Data to Define Long COVID and Identify Patients at Risk |
| title_fullStr | 58 Utilizing VA Data to Define Long COVID and Identify Patients at Risk |
| title_full_unstemmed | 58 Utilizing VA Data to Define Long COVID and Identify Patients at Risk |
| title_short | 58 Utilizing VA Data to Define Long COVID and Identify Patients at Risk |
| title_sort | 58 utilizing va data to define long covid and identify patients at risk |
| url | https://www.cambridge.org/core/product/identifier/S2059866123001462/type/journal_article |
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