Browse
Submit Data
Databases
API
Help

Dataset Information

14 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Predicting 90-day survival of patients with COVID-19: Survival of Severely Ill COVID (SOSIC) scores.

ABSTRACT: Predicting outcomes of critically ill intensive care unit (ICU) patients with coronavirus-19 disease (COVID-19) is a major challenge to avoid futile, and prolonged ICU stays. The objective was to develop predictive survival models for patients with COVID-19 after 1-to-2 weeks in ICU. Based on the COVID-ICU cohort, which prospectively collected characteristics, management, and outcomes of critically ill patients with COVID-19. Machine learning was used to develop dynamic, clinically useful models able to predict 90-day mortality using ICU data collected on day (D) 1, D7 or D14. Survival of Severely Ill COVID (SOSIC)-1, SOSIC-7, and SOSIC-14 scores were constructed with 4244, 2877, and 1349 patients, respectively, randomly assigned to development or test datasets. The three models selected 15 ICU-entry variables recorded on D1, D7, or D14. Cardiovascular, renal, and pulmonary functions on prediction D7 or D14 were among the most heavily weighted inputs for both models. For the test dataset, SOSIC-7's area under the ROC curve was slightly higher (0.80 [0.74-0.86]) than those for SOSIC-1 (0.76 [0.71-0.81]) and SOSIC-14 (0.76 [0.68-0.83]). Similarly, SOSIC-1 and SOSIC-7 had excellent calibration curves, with similar Brier scores for the three models. The SOSIC scores showed that entering 15 to 27 baseline and dynamic clinical parameters into an automatable XGBoost algorithm can potentially accurately predict the likely 90-day mortality post-ICU admission (sosic.shinyapps.io/shiny). Although external SOSIC-score validation is still needed, it is an additional tool to strengthen decisions about life-sustaining treatments and informing family members of likely prognosis.

SUBMITTER: Schmidt M

PROVIDER: S-EPMC8665857 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Transcriptomic clustering of critically-ill COVID-19 patients [Day 4 RNAseq]

Project description:Infections caused by SARS-CoV-2 may cause a severe disease, termed COVID-19, with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms, and their modulation is the only therapeutic strategy that has shown a mortality benefit. Herein, we used peripheral blood transcriptomes of critically-ill COVID-19 patients obtained at admission in an Intensive Care Unit, to identify two clusters that, in spite of no major clinical differences, have different gene expression profiles that reveal different underlying pathogenetic mechanisms and ultimately have different ICU outcome. A transcriptomic signature was used to identify these clusters in an external validation cohort, yielding a similar result. These results illustrate the potential of transcriptomic profiles to identify patient endotypes and point to relevant pathogenetic mechanisms in COVID-19.

2023-04-29 | GSE206264 | GEO

Role of Prealbumin in Predicting the Prognosis of Severely and Critically Ill COVID-19 Patients.

Project description:Most critically ill patients experience malnutrition, resulting in a poor prognosis. This study aimed to evaluate the association of prealbumin (PAB) with the prognosis for severely and critically ill coronavirus disease 2019 (COVID-19) patients and explore factors related to this association. Patients with laboratory-confirmed COVID-19 from West Campus of Union Hospital in Wuhan from January 29, 2020 to March 31, 2020 were enrolled in this study. Patients were classified into the PAB1 (150-400 mg/L; N = 183) and PAB2 (< 150 mg/L; N = 225) groups. Data collection was performed using the hospital's electronic medical records system. The predictive value of PAB was evaluated by measuring the area under the receiver-operating characteristic (AUROC) curve. Patients were defined as severely or critically ill based on the Guidance for COVID-19 (7th edition) by the National Health Commission of China. During this analysis, 316 patients had severe cases and 65 had critical cases. A reduced PAB level was associated with a higher risk of mortality and a longer hospital stay. The AUROC curve for the prognosis based on the PAB level was 0.93, with sensitivity of 97.2% and specificity of 77.6%. For severe cases, a lower level of PAB was associated with a higher risk of malnutrition, higher NK cell counts, and lower B lymphocyte counts; these factors were not significant in critical cases. C-reactive protein and nutritional status mediated the association between PAB and prognosis. This retrospective analysis suggests that the PAB level on admission is an indicator of the prognosis for COVID-19.

| S-EPMC8592333 | biostudies-literature

Transcriptomic clustering of critically-ill COVID-19 patients [Day 1-miRNA-Seq]

2022-02-28 | GSE197258 | GEO

Transcriptomic clustering of critically-ill COVID-19 patients [Day 1-RNA-Seq]

2022-02-28 | GSE197204 | GEO

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

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