32 Works

Additional file 1 of Accuracy of clinicians’ ability to predict the need for renal replacement therapy: a prospective multicenter study

Alexandre Sitbon, Michael Darmon, Guillaume Geri, Paul Jaubert, Pauline Lamouche-Wilquin, Clément Monet, Lucie Le Fèvre, Marie Baron, Marie-Line Harlay, Côme Bureau, Olivier Joannes-Boyau, Claire Dupuis, Damien Contou, Virginie Lemiale, Marie Simon, Christophe Vinsonneau, Clarisse Blayau, Frederic Jacobs & Lara Zafrani
Additional file 1: Figure S1. Physician prediction: Visual Likert Scale. Figure S2. PresagEER study timeline. Table S1. Delays between ICU admission, AKI diagnosis and RRT initiation. Table S2. RRT characteristics. Table S3. Characteristics and outcomes of AKI patients (n (%) or median (IQR)). Table S4. Multivariate analysis including variables associated with the risk of requiring RRT (without physician prediction).

Additional file 9 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 9: Table S9. Summary of univariate Cox proportional hazard analysis of the previously identified CpGs. Column variables represent Name; chr; chromosome number, pos; position, CpG name, relation_to_Island; where is CpG located in relationship to island, UCSC RefGene Name; UCSC gene name, UCSC RefGene Accession; UCSC gene accession, UCSC RefGene Group; where in respect to gene is CpG located, Beta; estimated coefficient beta from the model, StandardError; standard error, Z; z-score, LRT; likelihood ratio...

Outcomes of mild-to-moderate postresuscitation shock after non-shockable cardiac arrest and association with temperature management: a post hoc analysis of HYPERION trial data

Ines Ziriat, Aurélie Le Thuaut, Gwenhael Colin, Hamid Merdji, Guillaume Grillet, Patrick Girardie, Bertrand Souweine, Pierre-François Dequin, Thierry Boulain, Jean-Pierre Frat, Pierre Asfar, Bruno Francois, Mickael Landais, Gaëtan Plantefeve, Jean-Pierre Quenot, Jean-Charles Chakarian, Michel Sirodot, Stéphane Legriel, Nicolas Massart, Didier Thevenin, Arnaud Desachy, Arnaud Delahaye, Vlad Botoc, Sylvie Vimeux, Frederic Martino … & Jean Baptiste Lascarrou
Abstract Background Outcomes of postresuscitation shock after cardiac arrest can be affected by targeted temperature management (TTM). A post hoc analysis of the “TTM1 trial” suggested higher mortality with hypothermia at 33 °C. We performed a post hoc analysis of HYPERION trial data to assess potential associations linking postresuscitation shock after non-shockable cardiac arrest to hypothermia at 33 °C on favourable functional outcome. Methods We divided the patients into groups with vs. without postresuscitation (defined...

Accuracy of clinicians’ ability to predict the need for renal replacement therapy: a prospective multicenter study

Alexandre Sitbon, Michael Darmon, Guillaume Geri, Paul Jaubert, Pauline Lamouche-Wilquin, Clément Monet, Lucie Le Fèvre, Marie Baron, Marie-Line Harlay, Côme Bureau, Olivier Joannes-Boyau, Claire Dupuis, Damien Contou, Virginie Lemiale, Marie Simon, Christophe Vinsonneau, Clarisse Blayau, Frederic Jacobs & Lara Zafrani
Abstract Purpose Identifying patients who will receive renal replacement therapy (RRT) during intensive care unit (ICU) stay is a major challenge for intensivists. The objective of this study was to evaluate the performance of physicians in predicting the need for RRT at ICU admission and at acute kidney injury (AKI) diagnosis. Methods Prospective, multicenter study including all adult patients hospitalized in 16 ICUs in October 2020. Physician prediction was estimated at ICU admission and at...

Additional file 3 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 3: Table S3. Summary of identified differentially methylated CpGs between COVID-19 patients and controls. A. All significant CpGs B. Variable description from Table S2A. C. Significant CpGs from genes previously described as COVID-19 important [1]. D. Functional annotation of genes from Supplemental table 3C.

Additional file 4 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 4: Table S4. Summary of differentially methylated pathways detected between COVID-19 patients and controls based on CpG sites.

Additional file 9 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 9: Table S9. Summary of univariate Cox proportional hazard analysis of the previously identified CpGs. Column variables represent Name; chr; chromosome number, pos; position, CpG name, relation_to_Island; where is CpG located in relationship to island, UCSC RefGene Name; UCSC gene name, UCSC RefGene Accession; UCSC gene accession, UCSC RefGene Group; where in respect to gene is CpG located, Beta; estimated coefficient beta from the model, StandardError; standard error, Z; z-score, LRT; likelihood ratio...

DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Abstract Background COVID-19 infections could be complicated by acute respiratory distress syndrome (ARDS), increasing mortality risk. We sought to assess the methylome of peripheral blood mononuclear cells in COVID-19 with ARDS. Methods We recruited 100 COVID-19 patients with ARDS under mechanical ventilation and 33 non-COVID-19 controls between April and July 2020. COVID-19 patients were followed at four time points for 60 days. DNA methylation and immune cell populations were measured at each time point. A...

Additional file 2 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 2: Table S2. Summary of different models tested for estimating differences between controls and COVID-19 patients for immune cell proportions. Adjusted R2, residual standard error (sigma), AIC, and p.value for each tested model for each cell type are shown. The following models were tested, mod1; Age and ethnicity as covariates, mod2; age as a covariate, mod3; ethnicity as a covariate, mod4; no covariates.

Accuracy of clinicians’ ability to predict the need for renal replacement therapy: a prospective multicenter study

Alexandre Sitbon, Michael Darmon, Guillaume Geri, Paul Jaubert, Pauline Lamouche-Wilquin, Clément Monet, Lucie Le Fèvre, Marie Baron, Marie-Line Harlay, Côme Bureau, Olivier Joannes-Boyau, Claire Dupuis, Damien Contou, Virginie Lemiale, Marie Simon, Christophe Vinsonneau, Clarisse Blayau, Frederic Jacobs & Lara Zafrani
Abstract Purpose Identifying patients who will receive renal replacement therapy (RRT) during intensive care unit (ICU) stay is a major challenge for intensivists. The objective of this study was to evaluate the performance of physicians in predicting the need for RRT at ICU admission and at acute kidney injury (AKI) diagnosis. Methods Prospective, multicenter study including all adult patients hospitalized in 16 ICUs in October 2020. Physician prediction was estimated at ICU admission and at...

Additional file 10 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 10: Table S10. Description of the eight genes that are predictors of mortality. Data were collected from Gene Ontology (GO) to identify the functional annotation of each gene and recently published COVID-19 related articles to highlight the role of each gene in relation to COVID-19.

Additional file 11 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 11. Supplementary Figures.

Additional file 7 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 7: Table S7. Summary of immune cell changes and differentially methylated CpGs between recovered and dead patients over four time points 7A. Immune cell changes between recovered and dead patients over four time points, 7B. Differential methylation of CpGs between recovered and died patients over four time points. The b_0, b_1, b_2, and b_3 coefficients correspond to the reference model parameters, where survival phenotype is used as a reference. The d_0, d_1, d_2,...

Additional file 1 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 1:Table S1. Baseline characteristics of COVID-19 patients and controls.

DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Abstract Background COVID-19 infections could be complicated by acute respiratory distress syndrome (ARDS), increasing mortality risk. We sought to assess the methylome of peripheral blood mononuclear cells in COVID-19 with ARDS. Methods We recruited 100 COVID-19 patients with ARDS under mechanical ventilation and 33 non-COVID-19 controls between April and July 2020. COVID-19 patients were followed at four time points for 60 days. DNA methylation and immune cell populations were measured at each time point. A...

Additional file 1 of Accuracy of clinicians’ ability to predict the need for renal replacement therapy: a prospective multicenter study

Alexandre Sitbon, Michael Darmon, Guillaume Geri, Paul Jaubert, Pauline Lamouche-Wilquin, Clément Monet, Lucie Le Fèvre, Marie Baron, Marie-Line Harlay, Côme Bureau, Olivier Joannes-Boyau, Claire Dupuis, Damien Contou, Virginie Lemiale, Marie Simon, Christophe Vinsonneau, Clarisse Blayau, Frederic Jacobs & Lara Zafrani
Additional file 1: Figure S1. Physician prediction: Visual Likert Scale. Figure S2. PresagEER study timeline. Table S1. Delays between ICU admission, AKI diagnosis and RRT initiation. Table S2. RRT characteristics. Table S3. Characteristics and outcomes of AKI patients (n (%) or median (IQR)). Table S4. Multivariate analysis including variables associated with the risk of requiring RRT (without physician prediction).

Additional file 10 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 10: Table S10. Description of the eight genes that are predictors of mortality. Data were collected from Gene Ontology (GO) to identify the functional annotation of each gene and recently published COVID-19 related articles to highlight the role of each gene in relation to COVID-19.

Additional file 4 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 4: Table S4. Summary of differentially methylated pathways detected between COVID-19 patients and controls based on CpG sites.

Additional file 2 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 2: Table S2. Summary of different models tested for estimating differences between controls and COVID-19 patients for immune cell proportions. Adjusted R2, residual standard error (sigma), AIC, and p.value for each tested model for each cell type are shown. The following models were tested, mod1; Age and ethnicity as covariates, mod2; age as a covariate, mod3; ethnicity as a covariate, mod4; no covariates.

Additional file 3 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 3: Table S3. Summary of identified differentially methylated CpGs between COVID-19 patients and controls. A. All significant CpGs B. Variable description from Table S2A. C. Significant CpGs from genes previously described as COVID-19 important [1]. D. Functional annotation of genes from Supplemental table 3C.

Additional file 6 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 6: Table S6. Analysis of dead and recovered+A3 COVID-19 patients for immune cell proportions. A. Summary of different models tested for immune cell proportions. Adjusted R2, residual standard error (sigma), AIC, and p-value for each tested model for each cell type are shown. The following models were tested, mod1; Age + MV days + Gender + ICU LoS + ECMO + Nosocomial infections, mod2; Age + Gender + ICU LoS + ECMO +...

Additional file 8 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 8: Table S8. Description of 27 genes from 49 differentially methylated CpGs between survived and dead patients over four time points. A summarized description of the 27 genes obtained from Supplementary Table 7B, collected from Gene Ontology (GO) to identify the functional annotation of each gene and recently published COVID-19-related articles to highlight the role of each gene in relation to COVID-19.

Additional file 8 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 8: Table S8. Description of 27 genes from 49 differentially methylated CpGs between survived and dead patients over four time points. A summarized description of the 27 genes obtained from Supplementary Table 7B, collected from Gene Ontology (GO) to identify the functional annotation of each gene and recently published COVID-19-related articles to highlight the role of each gene in relation to COVID-19.

Additional file 11 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 11. Supplementary Figures.

Additional file 1 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 1:Table S1. Baseline characteristics of COVID-19 patients and controls.

Registration Year

  • 2022
    32

Resource Types

  • Text
    14
  • Dataset
    12
  • Collection
    6

Affiliations

  • Centre Hospitalier Universitaire de Clermont-Ferrand
    32
  • Memorial Sloan Kettering Cancer Center
    24
  • Hamad Medical Corporation
    24
  • Hamad bin Khalifa University
    23
  • Cornell University
    23
  • Weill Cornell Medical College in Qatar
    23
  • Hôpital Cochin
    8
  • Centre for Research in Epidemiology and Population Health
    8
  • Centre Hospitalier Victor Dupouy
    8
  • Centre Hospitalier Universitaire de Nantes
    5