Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Stroke as a complication and prognostic factor of COVID-19.

ABSTRACT: INTRODUCTION:Contradictory data have been reported on the incidence of stroke in patients with COVID-19 and the risk of SARS-CoV-2 infection among patients with history of stroke. METHODS:This study systematically reviews case series reporting stroke as a complication of COVID-19, and analyses the prognosis of patients with COVID-19 and history of stroke. The pathophysiological mechanisms of stroke in patients with COVID-19 are also reviewed. CONCLUSIONS:History of stroke increases the risk of death due to COVID-19 by 3 times. Stroke currently seems not to be one of the main complications of COVID-19.

SUBMITTER: Trejo-Gabriel-Galan JM

PROVIDER: S-EPMC7200328 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Stroke as a complication and prognostic factor of COVID-19.

Trejo-Gabriel-Galán J M JM

Neurologia 20200506 5

<h4>Introduction</h4>Contradictory data have been reported on the incidence of stroke in patients with COVID-19 and the risk of SARS-CoV-2 infection among patients with history of stroke.<h4>Methods</h4>This study systematically reviews case series reporting stroke as a complication of COVID-19, and analyses the prognosis of patients with COVID-19 and history of stroke. The pathophysiological mechanisms of stroke in patients with COVID-19 are also reviewed.<h4>Conclusions</h4>History of stroke i ...[more]

PMID: 32493597

Similar Datasets

Stroke as a complication and prognostic factor of COVID-19?.

Project description:IntroductionContradictory data have been reported on the incidence of stroke in patients with COVID-19 and the risk of SARS-CoV-2 infection among patients with history of stroke.MethodsThis study systematically reviews case series reporting stroke as a complication of COVID-19, and analyses the prognosis of patients with COVID-19 and history of stroke. The pathophysiological mechanisms of stroke in patients with COVID-19 are also reviewed.ConclusionsHistory of stroke increases the risk of death due to COVID-19 by 3 times. Stroke currently seems not to be one of the main complications of COVID-19.

| S-EPMC7256525 | biostudies-literature

Stroke as a Potential Complication of COVID-19-Associated Coagulopathy: A Narrative and Systematic Review of the Literature.

Project description:Coronavirus disease 2019 (COVID-19) is the most overwhelming medical threat of the past few decades. The infection, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can cause serious illness leading to respiratory insufficiency, and, in severely ill patients, it can progress to multiple organ failure leading to death. It has been noted from the earliest reports that the disease influences the hemostasis system and a hallmark of severe infection is elevated D-dimer levels. The profound coagulation changes in COVID-19 seem to be linked to inflammation-related events and severe endothelial cell injury. Besides the high incidence of venous thromboembolic events in SARS-CoV-2 infections, arterial events, including cerebrovascular events, were found to be associated with the disease. In this review, we aimed to summarize the available literature on COVID-19-associated coagulopathy and thrombosis. Furthermore, we performed a systematic search of the literature to identify the characteristics of stroke in COVID-19. Our findings showed that acute ischemic stroke (AIS) is the most frequent type of stroke occurring in infected patients. In most cases, stroke was severe (median NIHSS:16) and most of the patients had one or more vascular risk factors. Laboratory findings in AIS patients were consistent with COVID-19-associated coagulopathy, and elevated D-dimer levels were the most common finding. The outcome was unfavorable in most cases, as a large proportion of the reported patients died or remained bedridden. Limited data are available as yet on outcomes after acute vascular interventions in COVID-19 patients. In the future, well-designed studies are needed to better understand the risk of stroke in COVID-19, to optimize treatment, and to improve stroke care.

| S-EPMC7599722 | biostudies-literature

Stroke as a Neurological Complication of COVID-19: A Systematic Review and Meta-Analysis of Incidence, Outcomes and Predictors.

Project description:IntroductionCOVID-19 is a multi-system infection which predominantly affects the respiratory system, but also causes systemic inflammation, endothelialitis and thrombosis. The consequences of this include renal dysfunction, hepatitis and stroke. In this systematic review, we aimed to evaluate the epidemiology, clinical course, and outcomes of patients who suffer from stroke as a complication of COVID-19.MethodsWe conducted a systematic review of all studies published between November 1, 2019 and July 8, 2020 which reported on patients who suffered from stroke as a complication of COVID-19.Results326 studies were screened, and 30 studies reporting findings from 55,176 patients including 899 with stroke were included. The average age of patients who suffered from stroke as a complication of COVID-19 was 65.5 (Range: 40.4-76.4 years). The average incidence of stroke as a complication of COVID-19 was 1.74% (95% CI: 1.09% to 2.51%). The average mortality of stroke in COVID-19 patients was 31.76% (95% CI: 17.77% to 47.31%). These patients also had deranged clinical parameters including deranged coagulation profiles, liver function tests, and full blood counts.ConclusionAlthough stroke is an uncommon complication of COVID-19, when present, it often results in significant morbidity and mortality. In COVID-19 patients, stroke was associated with older age, comorbidities, and severe illness.

| S-EPMC7834121 | biostudies-literature

Rhabdomyolysis as Potential Late Complication Associated with COVID-19.

Project description:We describe a patient in Wuhan, China, with severe acute respiratory syndrome coronavirus 2 infection who had progressive pulmonary lesions and rhabdomyolysis with manifestations of lower limb pain and fatigue. Rapid clinical recognition of rhabdomyolysis symptoms in patients with severe acute respiratory syndrome coronavirus 2 infection can be lifesaving.

| S-EPMC7323559 | biostudies-literature

Cohort survey of healthcare workers with COVID-19 infection

Project description: Not available

| S-BSST563 | biostudies-other

A time-resolved proteomic and prognostic map of COVID-19

Project description:We performed quantitative proteomic profiling of 786 plasma samples from COVID-19 inpatients, treated at two different hospitals (Charité – Universitätsmedizin Berlin and University Hospital of Innsbruck). Sampling was performed at multiple time points throughout the course of the disease, to create a time-resolved map of COVID-19 progression. Full DIA-NN analysis reports are provided, as well as raw files for the QC runs.

2021-07-19 | PXD025752 | Pride

COVID-19-Related Stroke.

Project description:The COVID-19 pandemic is associated with neurological symptoms and complications including stroke. There is hypercoagulability associated with COVID-19 that is likely a "sepsis-induced coagulopathy" and may predispose to stroke. The SARS-CoV-2 virus binds to angiotensin-converting enzyme 2 (ACE2) present on brain endothelial and smooth muscle cells. ACE2 is a key part of the renin angiotensin system (RAS) and a counterbalance to angiotensin-converting enzyme 1 (ACE1) and angiotensin II. Angiotensin II is proinflammatory, is vasoconstrictive, and promotes organ damage. Depletion of ACE2 by SARS-CoV-2 may tip the balance in favor of the "harmful" ACE1/angiotensin II axis and promote tissue injury including stroke. There is a rationale to continue to treat with tissue plasminogen activator for COVID-19-related stroke and low molecular weight heparinoids may reduce thrombosis and mortality in sepsis-induced coagulopathy.

| S-EPMC7202903 | biostudies-literature

Covid-19

Project description:SARS-CoV2 sequences from Finland

| PRJEB42396 | ENA

Prognostic accuracy of Mass Spectrometric Analysis of Plasma in COVID-19

Project description:SARS-CoV-2 infection poses a worldwide public health problem affecting millions of people worldwide. There is a critical need for improvements in the noninvasive prognosis of COVID-19. We hypothesized that matrix-assisted laser desorption ionization mass spectrometry (MALDI-TOF MS) analysis combined with molecular-weight directed bottom-up proteomic analysis of plasma proteins may predict high and low risk cases of COVID-19. Patients and Methods: We used MALDI MS to analyze plasma small proteins and peptides isolated using C18 micro-columns from a cohort containing a total of 117 cases of high and low risk cases split into training (n = 88) and validation sets (n= 29). The plasma protein/peptide fingerprint obtained was used to train the algorithm before validation using a blinded test cohort. Several sample preparation, MS and data analysis parameters were optimized to achieve an overall accuracy of 85%, a sensitivity of 90%, and a specificity of 81% in the training set. In the blinded test set, this signature reached an overall accuracy of 93.1%, a sensitivity of 87.5%, and a specificity of 100%. From this signature, we identified two distinct regions corresponding to the single and doubly protonated proteins in the MALDI-TOF profile belonging to the same proteoforms. A combination of 1D SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins. We found a plasma proteomic profile that discriminates against patients with high and low risk COVID-19. Proteomic analysis of C18-fractionated plasma may have a role in the noninvasive prognosis of COVID-19. Further validation will be important to consolidate its clinical utility.

2021-06-22 | PXD021581 | Pride

Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles.

Project description:PurposeHeterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.MethodsThis is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes.ResultsThe database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III.ConclusionWe identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.

| S-EPMC8011766 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data