Cargando…

Predictors of Acute Encephalopathy in Patients with COVID-19

Introduction: The majority of patients with severe COVID-19 suffer from delirium as the main sign of encephalopathy associated with this viral infection. The aim of this study was to identify early markers of the development of this condition. Materials: The prospective cohort-based study included p...

Descripción completa

Detalles Bibliográficos
Autores principales: Vinogradov, Oleg I., Ogarkova, Tatyana K., Shamtieva, Kamila V., Alexandrov, Pavel V., Mushba, Astanda V., Kanshina, Daria S., Yakovleva, Daria V., Surma, Maria A., Nikolaev, Ilia S., Gorst, Nadezhda Kh.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8584437/
https://www.ncbi.nlm.nih.gov/pubmed/34768339
http://dx.doi.org/10.3390/jcm10214821
_version_ 1784597449057239040
author Vinogradov, Oleg I.
Ogarkova, Tatyana K.
Shamtieva, Kamila V.
Alexandrov, Pavel V.
Mushba, Astanda V.
Kanshina, Daria S.
Yakovleva, Daria V.
Surma, Maria A.
Nikolaev, Ilia S.
Gorst, Nadezhda Kh.
author_facet Vinogradov, Oleg I.
Ogarkova, Tatyana K.
Shamtieva, Kamila V.
Alexandrov, Pavel V.
Mushba, Astanda V.
Kanshina, Daria S.
Yakovleva, Daria V.
Surma, Maria A.
Nikolaev, Ilia S.
Gorst, Nadezhda Kh.
author_sort Vinogradov, Oleg I.
collection PubMed
description Introduction: The majority of patients with severe COVID-19 suffer from delirium as the main sign of encephalopathy associated with this viral infection. The aim of this study was to identify early markers of the development of this condition. Materials: The prospective cohort-based study included patients with community-acquired pneumonia and confirmed pulmonary tissue infiltration based on CT data, with a lesion consisting of at least 25% of one lung. The main group included patients who have developed acute encephalopathy (10 patients, 3 (30%) women; average age, 47.9 ± 7.3 years). The control group included patients who at discharge did not have acute encephalopathy (20 patients, 11 (55%) women; average age, 51.0 ± 10.5 years). The study collected clinical examination data, comprehensive laboratory data, neurophysiological data, pulse oximetry and CT data to identify the predictors of acute encephalopathy (study ClinicalTrials.gov identifier NCT04405544). Results: Data analysis showed a significant relationship between encephalopathy with the degree of lung tissue damage, arterial hypertension, and type 2 diabetes mellitus, as well as with D-dimer, LDH, and lymphopenia. Conclusions: The development of encephalopathy is secondary to the severity of the patient’s condition since a more severe course of the coronavirus infection leads to hypoxic brain damage.
format Online
Article
Text
id pubmed-8584437
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-85844372021-11-12 Predictors of Acute Encephalopathy in Patients with COVID-19 Vinogradov, Oleg I. Ogarkova, Tatyana K. Shamtieva, Kamila V. Alexandrov, Pavel V. Mushba, Astanda V. Kanshina, Daria S. Yakovleva, Daria V. Surma, Maria A. Nikolaev, Ilia S. Gorst, Nadezhda Kh. J Clin Med Article Introduction: The majority of patients with severe COVID-19 suffer from delirium as the main sign of encephalopathy associated with this viral infection. The aim of this study was to identify early markers of the development of this condition. Materials: The prospective cohort-based study included patients with community-acquired pneumonia and confirmed pulmonary tissue infiltration based on CT data, with a lesion consisting of at least 25% of one lung. The main group included patients who have developed acute encephalopathy (10 patients, 3 (30%) women; average age, 47.9 ± 7.3 years). The control group included patients who at discharge did not have acute encephalopathy (20 patients, 11 (55%) women; average age, 51.0 ± 10.5 years). The study collected clinical examination data, comprehensive laboratory data, neurophysiological data, pulse oximetry and CT data to identify the predictors of acute encephalopathy (study ClinicalTrials.gov identifier NCT04405544). Results: Data analysis showed a significant relationship between encephalopathy with the degree of lung tissue damage, arterial hypertension, and type 2 diabetes mellitus, as well as with D-dimer, LDH, and lymphopenia. Conclusions: The development of encephalopathy is secondary to the severity of the patient’s condition since a more severe course of the coronavirus infection leads to hypoxic brain damage. MDPI 2021-10-20 /pmc/articles/PMC8584437/ /pubmed/34768339 http://dx.doi.org/10.3390/jcm10214821 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vinogradov, Oleg I.
Ogarkova, Tatyana K.
Shamtieva, Kamila V.
Alexandrov, Pavel V.
Mushba, Astanda V.
Kanshina, Daria S.
Yakovleva, Daria V.
Surma, Maria A.
Nikolaev, Ilia S.
Gorst, Nadezhda Kh.
Predictors of Acute Encephalopathy in Patients with COVID-19
title Predictors of Acute Encephalopathy in Patients with COVID-19
title_full Predictors of Acute Encephalopathy in Patients with COVID-19
title_fullStr Predictors of Acute Encephalopathy in Patients with COVID-19
title_full_unstemmed Predictors of Acute Encephalopathy in Patients with COVID-19
title_short Predictors of Acute Encephalopathy in Patients with COVID-19
title_sort predictors of acute encephalopathy in patients with covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8584437/
https://www.ncbi.nlm.nih.gov/pubmed/34768339
http://dx.doi.org/10.3390/jcm10214821
work_keys_str_mv AT vinogradovolegi predictorsofacuteencephalopathyinpatientswithcovid19
AT ogarkovatatyanak predictorsofacuteencephalopathyinpatientswithcovid19
AT shamtievakamilav predictorsofacuteencephalopathyinpatientswithcovid19
AT alexandrovpavelv predictorsofacuteencephalopathyinpatientswithcovid19
AT mushbaastandav predictorsofacuteencephalopathyinpatientswithcovid19
AT kanshinadarias predictorsofacuteencephalopathyinpatientswithcovid19
AT yakovlevadariav predictorsofacuteencephalopathyinpatientswithcovid19
AT surmamariaa predictorsofacuteencephalopathyinpatientswithcovid19
AT nikolaevilias predictorsofacuteencephalopathyinpatientswithcovid19
AT gorstnadezhdakh predictorsofacuteencephalopathyinpatientswithcovid19