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Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series

OBJECTIVES: To determine if early CNS symptoms are associated with severe coronavirus disease 2019. DESIGN: A retrospective, observational case series study design. SETTING: Electronic health records were reviewed for patients from five healthcare systems across the state of Florida, United States....

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Autores principales: Marra, David E., Busl, Katharina M., Robinson, Christopher P., Bruzzone, Maria J., Miller, Amber H., Chen, Zhaoyi, Guo, Yi, Lyu, Tianchen, Bian, Jiang, Smith, Glenn E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202548/
https://www.ncbi.nlm.nih.gov/pubmed/34136827
http://dx.doi.org/10.1097/CCE.0000000000000456
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author Marra, David E.
Busl, Katharina M.
Robinson, Christopher P.
Bruzzone, Maria J.
Miller, Amber H.
Chen, Zhaoyi
Guo, Yi
Lyu, Tianchen
Bian, Jiang
Smith, Glenn E.
author_facet Marra, David E.
Busl, Katharina M.
Robinson, Christopher P.
Bruzzone, Maria J.
Miller, Amber H.
Chen, Zhaoyi
Guo, Yi
Lyu, Tianchen
Bian, Jiang
Smith, Glenn E.
author_sort Marra, David E.
collection PubMed
description OBJECTIVES: To determine if early CNS symptoms are associated with severe coronavirus disease 2019. DESIGN: A retrospective, observational case series study design. SETTING: Electronic health records were reviewed for patients from five healthcare systems across the state of Florida, United States. PATIENTS: A clinical sample (n = 36,615) of patients with confirmed diagnosis of coronavirus disease 2019 were included. Twelve percent (n = 4,417) of the sample developed severe coronavirus disease 2019, defined as requiring critical care, mechanical ventilation, or diagnosis of acute respiratory distress syndrome, sepsis, or severe inflammatory response syndrome. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: We reviewed the electronic health record for diagnosis of early CNS symptoms (encephalopathy, headache, ageusia, anosmia, dizziness, acute cerebrovascular disease) between 14 days before the diagnosis of coronavirus disease 2019 and 8 days after the diagnosis of coronavirus disease 2019, or before the date of severe coronavirus disease 2019 diagnosis, whichever came first. Hierarchal logistic regression models were used to examine the odds of developing severe coronavirus disease 2019 based on diagnosis of early CNS symptoms. Severe coronavirus disease 2019 patients were significantly more likely to have early CNS symptoms (32.8%) compared with nonsevere patients (6.11%; χ(2)[1] = 3,266.08, p < 0.0001, φ = 0.29). After adjusting for demographic variables and pertinent comorbidities, early CNS symptoms were significantly associated with severe coronavirus disease 2019 (odds ratio = 3.21). Diagnosis of encephalopathy (odds ratio = 14.38) was associated with greater odds of severe coronavirus disease 2019; whereas diagnosis of anosmia (odds ratio = 0.45), ageusia (odds ratio = 0.46), and headache (odds ratio = 0.63) were associated with reduced odds of severe coronavirus disease 2019. CONCLUSIONS: Early CNS symptoms, and specifically encephalopathy, are differentially associated with risk of severe coronavirus disease 2019 and may serve as an early marker for differences in clinical disease course. Therapies for early coronavirus disease 2019 are scarce, and further identification of subgroups at risk may help to advance understanding of the severity trajectories and enable focused treatment.
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spelling pubmed-82025482021-06-15 Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series Marra, David E. Busl, Katharina M. Robinson, Christopher P. Bruzzone, Maria J. Miller, Amber H. Chen, Zhaoyi Guo, Yi Lyu, Tianchen Bian, Jiang Smith, Glenn E. Crit Care Explor Observational Study OBJECTIVES: To determine if early CNS symptoms are associated with severe coronavirus disease 2019. DESIGN: A retrospective, observational case series study design. SETTING: Electronic health records were reviewed for patients from five healthcare systems across the state of Florida, United States. PATIENTS: A clinical sample (n = 36,615) of patients with confirmed diagnosis of coronavirus disease 2019 were included. Twelve percent (n = 4,417) of the sample developed severe coronavirus disease 2019, defined as requiring critical care, mechanical ventilation, or diagnosis of acute respiratory distress syndrome, sepsis, or severe inflammatory response syndrome. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: We reviewed the electronic health record for diagnosis of early CNS symptoms (encephalopathy, headache, ageusia, anosmia, dizziness, acute cerebrovascular disease) between 14 days before the diagnosis of coronavirus disease 2019 and 8 days after the diagnosis of coronavirus disease 2019, or before the date of severe coronavirus disease 2019 diagnosis, whichever came first. Hierarchal logistic regression models were used to examine the odds of developing severe coronavirus disease 2019 based on diagnosis of early CNS symptoms. Severe coronavirus disease 2019 patients were significantly more likely to have early CNS symptoms (32.8%) compared with nonsevere patients (6.11%; χ(2)[1] = 3,266.08, p < 0.0001, φ = 0.29). After adjusting for demographic variables and pertinent comorbidities, early CNS symptoms were significantly associated with severe coronavirus disease 2019 (odds ratio = 3.21). Diagnosis of encephalopathy (odds ratio = 14.38) was associated with greater odds of severe coronavirus disease 2019; whereas diagnosis of anosmia (odds ratio = 0.45), ageusia (odds ratio = 0.46), and headache (odds ratio = 0.63) were associated with reduced odds of severe coronavirus disease 2019. CONCLUSIONS: Early CNS symptoms, and specifically encephalopathy, are differentially associated with risk of severe coronavirus disease 2019 and may serve as an early marker for differences in clinical disease course. Therapies for early coronavirus disease 2019 are scarce, and further identification of subgroups at risk may help to advance understanding of the severity trajectories and enable focused treatment. Lippincott Williams & Wilkins 2021-06-11 /pmc/articles/PMC8202548/ /pubmed/34136827 http://dx.doi.org/10.1097/CCE.0000000000000456 Text en Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Observational Study
Marra, David E.
Busl, Katharina M.
Robinson, Christopher P.
Bruzzone, Maria J.
Miller, Amber H.
Chen, Zhaoyi
Guo, Yi
Lyu, Tianchen
Bian, Jiang
Smith, Glenn E.
Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series
title Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series
title_full Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series
title_fullStr Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series
title_full_unstemmed Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series
title_short Examination of Early CNS Symptoms and Severe Coronavirus Disease 2019: A Multicenter Observational Case Series
title_sort examination of early cns symptoms and severe coronavirus disease 2019: a multicenter observational case series
topic Observational Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202548/
https://www.ncbi.nlm.nih.gov/pubmed/34136827
http://dx.doi.org/10.1097/CCE.0000000000000456
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