Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

High neutralizing antibody titer in intensive care unit patients with COVID-19.

ABSTRACT: Coronavirus disease 2019 (COVID-19) has a wide spectrum of disease severity from mild upper respiratory symptoms to respiratory failure. The role of neutralizing antibody (NAb) response in disease progression remains elusive. This study determined the seroprevalence of 733 non-COVID-19 individuals from April 2018 to February 2020 in the Hong Kong Special Administrative Region and compared the neutralizing antibody (NAb) responses of eight COVID-19 patients admitted to the intensive care unit (ICU) with those of 42 patients not admitted to the ICU. We found that NAb against SARS-CoV-2 was not detectable in any of the anonymous serum specimens from the 733 non-COVID-19 individuals. The peak serum geometric mean NAb titer was significantly higher among the eight ICU patients than the 42 non-ICU patients (7280 [95% confidence interval (CI) 1468-36099]) vs (671 [95% CI, 368-1223]). Furthermore, NAb titer increased significantly at earlier infection stages among ICU patients than among non-ICU patients. The median number of days to reach the peak Nab titers after symptoms onset was shorter among the ICU patients (17.6) than that of the non-ICU patients (20.1). Multivariate analysis showed that oxygen requirement and fever during admission were the only clinical factors independently associated with higher NAb titers. Our data suggested that SARS-CoV-2 was unlikely to have silently spread before the COVID-19 emergence in Hong Kong. ICU patients had an accelerated and augmented NAb response compared to non-ICU patients, which was associated with disease severity. Further studies are required to understand the relationship between high NAb response and disease severity.

SUBMITTER: Liu L

PROVIDER: S-EPMC7473321 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Cardiometabolic MicroRNAs are Associated with severeerity and 28-Day Intensive Care Unit Mortality in COVID-19 Patients

Project description:Coronavirus disease 2019 (COVID-19) can lead to multiorgan damage and fatal outcomes. MicroRNAs (miRNAs) are detectable in blood, reflecting cell activation and tissue injury. We performed small RNA-Seq in healthy controls (N=11), non-severe (N=18) and severe (N=16) COVID-19 patients

2021-11-17 | GSE176498 | GEO

intensive care unit

Project description:intensive care unit Raw sequence reads

| PRJNA561526 | ENA

A proteomic survival predictor for COVID-19 patients in intensive care

Project description:Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Instead, using plasma proteomes quantifying 302 plasma protein groups at 387 timepoints in 57 critically ill patients on invasive mechanical ventilation, we found 14 proteins that showed trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81, n=49). We tested the established predictor on an independent validation cohort (AUROC of 1.0, n=24). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.

2022-03-03 | PXD029009 | Pride

Intensive care unit acquired muscle weakness in COVID-19 patients.

Project description: Not available

| S-EPMC7520507 | biostudies-literature

Monocyte CD169 expression in COVID-19 patients upon intensive care unit admission.

Project description:During the second surge of COVID-19 in France (fall 2020), we assessed the expression of monocyte CD169 (i.e., Siglec-1, one of the numerous IFN-stimulated genes) upon admission to intensive care units of 45 patients with RT-PCR-confirmed SARS-CoV2 pulmonary infection. Overall, CD169 expression was strongly induced on circulating monocytes of COVID-19 patients compared with healthy donors and patients with bacterial sepsis. Beyond its contribution at the emergency department, CD169 testing may be also helpful for patients' triage at the ICU to rapidly reinforce suspicion of COVID-19 etiology in patients with acute respiratory failure awaiting for PCR results for definitive diagnosis.

| S-EPMC8014094 | biostudies-literature

Neutralizing type-I interferon autoantibodies are associated with delayed viral clearance and intensive care unit admission in patients with COVID-19.

Project description:Type-I interferons (IFNs) mediate antiviral activity and have emerged as important immune mediators during coronavirus disease 19 (COVID-19). Several lines of evidence suggest that impaired type-I IFN signaling may predispose to severe COVID-19. However, the pathophysiologic mechanisms that contribute to illness severity remain unclear. In this study, our goal was to gain insight into how type-I IFNs influence outcomes in patients with COVID-19. To achieve this goal, we compared clinical outcomes between 26 patients with neutralizing type-I IFN autoantibodies (AAbs) and 192 patients without AAbs who were hospitalized for COVID-19 at three Italian hospitals. The presence of circulating AAbs to type-I IFNs was associated with an increased risk of admission to the intensive care unit and a delayed time to viral clearance. However, survival was not adversely affected by the presence of type-I IFN AAbs. Our findings provide further support for the role of type-I IFN AAbs in impairing host antiviral defense and promoting the development of critical COVID-19 pneumonia in severe acute respiratory syndrome coronavirus 2-infected individuals.

| S-EPMC8444766 | biostudies-literature

Visceral adipose tissue area predicts intensive care unit admission in COVID-19 patients.

Project description:We retrospectively investigated, in 62 consecutive hospitalised COVID-19 patients (aged 70 ± 14 years, 40 males), the prognostic value of CT-derived subcutaneous adipose tissue and visceral adipose tissue (VAT) metrics, testing them in four predictive models for admission to intensive care unit (ICU), with and without pre-existing comorbidities. Multivariate logistic regression identified VAT score as the best ICU admission predictor (odds ratios 4.307-12.842). A non-relevant contribution of comorbidities at receiver operating characteristic analysis (area under the curve 0.821 for the CT-based model, 0.834 for the one including comorbidities) highlights the potential one-stop-shop prognostic role of CT-derived lung and adipose tissue metrics.

| S-EPMC7836243 | biostudies-literature

Early predictors of intensive care unit admission among COVID-19 patients in Qatar.

Project description:BackgroundCOVID-19 is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission among patients with COVID-19.MethodsThis was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29-May 29, 2020. For each case enrolled, one control was matched by age and gender.ResultsA total of 1,560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (adjusted odds ratio (aOR) = 1.64, 95% confidence interval (CI): 1.16-2.32, p = 0.005), diabetes (aOR = 1.52, 95% CI: 1.08-2.13, p = 0.016), obesity (aOR = 1.46, 95% CI: 1.03-2.08, p = 0.034), lymphopenia (aOR = 2.69, 95% CI: 1.80-4.02, p < 0.001), high AST (aOR = 2.59, 95% CI: 1.53-4.36, p < 0.001), high ferritin (aOR = 1.96, 95% CI: 1.40-2.74, p < 0.001), high CRP (aOR = 4.09, 95% CI: 2.81-5.96, p < 0.001), and dyspnea (aOR = 2.50, 95% CI: 1.77-3.54, p < 0.001).ConclusionHaving cardiovascular disease, diabetes, obesity, lymphopenia, dyspnea, and increased AST, ferritin, and CRP were independent predictors for ICU admission in patients with COVID-19.

| S-EPMC10987715 | biostudies-literature

[Nationwide exposure model for COVID-19 intensive care unit admission].

Project description:BackgroundForecasting models for intensive care occupancy of coronavirus disease 2019 (COVID-19) patients are important in the current pandemic for strategic planning of patient allocation and avoidance of regional overcrowding. They are often trained entirely on retrospective infection and occupancy data, which can cause forecast uncertainty to grow exponentially with the forecast horizon.MethodologyWe propose an alternative modeling approach in which the model is created largely independent of the occupancy data being simulated. The distribution of bed occupancies for patient cohorts is calculated directly from occupancy data from "sentinel clinics". By coupling with infection scenarios, the prediction error is constrained by the error of the infection dynamics scenarios. The model allows systematic simulation of arbitrary infection scenarios, calculation of bed occupancy corridors, and sensitivity analyses with respect to protective measures.ResultsThe model was based on hospital data and by adjusting only two parameters of data in the Aachen city region and Germany as a whole. Using the example of the simulation of the respective bed occupancy rates for Germany as a whole, the loading model for the calculation of occupancy corridors is demonstrated. The occupancy corridors form barriers for bed occupancy in the event that infection rates do not exceed specific thresholds. In addition, lockdown scenarios are simulated based on retrospective events.DiscussionOur model demonstrates that a significant reduction in forecast uncertainty in occupancy forecasts is possible by selectively combining data from different sources. It allows arbitrary combination with infection dynamics models and scenarios, and thus can be used both for load forecasting and for sensitivity analyses for expected novel spreading and lockdown scenarios.

| S-EPMC7856858 | biostudies-literature

Nosocomial infections among COVID-19 patients: an analysis of intensive care unit surveillance data.

Project description:Surveillance of nosocomial infections, like catheter-associated urinary tract infection (CAUTI), central line-associated bloodstream infection, possible ventilator-associated pneumonia and secondary bloodstream infections were observed to study the impact of COVID-19 outbreak in ICUs from Tan Tock Seng Hospital and National Centre for Infectious Diseases, Singapore between February and June 2020. Higher nosocomial infection rates were observed in COVID-19 patients, although it was not statistically significant. Moreover, COVID-19 patients seem to be more predisposed to CAUTI despite a higher proportion of non-COVID-19 patients having urinary catheters. Thus, continued vigilance to ensure adherence to IPC measures is needed.

| S-EPMC8358905 | 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