14 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 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 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.
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 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.
Additional file 1 of 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
Additional file 1: Fig. S1. Study flowchart. Table S1. Baseline characteristics and mortality in the groups with vs. without mild-to-moderate postresuscitation shock (PRS) at intensive-care-unit (ICU) admission. Table S2. Multivariate logistic regression modelling to identify admission variables associated with a favourable outcome on day 90 in the overall population (n = 532).
Additional file 1 of 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
Additional file 1: Fig. S1. Study flowchart. Table S1. Baseline characteristics and mortality in the groups with vs. without mild-to-moderate postresuscitation shock (PRS) at intensive-care-unit (ICU) admission. Table S2. Multivariate logistic regression modelling to identify admission variables associated with a favourable outcome on day 90 in the overall population (n = 532).
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 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 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 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 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).
Affiliations
-
Centre Hospitalier Universitaire de Clermont-Ferrand14
-
Memorial Sloan Kettering Cancer Center10
-
Hamad Medical Corporation10
-
Hamad bin Khalifa University10
-
Cornell University8
-
Weill Cornell Medical College in Qatar8
-
Hôpital Cochin4
-
Centre for Research in Epidemiology and Population Health4
-
Centre Hospitalier Victor Dupouy4
-
Centre Hospitalier Universitaire de Tours2