Browse
Submit Data
Databases
API
Help

Dataset Information

22 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Early predictors of mortality for moderate to severely ill patients with Covid-19.

ABSTRACT:

Background

Determining the factors affecting the mortality and clinical conditions of the patients with Covid-19 are indispensable needs in developing patient treatment algorithms. We aimed to determine the parameters that can predict the mortality of moderate to severely ill patients with laboratory confirmed Covid-19.

Methods

Moderate to severely ill, Covid-19 patients older than 18 years were included. Mild Covid-19 patients and the ones with negative polymerase chain reaction test results were excluded from the study. The primary outcome of the study was 30-day mortality rate and we aimed to determine the factors affecting mortality in moderate to severely ill Covid-19 patients.

Results

168 patient results were analyzed. Median age of the patients was 59.5 (48.3 to 76) and 90 (53.6%) were male. According to multivariate regression analysis results, the presence of any comorbid disease (p = 0.027, HR = 26.11 (95%CI: 1.45 to 471.31)), elevated C-reactive protein levels (CRP) (p < 0.001, HR = 1.24 (95%CI: 1.11 to 1.38)) and presence of dyspnea (p = 0.026, HR = 4.26 ((95%CI: 1.19 to 15.28)) were found to significantly increase the mortality, while high pulse O 2 saturation level (p < 0.001, HR = 0.90 (95%CI: 0.82 to 0.99) was found to decrease. When receiver operating characteristic curve was created for laboratory tests, it was determined that white blood cell counts, neutrophil counts, CRP levels and neutrophil/lymphocyte ratio predicted mortality while Lymphocyte levels did not.

Conclusion

Dyspnea, the presence of any comorbid disease, elevated CRP levels, and low pulse O 2 saturation levels predict mortality in moderate to severely ill Covid-19 patients.

SUBMITTER: Aksel G

PROVIDER: S-EPMC7452846 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

A model to predict the risk of mortality in severely ill COVID-19 patients.

Project description:BackgroundTo investigate and select the useful prognostic parameters to develop and validate a model to predict the mortality risk for severely and critically ill patients with the coronavirus disease 2019 (COVID-19).MethodsWe established a retrospective cohort of patients with laboratory-confirmed COVID-19 (≥18 years old) from two tertiary hospitals: the People's Hospital of Wuhan University and Leishenshan Hospital between February 16, 2020, and April 14, 2020. The diagnosis of the cases was confirmed according to the WHO interim guidance. The data of consecutive severely and critically ill patients with COVID-19 admitted to these hospitals were analyzed. A total of 566 patients from the People's Hospital of Wuhan University were included in the training cohort and 436 patients from Leishenshan Hospital were included in the validation cohort. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the variables and build the mortality risk prediction model.ResultsThe prediction model was presented as a nomograph and developed based on identified predictors, including age, chronic lung disease, C-reactive protein (CRP), D-dimer levels, neutrophil-to-lymphocyte ratio (NLR), creatinine, and total bilirubin. In the training cohort, the model displayed good discrimination with an AUC of 0.912 [95% confidence interval (CI): 0.884-0.940] and good calibration (intercept = 0; slope = 1). In the validation cohort, the model had an AUC of 0.922 [95% confidence interval (CI): 0.891-0.953] and a good calibration (intercept = 0.056; slope = 1.161). The decision curve analysis (DCA) demonstrated that the nomogram was clinically useful.ConclusionA risk score for severely and critically ill COVID-19 patients' mortality was developed and externally validated. This model can help clinicians to identify individual patients at a high mortality risk.

| S-EPMC7983362 | biostudies-literature

Severely ill COVID-19 patients display impaired exhaustion features in SARS-CoV-2-reactive CD8+ T cells

Project description:The molecular properties of CD8+ T cells that respond to SARS-CoV-2 infection are not fully known. Here, we report on the single-cell transcriptomes of >80,000 virus-reactive CD8+ T cells, obtained using a modified Antigen-Reactive T cell Enrichment (ARTE) assay, from 39 COVID-19 patients and 10 healthy subjects. COVID-19 patients segregated into two groups based on whether the dominant CD8+ T cell response to SARS-CoV-2 was ‘exhausted’ or not. SARS-CoV-2-reactive cells in the exhausted subset were increased in frequency and displayed lesser cytotoxicity and inflammatory features in COVID-19 patients with mild compared to severe illness. In contrast, SARS-CoV-2-reactive cells in the dominant non-exhausted subset from patients with severe disease showed enrichment of transcripts linked to co-stimulation, pro-survival NF-κB signaling, and anti-apoptotic pathways, suggesting the generation of robust CD8+ T cell memory responses in patients with severe COVID-19 illness. CD8+ T cells reactive to influenza and respiratory syncytial virus from healthy subjects displayed polyfunctional features and enhanced glycolysis. Cells with such features were largely absent in SARS-CoV-2-reactive cells from both COVID-19 patients and healthy controls non-exposed to SARS-CoV-2. Overall, our single-cell analysis revealed substantial diversity in the nature of CD8+ T cells responding to SARS-CoV-2.

2020-07-22 | GSE153931 | GEO

Gene expression profile of peripheral blood mononuclear cells from mild and severe COVID-19 patients, severely ill COVID-19 patients treated with dexamethasone and healthy controls

Project description:The objective of this experiment was to compare the transcriptomic profile (NanoString platform) of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients with mild disease, and patients with severe COVID-19 with and without dexamethasone treatment, and healthy controls. We analyzed PBMCs from 4 mild COVID patients, 3 severe COVID patients,4 severe COVID patients treated with dexamethasone, and 5 healthy controls

2023-03-23 | GSE227341 | GEO

Transcriptomic clustering of critically-ill COVID-19 patients

Project description:This SuperSeries is composed of the SubSeries listed below.

2022-02-28 | GSE197259 | GEO

Cell type-specific immune dysregulation in severely ill COVID-19 patients.

Project description:Coronavirus disease 2019 (COVID-19) has quickly become the most serious pandemic since the 1918 flu pandemic. In extreme situations, patients develop a dysregulated inflammatory lung injury called acute respiratory distress syndrome (ARDS) that causes progressive respiratory failure requiring mechanical ventilatory support. Recent studies have demonstrated immunologic dysfunction in severely ill COVID-19 patients. To further delineate the dysregulated immune response driving more severe clinical course from SARS-CoV-2 infection, we used single-cell RNA sequencing (scRNAseq) to analyze the transcriptome of peripheral blood mononuclear cells (PBMC) from hospitalized COVID-19 patients having mild disease (n = 5), developing ARDS (n = 6), and recovering from ARDS (n = 6). Our data demonstrated an overwhelming inflammatory response with select immunodeficiencies within various immune populations in ARDS patients. Specifically, their monocytes had defects in antigen presentation and deficiencies in interferon responsiveness that contrasted the higher interferon signals in lymphocytes. Furthermore, cytotoxic activity was suppressed in both NK and CD8 lymphocytes whereas B cell activation was deficient, which is consistent with the delayed viral clearance in severely ill COVID-19 patients. Finally, we identified altered signaling pathways in the severe group that suggests immunosenescence and immunometabolic changes could be contributing to the dysfunctional immune response. Our study demonstrates that COVID-19 patients with ARDS have an immunologically distinct response when compared to those with a more innocuous disease course and show a state of immune imbalance in which deficiencies in both the innate and adaptive immune response may be contributing to a more severe disease course in COVID-19.

| S-EPMC7386732 | biostudies-literature

Cell-Type-Specific Immune Dysregulation in Severely Ill COVID-19 Patients.

Project description:Recent studies have demonstrated immunologic dysfunction in severely ill coronavirus disease 2019 (COVID-19) patients. We use single-cell RNA sequencing (scRNA-seq) to analyze the transcriptome of peripheral blood mononuclear cells (PBMCs) from healthy (n = 3) and COVID-19 patients with moderate disease (n = 5), acute respiratory distress syndrome (ARDS, n = 6), or recovering from ARDS (n = 6). Our data reveal transcriptomic profiles indicative of defective antigen presentation and interferon (IFN) responsiveness in monocytes from ARDS patients, which contrasts with higher responsiveness to IFN signaling in lymphocytes. Furthermore, genes involved in cytotoxic activity are suppressed in both natural killer (NK) and CD8 T lymphocytes, and B cell activation is deficient, which is consistent with delayed viral clearance in severely ill COVID-19 patients. Our study demonstrates that COVID-19 patients with ARDS have a state of immune imbalance in which dysregulation of both innate and adaptive immune responses may be contributing to a more severe disease course.

| S-EPMC7744012 | biostudies-literature

Adjunct Immunotherapies for the Management of Severely Ill COVID-19 Patients.

Project description:Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has infected millions, with more than 275,000 fatal cases as of May 8, 2020. Currently, there are no specific COVID-19 therapies. Most patients depend on mechanical ventilation. Current COVID-19 data clearly highlight that cytokine storm and activated immune cell migration to the lungs characterize the early immune response to COVID-19 that causes severe lung damage and development of acute respiratory distress syndrome. In view of uncertainty associated with immunosuppressive treatments, such as corticosteroids and their possible secondary effects, including risks of secondary infections, we suggest immunotherapies as an adjunct therapy in severe COVID-19 cases. Such immunotherapies based on inflammatory cytokine neutralization, immunomodulation, and passive viral neutralization not only reduce inflammation, inflammation-associated lung damage, or viral load but could also prevent intensive care unit hospitalization and dependency on mechanical ventilation, both of which are limited resources.

| S-EPMC7190525 | biostudies-literature

Role of extracorporeal membrane oxygenation in critically Ill COVID-19 patients and predictors of mortality.

Project description:The role of extracorporeal membrane oxygenation (ECMO) in the management of critically ill COVID-19 patients remains unclear. Our study aims to analyze the outcomes and risk factors from patients treated with ECMO. This retrospective, single-center study includes 17 COVID-19 patients treated with ECMO. Univariate and multivariate parametric survival regression identified predictors of survival. Nine patients (53%) were successfully weaned from ECMO and discharged. The incidence of in-hospital mortality was 47%. In a univariate analysis, only four out of 83 pre-ECMO variables were significantly different; IL-6, PCT, and NT-proBNP were significantly higher in non-survivors than in survivors. The Respiratory Extracorporeal Membrane Oxygenation Survival Prediction (RESP) score was significantly higher in survivors. After a multivariate parametric survival regression, IL-6, NT-proBNP and RESP scores remained significant independent predictors, with hazard ratios (HR) of 1.069 [95%-CI: 0.986-1.160], P = .016 1.001 [95%-CI: 1.000-1.001], P = .012; and .843 [95%-CI: 0.564-1.260], P = .040, respectively. A prediction model comprising IL-6, NT-proBNP, and RESP score showed an area under the curve (AUC) of 0.87, with a sensitivity of 87.5% and 77.8% specificity compared to an AUC of 0.79 for the RESP score alone. The present study suggests that ECMO is a potentially lifesaving treatment for selected critically ill COVID-19 patients. Considering IL-6 and NT-pro-BNP, in addition to the RESP score, may enhance outcome predictions.

| S-EPMC7753822 | biostudies-literature

Evaluation of Nutrition Risk and Its Association With Mortality Risk in Severely and Critically Ill COVID-19 Patients.

Project description:BackgroundThe nutrition status of coronavirus disease 2019 patients is unknown. This study evaluates clinical and nutrition characteristics of severely and critically ill patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and investigates the relationship between nutrition risk and clinical outcomes.MethodsA retrospective, observational study was conducted at West Campus of Union Hospital in Wuhan. Patients confirmed with SARS-CoV-2 infection by a nucleic acid-positive test and identified as severely or critically ill were enrolled in this study. Clinical data and outcomes information were collected and nutrition risk was assessed using Nutritional Risk Screening 2002 (NRS).ResultsIn total, 413 patients were enrolled in this study, including 346 severely and 67 critically ill patients. Most patients, especially critically ill patients, had significant changes in nutrition-related parameters and inflammatory markers. As for nutrition risk, the critically ill patients had significantly higher proportion of high NRS scores (P < .001), which were correlated with inflammatory and nutrition-related markers. Among 342 patients with NRS score ≥3, only 84 (of 342, 25%) received nutrition support. Critically ill patients and those with higher NRS score had a higher risk of mortality and longer stay in hospital. In logistic regression models, 1-unit increase in NRS score was associated with the risk of mortality increasing by 1.23 times (adjusted odds ratio, 2.23; 95% CI, 1.10-4.51; P = .026).ConclusionsMost severely and critically ill patients infected with SARS-CoV-2 are at nutrition risk. The patients with higher nutrition risk have worse outcome and require nutrition therapy.

| S-EPMC7361906 | biostudies-literature

ICU Admission Levels of Endothelial Biomarkers as Predictors of Mortality in Critically Ill COVID-19 Patients.

Project description:Endotheliopathy is suggested to be an important feature of COVID-19 in hospitalized patients. To determine whether endotheliopathy is involved in COVID-19-associated mortality, markers of endothelial damage were assessed in critically ill COVID-19 patients upon intensive care unit (ICU) admission. Thirty-eight critically ill COVID-19 patients were included in this observational study, 10 of whom died in the ICU. Endothelial biomarkers, including soluble (s)E-selectin, sP-selectin, angiopoietin 1 and 2 (Ang-1 and Ang-2, respectively), soluble intercellular adhesion molecule 1 (sICAM-1), vascular endothelial growth factor (VEGF), soluble vascular endothelial (VE)-cadherin, and von Willebrand factor (vWf), were measured upon ICU admission. The ICU cohort was subsequently divided into survivors and non-survivors; Kaplan-Meier analysis was used to explore associations between biomarkers and survival, while receiver operating characteristic (ROC) curves were generated to determine their potential prognostic value. sE-selectin, sP-selectin, Ang-2, and sICAM-1 were significantly elevated in ICU non-survivors compared to survivors, and also associated with a higher mortality probability in the Kaplan-Meier analysis. The prognostic values of sE-selectin, Ang-2, and sICAM-1 from the generated ROC curves were greater than 0.85. Hence, we conclude that in our cohort, ICU non-survivors had higher levels of specific endothelial markers compared to survivors. Elevated levels of these markers upon ICU admission could possibly predict mortality in COVID-19.

| S-EPMC7832393 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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