One-year mortality after ICU admission due to COVID-19 infection.
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ABSTRACT:
SUBMITTER: Ceccato A
PROVIDER: S-EPMC8764887 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Genome-wide DNA methylation analysis of COVID-19 severity using the Illumina HumanMethylationEPIC microarray platform to analyze over 850,000 methylation sites, comparing COVID-19 patients during and one year after infection, using whole blood tissue.
2022-02-23 | GSE197152 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2023-06-14 | GSE220078 | GEO
Project description:BackgroundSeveral studies have recently addressed factors associated with severe Coronavirus disease 2019 (COVID-19); however, some medications and comorbidities have yet to be evaluated in a large matched cohort. We therefore explored the role of relevant comorbidities and medications in relation to the risk of intensive care unit (ICU) admission and mortality.MethodsAll ICU COVID-19 patients in Sweden until 27 May 2020 were matched to population controls on age and gender to assess the risk of ICU admission. Cases were identified, comorbidities and medications were retrieved from high-quality registries. Three conditional logistic regression models were used for risk of ICU admission and three Cox proportional hazards models for risk of ICU mortality, one with comorbidities, one with medications and finally with both models combined, respectively.ResultsWe included 1981 patients and 7924 controls. Hypertension, type 2 diabetes mellitus, chronic renal failure, asthma, obesity, being a solid organ transplant recipient and immunosuppressant medications were independent risk factors of ICU admission and oral anticoagulants were protective. Stroke, asthma, chronic obstructive pulmonary disease and treatment with renin-angiotensin-aldosterone inhibitors (RAASi) were independent risk factors of ICU mortality in the pre-specified primary analyses; treatment with statins was protective. However, after adjusting for the use of continuous renal replacement therapy, RAASi were no longer an independent risk factor.ConclusionIn our cohort oral anticoagulants were protective of ICU admission and statins was protective of ICU death. Several comorbidities and ongoing RAASi treatment were independent risk factors of ICU admission and ICU mortality.
| S-EPMC8013403 | biostudies-literature
Project description:Individuals infected with SARS-CoV-2 vary greatly in their symptomatology and disease progression, likely as a result of numerous genetic, biological and environmental factors and their complex interactions. Meanwhile, the potential roles of microRNAs (miRNAs) in SARS-CoV-2 infection have not been fully described. MiRNAs have emerged as key post-transcriptional regulators of gene expression, and their dysregulation can be indicative of aberrant immune function. In this study, we characterize the potential roles of mIRNAs in early COVID-19 disease progression. We studied a diverse cohort of 259 patients admitted to hospitals in Abu Dhabi, United Arab Emirates to understand the clinical and biological factors associated with ICU admission during COVID-19 treatment, integrating electronic health records (EHR), global miRNA and RNA expression, and genotyping data. Using EHR, we identified 26 factors correlated with ICU admission, including 8 blood phenotypes such as neutrophil-to-lymphocyte ratio, Interleukin-6, and C-reactive protein levels. Using genome-wide miRNA expression data for a subset of 96 individuals from Southeast Asia and the Middle East and North Africa, we identified 27 miRNAs significantly associated with ICU admission (p < 0.01), and 97 miRNAs associated with at least one of the 8 blood phenotypes. [cross-cor] Integrating expression data for 632 miRNAs and genotyping data for ~260,000 SNPs, we identified 168 significant cis-expression quantitative trait loci (cis-eQTLs), of which 59 were associated with either ICU admission or one of the 8 blood phentoypes. Overall, our findings characterize the miRNA architecture of blood phenotypes during the early stages of COVID-19 infection, identify miRNAs associated with ICU admission and therefore COVID-19 disease severity, and suggest a potential genetic control of miRNA expression during early COVID-19 disease progression.
2023-06-14 | GSE220077 | GEO
Project description:Individuals infected with SARS-CoV-2 vary greatly in their symptomatology and disease progression, likely as a result of numerous genetic, biological and environmental factors and their complex interactions. Meanwhile, the potential roles of microRNAs (miRNAs) in SARS-CoV-2 infection have not been fully described. MiRNAs have emerged as key post-transcriptional regulators of gene expression, and their dysregulation can be indicative of aberrant immune function. In this study, we characterize the potential roles of mIRNAs in early COVID-19 disease progression. We studied a diverse cohort of 259 patients admitted to hospitals in Abu Dhabi, United Arab Emirates to understand the clinical and biological factors associated with ICU admission during COVID-19 treatment, integrating electronic health records (EHR), global miRNA and RNA expression, and genotyping data. Using EHR, we identified 26 factors correlated with ICU admission, including 8 blood phenotypes such as neutrophil-to-lymphocyte ratio, Interleukin-6, and C-reactive protein levels. Using genome-wide miRNA expression data for a subset of 96 individuals from Southeast Asia and the Middle East and North Africa, we identified 27 miRNAs significantly associated with ICU admission (p < 0.01), and 97 miRNAs associated with at least one of the 8 blood phenotypes. [cross-cor] Integrating expression data for 632 miRNAs and genotyping data for ~260,000 SNPs, we identified 168 significant cis-expression quantitative trait loci (cis-eQTLs), of which 59 were associated with either ICU admission or one of the 8 blood phentoypes. Overall, our findings characterize the miRNA architecture of blood phenotypes during the early stages of COVID-19 infection, identify miRNAs associated with ICU admission and therefore COVID-19 disease severity, and suggest a potential genetic control of miRNA expression during early COVID-19 disease progression.
2023-06-14 | GSE220076 | GEO
Project description:This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment.
| S-EPMC7392248 | biostudies-literature
Project description:As predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) Healthcare database, we developed and internally validated models using patients presenting to the Emergency Department (ED) between March-April 2020 (n = 3597) and further validated them using temporally distinct individuals who presented to the ED between May-August 2020 (n = 1711). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and O2 saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m2, and neutrophil and lymphocyte percentages were the most important variables for predicting mortality. Implementing such models could help in clinical decision-making for future infectious disease outbreaks including COVID-19.
| S-EPMC8140139 | biostudies-literature
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
Project description:BackgroundThis study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients.MethodsThis retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC).ResultsThe top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760-0.785]) and 0.844 (95% CI [0.839-0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726-0.729]) and 0.848 (95% CI [0.847-0.849]), respectively.ConclusionsDeep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.
| S-EPMC7651477 | biostudies-literature
Project description:Genome-wide DNA methylation analysis one year post-COVID-19 infection
| PRJNA809228 | ENA