Author: "Burgun, A." / Language: undetermined - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Burgun, A."' showing total 357 results

Start Over Author "Burgun, A." Language undetermined

357 results on '"Burgun, A."'

1. A systemic approach to grapevine decline diagnosed using three key indicators: plant mortality, yield loss and vigour decrease

Author: Anne MEROT, Guillaume Coulouma, Nathalie Smits, Elsa Robelot, Christian Gary, Lucia Guerin-Dubrana, Jouanel Poulmach, Xavier Burgun, Anne Pellegrino, Marc Fermaud, Agrosystèmes Biodiversifiés (UMR ABSys), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Santé et agroécologie du vignoble (UMR SAVE), Université de Bordeaux (UB)-Institut des Sciences de la Vigne et du Vin (ISVV)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Chambre d'Agriculture de l'Hérault (CA 34), Institut Français de la Vigne et du Vin (IFV), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, and Plan National Dépérissement Viticole (PNDV) / France Agrimer
Subjects: vine mortality, diagnosis, dieback, [SDV]Life Sciences [q-bio], yield loss, vigour, Horticulture, decline, indicators, Food Science
Abstract: International audience; Grapevine decline, a major global viticulture issue, is defined as a multi-year decrease in vine productivity and/or increase in vine mortality. Although grapevine trunk diseases are one of the most studied causes, the decline is multifactorial and associated with more than 70 factors, including abiotic and biotic hazards. With so many factors to consider, the phenomenon is difficult to understand. Our study aims to make it easier to determine and assess grapevine decline by focusing on three key indicators: yield, mortality and vegetative vigour. We investigated the relationships between these indicators from both a temporal and spatial perspective to propose a set of diagnostic indicators. Thus, we conducted a winegrower survey, a historical analysis of grapevine decline and field measurements of the abovementioned indicators on plot networks in three major French winegrowing regions (see graphical abstract): Bordeaux, Cognac and Languedoc. We found that winegrowers' perceptions of decline were consistent with an objective characterisation based on field measurements of the indicators. Although vine mortality progressively spread over the years, neither the survey nor the historical analysis showed a direct link between decline and yield loss. Rather, large yearly fluctuations in yield, which did not systematically decrease over time, account for this finding. As a result, the mortality rate and the normalised difference vegetation index (NDVI) indicators were shown to be earlier indicators of grapevine decline than yield loss (addressed from the yield achievement ratio, YAR). We performed a multifactorial analysis of the overall data set from the three regions to deepen our understanding of the diversity of declining situations and the underlying environmental and management factors contributing to decline. Finally, two ground-based NDVI indicators and an image-analysis methodology using aerial photographs were proposed as easy-to-obtain indicators of grapevine decline. NDVI indicators were linearly correlated with both the YAR and mortality rate. This study provides a better understanding and promising tools for the early diagnosis of grapevine decline.
Published: 2023
Full Text: View/download PDF

2. Association Between FIASMAs and Reduced Risk of Intubation or Death in Individuals Hospitalized for Severe COVID‐19: An Observational Multicenter Study

Author: Hoertel, Nicolas, Sánchez-Rico, Marina, Gulbins, Erich, Kornhuber, Johannes, Carpinteiro, Alexander, Lenze, Eric J., Reiersen, Angela M., Abellán, Miriam, de la Muela, Pedro, Vernet, Raphaël, Blanco, Carlos, Cougoule, Céline, Neuraz, Antoine, Gorwood, Philip, Alvarado, Jesús M., Meneton, Pierre, Limosin, Frédéric, Ancel, Pierre-Yves, Bauchet, Alain, Beeker, Nathanaël, Benoit, Vincent, Bernaux, Mélodie, Bellamine, Ali, Bey, Romain, Bourmaud, Aurélie, Breant, Stéphane, Burgun, Anita, Carrat, Fabrice, Caucheteux, Charlotte, Champ, Julien, Cormont, Sylvie, Daniel, Christel, Dubiel, Julien, Ducloas, Catherine, Esteve, Loic, Frank, Marie, Garcelon, Nicolas, Gramfort, Alexandre, Griffon, Nicolas, Grisel, Olivier, Guilbaud, Martin, Hassen-Khodja, Claire, Hemery, François, Hilka, Martin, Sophie Jannot, Anne, Lambert, Jerome, Layese, Richard, Leblanc, Judith, Lebouter, Léo, Lemaitre, Guillaume, Leprovost, Damien, Lerner, Ivan, Levi Sallah, Kankoe, Maire, Aurélien, Mamzer, Marie-France, Martel, Patricia, Mensch, Arthur, Moreau, Thomas, Orlova, Nina, Paris, Nicolas, Rance, Bastien, Ravera, Hélène, Rozes, Antoine, Salamanca, Elisa, Sandrin, Arnaud, Serre, Patricia, Tannier, Xavier, Treluyer, Jean-Marc, van Gysel, Damien, Varoquaux, Gaël, Vie, Jill Jen, Wack, Maxime, Wajsburt, Perceval, Wassermann, Demian, Zapletal, Eric, Institut de psychiatrie et neurosciences de Paris (IPNP - U1266 Inserm), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Hôpital Corentin Celton [Issy-les-Moulineaux], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Universidad Complutense de Madrid = Complutense University of Madrid [Madrid] (UCM), University of Duisburg-Essen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Washington University School of Medicine in St. Louis, Washington University in Saint Louis (WUSTL), Hôpital Européen Georges Pompidou [APHP] (HEGP), National Institute on Drug Abuse [Bethesda] (NIDA), Institut de pharmacologie et de biologie structurale (IPBS), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), CHU Necker - Enfants Malades [AP-HP], Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, AP-HP / Université de Paris / INSERM COVID-19 research collaboration, AP-HP COVID CDR Initiative, 'Entrepôt de Données de Santé' AP-HP Consortium: Pierre-Yves Ancel, Alain Bauchet, Nathanaël Beeker, Vincent Benoit, Mélodie Bernaux, Ali Bellamine, Romain Bey, Aurélie Bourmaud, Stéphane Breant, Anita Burgun, Fabrice Carrat, Charlotte Caucheteux, Julien Champ, Sylvie Cormont, Christel Daniel, Julien Dubiel, Catherine Ducloas, Loic Esteve, Marie Frank, Nicolas Garcelon, Alexandre Gramfort, Nicolas Griffon, Olivier Grisel, Martin Guilbaud, Claire Hassen-Khodja, François Hemery, Martin Hilka, Anne Sophie Jannot, Jerome Lambert, Richard Layese, Judith Leblanc, Léo Lebouter, Guillaume Lemaitre, Damien Leprovost, Ivan Lerner, Kankoe Levi Sallah, Aurélien Maire, Marie-France Mamzer, Patricia Martel, Arthur Mensch, Thomas Moreau, Antoine Neuraz, Nina Orlova, Nicolas Paris, Bastien Rance, Hélène Ravera, Antoine Rozes, Elisa Salamanca, Arnaud Sandrin, Patricia Serre, Xavier Tannier, Jean-Marc Treluyer, Damien van Gysel, Gaël Varoquaux, Jill Jen Vie, Maxime Wack, Perceval Wajsburt, Demian Wassermann, Eric Zapletal, Martinez Rico, Clara, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), National Institute on Drug Abuse [Bethesda], Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Collaborators : AP-HP / Université de Paris / INSERM COVID-19 research collaboration, AP-HP COVID CDR Initiative, 'Entrepôt de Données de Santé' AP-HP Consortium: Pierre-Yves Ancel, Alain Bauchet, Nathanaël Beeker, Vincent Benoit, Mélodie Bernaux, Ali Bellamine, Romain Bey, Aurélie Bourmaud, Stéphane Breant, Anita Burgun, Fabrice Carrat, Charlotte Caucheteux, Julien Champ, Sylvie Cormont, Christel Daniel, Julien Dubiel, Catherine Ducloas, Loic Esteve, Marie Frank, Nicolas Garcelon, Alexandre Gramfort, Nicolas Griffon, Olivier Grisel, Martin Guilbaud, Claire Hassen-Khodja, François Hemery, Martin Hilka, Anne Sophie Jannot, Jerome Lambert, Richard Layese, Judith Leblanc, Léo Lebouter, Guillaume Lemaitre, Damien Leprovost, Ivan Lerner, Kankoe Levi Sallah, Aurélien Maire, Marie-France Mamzer, Patricia Martel, Arthur Mensch, Thomas Moreau, Antoine Neuraz, Nina Orlova, Nicolas Paris, Bastien Rance, Hélène Ravera, Antoine Rozes, Elisa Salamanca, Arnaud Sandrin, Patricia Serre, Xavier Tannier, Jean-Marc Treluyer, Damien Van Gysel, Gaël Varoquaux, Jill Jen Vie, Maxime Wack, Perceval Wajsburt, Demian Wassermann, Eric Zapletal, Universität Duisburg-Essen = University of Duisburg-Essen [Essen], Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), École Pratique des Hautes Études (EPHE), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Imagine - Institut des maladies génétiques (IMAGINE - U1163), and Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: Male, [SDV]Life Sciences [q-bio], medicine.medical_treatment, Medizin, law.invention, Cohort Studies, COVID-19 Testing, medications, Randomized controlled trial, law, Clinical endpoint, Intubation, Pharmacology (medical), Enzyme Inhibitors, acid sphingomyelinase, Aged, 80 and over, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, treatment, Inverse probability weighting, Hazard ratio, Middle Aged, 3. Good health, Hospitalization, [SDV] Life Sciences [q-bio], Sphingomyelin Phosphodiesterase, Female, FIASMA, Adult, medicine.medical_specialty, Adolescent, intubation, Young Adult, Internal medicine, Intubation, Intratracheal, medicine, Humans, ceramide, Aged, Retrospective Studies, Pharmacology, SARS-CoV-2, Proportional hazards model, business.industry, COVID-19, mortality, Confidence interval, COVID-19 Drug Treatment, Observational study, business, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract: Collaborators : AP-HP / Université de Paris / INSERM COVID-19 research collaboration, AP-HP COVID CDR Initiative, “Entrepôt de Données de Santé” AP-HP Consortium: Pierre-Yves Ancel, Alain Bauchet, Nathanaël Beeker, Vincent Benoit, Mélodie Bernaux, Ali Bellamine, Romain Bey, Aurélie Bourmaud, Stéphane Breant, Anita Burgun, Fabrice Carrat, Charlotte Caucheteux, Julien Champ, Sylvie Cormont, Christel Daniel, Julien Dubiel, Catherine Ducloas, Loic Esteve, Marie Frank, Nicolas Garcelon, Alexandre Gramfort, Nicolas Griffon, Olivier Grisel, Martin Guilbaud, Claire Hassen-Khodja, François Hemery, Martin Hilka, Anne Sophie Jannot, Jerome Lambert, Richard Layese, Judith Leblanc, Léo Lebouter, Guillaume Lemaitre, Damien Leprovost, Ivan Lerner, Kankoe Levi Sallah, Aurélien Maire, Marie-France Mamzer, Patricia Martel, Arthur Mensch, Thomas Moreau, Antoine Neuraz, Nina Orlova, Nicolas Paris, Bastien Rance, Hélène Ravera, Antoine Rozes, Elisa Salamanca, Arnaud Sandrin, Patricia Serre, Xavier Tannier, Jean-Marc Treluyer, Damien Van Gysel, Gaël Varoquaux, Jill Jen Vie, Maxime Wack, Perceval Wajsburt, Demian Wassermann, Eric Zapletal; International audience; Several medications commonly used for a number of medical conditions share a property of functional inhibition of acid sphingomyelinase (ASM), or FIASMA. Preclinical and clinical evidence suggest that the (ASM)/ceramide system may be central to SARS-CoV-2 infection. We examined the potential usefulness of FIASMA use among patients hospitalized for severe COVID-19 in an observational multicenter study conducted at Greater Paris University hospitals. Of 2,846 adult patients hospitalized for severe COVID-19, 277 (9.7%) were taking a FIASMA medication at the time of their hospital admission. The primary endpoint was a composite of intubation and/or death. We compared this endpoint between patients taking vs. not taking a FIASMA medication in time-to-event analyses adjusted for sociodemographic characteristics and medical comorbidities. The primary analysis was a Cox regression model with inverse probability weighting (IPW). Over a mean follow-up of 9.2 days (SD=12.5), the primary endpoint occurred in 104 patients (37.5%) receiving a FIASMA medication, and 1,060 patients (41.4%) who did not. Despite being significantly and substantially associated with older age and greater medical severity, FIASMA medication use was significantly associated with reduced likelihood of intubation or death in both crude (HR=0.71; 95%CI=0.58-0.87; p
Published: 2021
Full Text: View/download PDF

3. Effects of Rare Earth Metal Promotion over Zeolite-Supported Fe–Cu-Based Catalysts on the Light Olefin Production Performance in Fischer–Tropsch Synthesis

Author: Utku Burgun, Hadi R. Zonouz, Hasancan Okutan, Hüsnü Atakül, Selim Senkan, Alper Sarioglan, and Gamze Gumuslu Gur
Subjects: General Chemical Engineering, General Chemistry
Published: 2022
Full Text: View/download PDF

4. Prévenir la nullité de mariage par la préparation au mariage. Quelques repères

Author: Cédric Burgun
Published: 2022
Full Text: View/download PDF

5. Ethical Aspects of Artificial Intelligence in Radiation Oncology

Author: Lucien, Lahmi, Marie-France, Mamzer, Anita, Burgun, Catherine, Durdux, Jean-Emmanuel, Bibault, Institut Curie [Paris], Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Service d'informatique médicale, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)
Subjects: Cancer Research, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Oncology, Artificial Intelligence, Radiation Oncology, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Humans, Radiology, Nuclear Medicine and imaging, Delivery of Health Care, Workflow, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; Radiation oncology is a field that heavily relies on new technology. Data science and artificial intelligence will have an important role in the entire radiotherapy workflow. A new paradigm of routine healthcare data reuse to automate treatments and provide decision support is emerging. This review will discuss the ethical aspects of the use of artificial intelligence (AI) in radiation oncology. More specifically, the review will discuss the evolution of work through the ages, as well as the impact AI will have on it. We will then explain why AI opens a new technical era for the field and we will conclude on the challenges in the years to come.
Published: 2022
Full Text: View/download PDF

6. Dématérialisation informatique de la procédure et notifications

Author: Cédric Burgun
Published: 2022
Full Text: View/download PDF

7. De la concordia à la conventio

Author: Cédric Burgun
Published: 2022
Full Text: View/download PDF

8. Improving Patient Similarity Using Different Modalities of Phenotypes Extracted from Clinical Narratives

Author: Xiaoyi Chen, Carole Faviez, Marc Vincent, Sophie Saunier, Nicolas Garcelon, and Anita Burgun
Abstract: In the context of medical concept extraction, it is critical to determine if clinical signs or symptoms mentioned in the text were present or absent, experienced by the patient or their relatives. Previous studies have focused on the NLP aspect but not on how to leverage this supplemental information for clinical applications. In this paper, we aim to use the patient similarity networks framework to aggregate different phenotyping modalities. NLP techniques were applied to extract phenotypes and predict their modalities from 5470 narrative reports of 148 patients with ciliopathies (a group of rare diseases). Patient similarities were computed using each modality separately for aggregation and clustering. We found that aggregating negated phenotypes improved patient similarity, but further aggregating relatives’ phenotypes worsened the result. We suggest that different modalities of phenotypes can contribute to patient similarity, but they should be aggregated carefully and with appropriate similarity metrics and aggregation models.
Published: 2023
Full Text: View/download PDF

9. The genetic landscape and clinical spectrum of nephronophthisis and related ciliopathies

Author: Friederike Petzold, Katy Billot, Xiaoyi Chen, Charline Henry, Emilie Filhol, Yoann Martin, Marina Avramescu, Maxime Douillet, Vincent Morinière, Pauline Krug, Cécile Jeanpierre, Kalman Tory, Olivia Boyer, Anita Burgun, Aude Servais, Remi Salomon, Alexandre Benmerah, Laurence Heidet, Nicolas Garcelon, Corinne Antignac, Mohamad Zaidan, Sophie Saunier, Tania Attié-Bitach, Valerie Comier-Daire, Jean-Michel Rozet, Yaacov Frishberg, Brigitte Llanas, Michel Broyer, Nabil Mohsin, Marie-Alice Macher, Nicole Philip, Véronique Baudouin, Damian Brackman, Chantal Loirat, Marina Charbit, Maud Dehennault, Claude Guyot, Pierre Bataille, Mariet Elting, Georges Deschenes, Andrea Gropman, Geneviève Guest, Marie-France Gagnadoux, Philippe Nicoud, Pierre Cochat, Bruno Ranchin, Albert Bensman, Anne-Marie Guerrot, Bertrand Knebelmann, Ilmay Bilge, Danièle Bruno, Stéphane Burtey, Caroline Rousset Rouvière, Valérie Caudwell, Denis Morin, Hélène Dollfus, Anne Maisin, Christian Hamel, Eric Bieth, Sophie Gie, Judith Goodship, Gwenaelle Roussey, Hermine La Selve, Hubert Nivet, Lucie Bessenay, Mathilde Caillez, Jean Bernard Palcoux, Stéphane Benoît, Philippe Dubot, Marc Fila, Fabienne Giuliano, Daouya Iftene, Michele Kessler, Theresa Kwon, Anine Lahoche, Audrey Laurent, Anne-Laure Leclerc, David Milford, Thomas Neuhaus, Sylvie Odent, Philippe Eckart, Dominique Chauveau, Patrick Niaudet, Horacio Repetto, Sophie Taque, Alexandra Bruel, Alexandra Noel-Botte, Emma Allain Launay, Lisa Allard, Dany Anlicheau, Anne-Laure Adra, Arnaud Garnier, Arvind Nagra, Remy Baatard, Justine Bacchetta, Banu Sadikoglu, Christine Barnerias, Anne Barthelemy, Lina Basel, Nader Bassilios, Hedi Ben Maiz, Fatma Ben Moussa, Faïza Benmati, Romain Berthaud, Aurélia Bertholet, Dominique Blanchier, Jean Jacques Boffa, Karim Bouchireb, Ihab Bouhabel, Zakaria Boukerroucha, Guylhène Bourdat-Michel, Odile Boute, Karine Brochard, Roseline Caumes, Siham Chafai Elalaoui, Bernard Chamontin, Marie Caroline Chastang, Christine Pietrement, Christine Richer, Christophe Legendre, Karin Dahan, Fabienne Dalla-Vale, Damien Thibaudin, Maxime Dauvergne, Salandre Davourie, Martin Debeukelaer, Jean Daniel Delbet, Constantinos Deltas, Denis Graber, Nadège Devillars, Boucar Diouf, Martine Doco Fenzy, Jean-Luc André, Dominique Joly, Alan Fryer, Laetitia Albano, Elisabeth Cassuto, Aline Pincon, Ana Medeira, Annabelle Chaussenot, Anne Mensire-Marinier, Francois Bouissou, Stephane Decramer, Armand Bottani, Aurélie Hummel, Alexandre Karras, Avi Katz, Christine Azema, Bénédicte Janbon, Bernard Roussel, Claude Bonniol, Christiophe Mariat, Gérard Champion, Deborah Chantreuil, Nicolas Chassaing, Christiane Mousson, Christine Baudeau, Delphine Hafdar Cuntz, Cyril Mignot, Laurene Dehoux, Didier Lacombe, Thierry Hannedouche, Elodie Mérieau, Emmanuelle Charlin, Eric Gauthier, Florent Plasse, Stanislas Faguer, Fanny Lebas, Florence Demurger, Francesco Emma, François Cartault, Geneviève Dumont, Nathalie Godefroid, Vincent Guigonis, Sophie Hillaire, Jaap Groothoff, Jan Dudley, Noémie Jourde-Chiche, Khalil El Karoui, Saoussen Krid, Krier Coudert, Larbi Bencheick, Laurent Yver, Marie-Pierre Lavocat, Le Monies De Sagazan, Valerie Leroy, Lise Thibaudin, Liz Ingulli, Lorraine Gwanmesia, Lydie Burglen, Marie-Hélène Saïd-Menthon, Marta Carrera, Mathilde Nizon, Catherine Melander, Michel Foulard, Monique Blayo, Jacques Prinseau, Nadine Jay, Nathalie Brun, Nicolas Camille, François Nobili, Olivier Devuyst, Ouafa Ben Brahim, Paloma Parvex, Laurence Perrin Sabourin, Philippe Blanc, Philippe Vanhille, Pierre Galichon, Sophie Pierrepont, Vincent Planquois, Gwenaelle Poussard, Claire Pouteil Noble, Radia Allal, Raphaelle Bernard, Raynaud Mounet, Rémi Cahen, Renaud Touraine, Claire Rigothier, Amélie Ryckewaert, Mathieu Sacquepee, Salima El Chehadeh, Charlotte Samaille, Shuman Haq, Ari Simckes, Stéphanie Lanoiselée, Stephanie Tellier, Jean-François Subra, Sylvie Cloarec, Julie Tenenbam, Thomas Lamy, Valérie Drouin Garraud, Huguette Valette, Vanina Meyssonnier, Rosa Vargas-Poussou, Yves Snajer, Sandrine Durault, Emmanuelle Plaisier, Etienne Berard, Fadi Fakhouri, Ferielle Louillet, Paul Finielz, Michel Fischbach, Bernard Foliguet, Hélène Francois-Pradier, Florentine Garaix, Marion Gerard, Gianfranco Rizzoni, Brigitte Gilbert, Denis Glotz, Astrid Godron Dubrasquet, Jean-Pierre Grünfeld, Guillaume Bollee, Michelle Hall, Sverker Hansson, Damien Haye, Hélène Taffin, Friedhelm Hildebrandt, Maryvonne Hourmand, Hümya Kayserili, Ivan Tack, Marie Line Jacquemont, Jennifer Fabre-Teste, Cliff Kashtan, Kkoen Van Hoeck, Alexandre Klein, Yannick Knefati, Nine Knoers, Martin Konrad, Alain Lachaux, Isabelle Landru, Gilbert Landthaler, Philippe Lang, Patrick Le Pogamp, Tristan Legris, Catherine Didailler, Thierry Lobbedez, Loïc de Parscau, Lucile Pinson, Hervé Maheut, Marc Duval-Arnould, Marlène Rio, Marie-Claire Gubler, Pierre Merville, Guillaume Mestrallet, Maite Meunier, Karine Moreau, Jérôme Harambat, Graeme Morgan, Georges Mourad, Niksic Stuber, Odile Boespflug-Tanguy, Olivier Dunand, Olivier Niel, Nacera Ouali, Paolo Malvezzi, Pauline Abou Jaoude, Solenne Pelletier, Julie Peltier, M.B. Petersen, Philippe Michel, Philippe Rémy, Jean-Baptiste Philit, Valérie Pichault, Thierry Billette de Villemeur, Bernard Boudailliez, Bruno Leheup, Claire Dossier, Djamal-Dine Djeddi, Yves Berland, Bruno Hurault de Ligny, Susan Rigden, Christophe Robino, Annick Rossi, Sabine Sarnacki, Messaoud Saidani, Albane Brodin Sartorius, Elise Schäfer, Sztriha Laszlo, Marie-Christine Thouret, Angélique Thuillier-Lecouf, Howard Trachtman, Claire Trivin, Michel Tsimaratos, Rita Van Damme-Lombaerts, Marjolaine Willems, Michel Youssef, Ariane Zaloszyc, Alexis Zawodnik, and Marie-Julia Ziliotis
Subjects: Nephrology
Abstract: Nephronophthisis (NPH) is an autosomal-recessive ciliopathy representing one of the most frequent causes of kidney failure in childhood characterized by a broad clinical and genetic heterogeneity. Applied to one of the worldwide largest cohorts of patients with NPH, genetic analysis encompassing targeted and whole exome sequencing identified disease-causing variants in 600 patients from 496 families with a detection rate of 71%. Of 788 pathogenic variants, 40 known ciliopathy genes were identified. However, the majority of patients (53%) bore biallelic pathogenic variants in NPHP1. NPH-causing gene alterations affected all ciliary modules defined by structural and/or functional subdomains. Seventy six percent of these patients had progressed to kidney failure, of which 18% had an infantile form (under five years) and harbored variants affecting the Inversin compartment or intraflagellar transport complex A. Forty eight percent of patients showed a juvenile (5-15 years) and 34% a late-onset disease (over 15 years), the latter mostly carrying variants belonging to the Transition Zone module. Furthermore, while more than 85% of patients with an infantile form presented with extra-kidney manifestations, it only concerned half of juvenile and late onset cases. Eye involvement represented a predominant feature, followed by cerebellar hypoplasia and other brain abnormalities, liver and skeletal defects. The phenotypic variability was in a large part associated with mutation types, genes and corresponding ciliary modules with hypomorphic variants in ciliary genes playing a role in early steps of ciliogenesis associated with juvenile-to-late onset NPH forms. Thus, our data confirm a considerable proportion of late-onset NPH suggesting an underdiagnosis in adult chronic kidney disease.
Published: 2023
Full Text: View/download PDF

10. Additional file 2 of A food bank program to help food pantries improve healthy food choices: mixed methods evaluation of The Greater Boston Food Bank’s Healthy Pantry Program

Author: Jia, Jenny, Burgun, Rachel, Reilly, Alexa, Sonnenblick, Ross, Fiechtner, Lauren, Zack, Rachel M., Porneala, Bianca, and Thorndike, Anne N.
Abstract: Additional file 2. Interview questionnaire administered to food pantry staff who participated in Healthy Pantry Program training. Document of interview questionnaire used for pantry staff interviews with CFIR domains.
Published: 2023
Full Text: View/download PDF

11. Additional file 1 of A food bank program to help food pantries improve healthy food choices: mixed methods evaluation of The Greater Boston Food Bank’s Healthy Pantry Program

Author: Jia, Jenny, Burgun, Rachel, Reilly, Alexa, Sonnenblick, Ross, Fiechtner, Lauren, Zack, Rachel M., Porneala, Bianca, and Thorndike, Anne N.
Abstract: Additional file 1. Nutrition thresholds of green, yellow, red categories for various food groups in the 2019 Supporting Wellness at Pantries (SWAP) traffic-light nutrition ranking system. A graphic containing thresholds of saturated fat, sodium, and sugar content by food group to categorize foods into green (choose often), yellow (choose sometimes), or red (choose rarely) in the Supporting Wellness at Pantries system.
Published: 2023
Full Text: View/download PDF

12. Simultaneous Mid-Temperature Pyrolysis and Plasma Technology for the Fabrication of Zeolitic Imidazolate Framework Zif‐67 Used in Fischer–Tropsch Synthesis

Author: sogand aghamohammadi, Mohsen Mohseni, Sajjad Abbasi, Sahar Rezaei, Alireza Hosseinzadeh, alper sarioglan, Gamze Gumuslu Gur, Serife Sarioglan, Yasemin Fatih, Utku Burgun, Yavuz Aydemir, and Mohammad Haghighi
Published: 2023
Full Text: View/download PDF

13. Additional file 3 of A food bank program to help food pantries improve healthy food choices: mixed methods evaluation of The Greater Boston Food Bank’s Healthy Pantry Program

Author: Jia, Jenny, Burgun, Rachel, Reilly, Alexa, Sonnenblick, Ross, Fiechtner, Lauren, Zack, Rachel M., Porneala, Bianca, and Thorndike, Anne N.
Abstract: Additional file 3. Demographic and work characteristics of pantry staff interviewees who had participated in Healthy Pantry Program training. A table of descriptive data on the pantry staff who were interviewed for this study.
Published: 2023
Full Text: View/download PDF

14. Le blasphème, entre droits sacrés et droits civils : place de l’intime et déplacement du sujet

Author: Cédric Burgun
Subjects: Psychiatry and Mental health, Clinical Psychology
Published: 2021
Full Text: View/download PDF

15. External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals

Author: Lombardi, Yannis, Azoyan, Loris, Szychowiak, Piotr, Bellamine, Ali, Lemaitre, Guillaume, Bernaux, Mélodie, Daniel, Christel, Leblanc, Judith, Riller, Quentin, Steichen, Olivier, Ancel, Pierre-Yves, Bauchet, Alain, Beeker, Nathanael, Benoit, Vincent, Bey, Romain, Bourmaud, Aurélie, Bréant, Stéphane, Burgun, Anita, Carrat, Fabrice, Caucheteux, Charlotte, Champ, Julien, Cormont, Sylvie, Dubiel, Julien, Duclos, Catherine, Esteve, Loic, Frank, Marie, Garcelon, Nicolas, Gramfort, Alexandre, Griffon, Nicolas, Grisel, Olivier, Guilbaud, Martin, Hassen-Khodja, Claire, Hemery, François, Hilka, Martin, Jannot, Anne Sophie, Lambert, Jerome, Layese, Richard, Lebouter, Léo, Leprovost, Damien, Lerner, Ivan, Sallah, Kankoe Levi, Maire, Aurélien, Mamzer, Marie-France, Martel, Patricia, Mensch, Arthur, Moreau, Thomas, Neuraz, Antoine, Orlova, Nina, Paris, Nicolas, Rance, Bastien, Ravera, Hélène, Rozes, Antoine, Rufat, Pierre, Salamanca, Elisa, Sandrin, Arnaud, Serre, Patricia, Tannier, Xavier, Treluyer, Jean-Marc, van Gysel, Damien, Varoquaux, Gael, Vie, Jill-Jênn, Wack, Maxime, Wajsburt, Perceval, Wassermann, Demian, Zapletal, Eric, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CHU Trousseau [Tours], Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Méthodes computationnelles et mathématiques pour comprendre la société et la santé à partir de données (SODA), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Centre d'Immunologie et de Maladies Infectieuses (CIMI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Direction générale déléguée à l'innovation (DGD-I), Institut National de Recherche en Informatique et en Automatique (Inria), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de Référence Maladies auto-immunes Systémiques Rares [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), CHU Tenon [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Sorbonne Université (SU), Inria Siège, Service de Département de médecine interne et immunologie clinique [CHU Pitié-Salpêtrière] (DMIIC), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), and Gestionnaire, HAL Sorbonne Université 5
Subjects: Adult, Paris, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), Original, [SDV]Life Sciences [q-bio], 030204 cardiovascular system & hematology, Critical Care and Intensive Care Medicine, law.invention, Cohort Studies, Hospitals, University, 03 medical and health sciences, 0302 clinical medicine, law, Anesthesiology, medicine, Humans, 030212 general & internal medicine, Mortality, Retrospective Studies, [SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Receiver operating characteristic, SARS-CoV-2, business.industry, External validation, COVID-19, Prognosis, University hospital, Intensive care unit, 3. Good health, Intensive Care Units, Editorial, SARS-CoV2, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Emergency medicine, Cohort, business, Cohort study
Abstract: Purpose The Coronavirus disease 2019 (COVID-19) has led to an unparalleled influx of patients. Prognostic scores could help optimizing healthcare delivery, but most of them have not been comprehensively validated. We aim to externally validate existing prognostic scores for COVID-19. Methods We used “COVID-19 Evidence Alerts” (McMaster University) to retrieve high-quality prognostic scores predicting death or intensive care unit (ICU) transfer from routinely collected data. We studied their accuracy in a retrospective multicenter cohort of adult patients hospitalized for COVID-19 from January 2020 to April 2021 in the Greater Paris University Hospitals. Areas under the receiver operating characteristic curves (AUC) were computed for the prediction of the original outcome, 30-day in-hospital mortality and the composite of 30-day in-hospital mortality or ICU transfer. Results We included 14,343 consecutive patients, 2583 (18%) died and 5067 (35%) died or were transferred to the ICU. We examined 274 studies and found 32 scores meeting the inclusion criteria: 19 had a significantly lower AUC in our cohort than in previously published validation studies for the original outcome; 25 performed better to predict in-hospital mortality than the composite of in-hospital mortality or ICU transfer; 7 had an AUC > 0.75 to predict in-hospital mortality; 2 had an AUC > 0.70 to predict the composite outcome. Conclusion Seven prognostic scores were fairly accurate to predict death in hospitalized COVID-19 patients. The 4C Mortality Score and the ABCS stand out because they performed as well in our cohort and their initial validation cohort, during the first epidemic wave and subsequent waves, and in younger and older patients. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-021-06524-w.
Published: 2021
Full Text: View/download PDF

16. Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study

Author: Byorn W.L. Tan, Bryce W.Q. Tan, Amelia L.M. Tan, Emily R. Schriver, Alba Gutiérrez-Sacristán, Priyam Das, William Yuan, Meghan R. Hutch, Noelia García Barrio, Miguel Pedrera Jimenez, Noor Abu-el-rub, Michele Morris, Bertrand Moal, Guillaume Verdy, Kelly Cho, Yuk-Lam Ho, Lav P. Patel, Arianna Dagliati, Antoine Neuraz, Jeffrey G. Klann, Andrew M. South, Shyam Visweswaran, David A. Hanauer, Sarah E. Maidlow, Mei Liu, Danielle L. Mowery, Ashley Batugo, Adeline Makoudjou, Patric Tippmann, Daniela Zöller, Gabriel A. Brat, Yuan Luo, Paul Avillach, Riccardo Bellazzi, Luca Chiovato, Alberto Malovini, Valentina Tibollo, Malarkodi Jebathilagam Samayamuthu, Pablo Serrano Balazote, Zongqi Xia, Ne Hooi Will Loh, Lorenzo Chiudinelli, Clara-Lea Bonzel, Chuan Hong, Harrison G. Zhang, Griffin M. Weber, Isaac S. Kohane, Tianxi Cai, Gilbert S. Omenn, John H. Holmes, Kee Yuan Ngiam, James R. Aaron, Giuseppe Agapito, Adem Albayrak, Giuseppe Albi, Mario Alessiani, Anna Alloni, Danilo F. Amendola, François Angoulvant, Li L.L.J. Anthony, Bruce J. Aronow, Fatima Ashraf, Andrew Atz, Vidul Ayakulangara Panickan, Paula S. Azevedo, James Balshi, Brett K. Beaulieu-Jones, Brendin R. Beaulieu-Jones, Douglas S. Bell, Antonio Bellasi, Vincent Benoit, Michele Beraghi, José Luis Bernal-Sobrino, Mélodie Bernaux, Romain Bey, Surbhi Bhatnagar, Alvar Blanco-Martínez, Martin Boeker, John Booth, Silvano Bosari, Florence T. Bourgeois, Robert L. Bradford, Stéphane Bréant, Nicholas W. Brown, Raffaele Bruno, William A. Bryant, Mauro Bucalo, Emily Bucholz, Anita Burgun, Mario Cannataro, Aldo Carmona, Anna Maria Cattelan, Charlotte Caucheteux, Julien Champ, Jin Chen, Krista Y. Chen, James J. Cimino, Tiago K. Colicchio, Sylvie Cormont, Sébastien Cossin, Jean B. Craig, Juan Luis Cruz-Bermúdez, Jaime Cruz-Rojo, Mohamad Daniar, Christel Daniel, Batsal Devkota, Audrey Dionne, Rui Duan, Julien Dubiel, Scott L. DuVall, Loic Esteve, Hossein Estiri, Shirley Fan, Robert W. Follett, Thomas Ganslandt, Noelia García-Barrio, Lana X. Garmire, Nils Gehlenborg, Emily J. Getzen, Alon Geva, Tomás González González, Tobias Gradinger, Alexandre Gramfort, Romain Griffier, Nicolas Griffon, Olivier Grisel, Pietro H. Guzzi, Larry Han, Christian Haverkamp, Derek Y. Hazard, Bing He, Darren W. Henderson, Martin Hilka, Jacqueline P. Honerlaw, Kenneth M. Huling, Richard W. Issitt, Anne Sophie Jannot, Vianney Jouhet, Ramakanth Kavuluru, Mark S. Keller, Chris J. Kennedy, Kate F. Kernan, Daniel A. Key, Katie Kirchoff, Ian D. Krantz, Detlef Kraska, Ashok K. Krishnamurthy, Sehi L'Yi, Trang T. Le, Judith Leblanc, Guillaume Lemaitre, Leslie Lenert, Damien Leprovost, Molei Liu, Qi Long, Sara Lozano-Zahonero, Kristine E. Lynch, Sadiqa Mahmood, Simran Makwana, Kenneth D. Mandl, Chengsheng Mao, Anupama Maram, Monika Maripuri, Patricia Martel, Marcelo R. Martins, Jayson S. Marwaha, Aaron J. Masino, Maria Mazzitelli, Diego R. Mazzotti, Arthur Mensch, Marianna Milano, Marcos F. Minicucci, Taha Mohseni Ahooyi, Jason H. Moore, Cinta Moraleda, Jeffrey S. Morris, Karyn L. Moshal, Sajad Mousavi, Douglas A. Murad, Shawn N. Murphy, Thomas P. Naughton, Carlos Tadeu Breda Neto, Jane Newburger, Wanjiku F.M. Njoroge, James B. Norman, Jihad Obeid, Marina P. Okoshi, Karen L. Olson, Nina Orlova, Brian D. Ostasiewski, Nathan P. Palmer, Nicolas Paris, Miguel Pedrera-Jiménez, Ashley C. Pfaff, Emily R. Pfaff, Danielle Pillion, Sara Pizzimenti, Tanu Priya, Hans U. Prokosch, Robson A. Prudente, Andrea Prunotto, Víctor Quirós-González, Rachel B. Ramoni, Maryna Raskin, Siegbert Rieg, Gustavo Roig-Domínguez, Pablo Rojo, Paula Rubio-Mayo, Paolo Sacchi, Carlos Sáez, Elisa Salamanca, L. Nelson Sanchez-Pinto, Arnaud Sandrin, Nandhini Santhanam, Janaina C.C. Santos, Fernando J. Sanz Vidorreta, Maria Savino, Petra Schubert, Juergen Schuettler, Luigia Scudeller, Neil J. Sebire, Pablo Serrano-Balazote, Patricia Serre, Arnaud Serret-Larmande, Mohsin Shah, Zahra Shakeri Hossein Abad, Domenick Silvio, Piotr Sliz, Jiyeon Son, Charles Sonday, Francesca Sperotto, Anastasia Spiridou, Zachary H. Strasser, Suzana E. Tanni, Deanne M. Taylor, Ana I. Terriza-Torres, Emma M.S. Toh, Carlo Torti, Enrico M. Trecarichi, Andrew K. Vallejos, Gael Varoquaux, Margaret E. Vella, Jill-Jênn Vie, Michele Vitacca, Kavishwar B. Wagholikar, Lemuel R. Waitman, Xuan Wang, Demian Wassermann, Martin Wolkewitz, Scott Wong, Xin Xiong, Ye Ye, Nadir Yehya, Joany M. Zachariasse, Janet J. Zahner, Alberto Zambelli, Valentina Zuccaro, and Chiara Zucco
Subjects: General Medicine
Abstract: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking.A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021.Advanced age (HR 2.77, 95%CI 2.53-3.04, p 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI.COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery.Authors are supported by various funders, with full details stated in the acknowledgement section.
Published: 2022

17. Determining the Set of Items to Include in Breast Operative Reports, Using Clustering Algorithms on Retrospective Data Extracted from Clinical DataWarehouse

Author: Adrien Boukobza, Maxime Wack, Antoine Neuraz, Daniela Geromin, Cécile Badoual, Anne-Sophie Bats, Anita Burgun, Meriem Koual, Rosy Tsopra, Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), TSOPRA, Rosy, École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE)
Subjects: MESH: Data Warehousing, MESH: Humans, [SDV]Life Sciences [q-bio], MESH: Algorithms, MESH: Retrospective Studies, Breast Neoplasms, [INFO] Computer Science [cs], NLP, MESH: Cluster Analysis, Clustering, [SDV] Life Sciences [q-bio], Breast cancer, Data Warehousing, Machine learning, Cluster Analysis, Humans, [INFO]Computer Science [cs], Female, MESH: Female, MESH: Breast Neoplasms, Algorithms, Retrospective Studies
Abstract: International audience; Medical reports are key elements to guarantee the quality, and continuity of care but their quality remains an issue. Standardization and structuration of reports can increase their quality, but are usually based on expert opinions. Here, we hypothesize that a structured model of medical reports could be learnt using machine learning on retrospective medical reports extracted from clinical data warehouses (CDW). To investigate our hypothesis, we extracted breast cancer operative reports from our CDW. Each document was preprocessed and split into sentences. Clustering was performed using TFIDF, Paraphrase or Universal Sentence Encoder along with K-Means, DBSCAN, or Hierarchical clustering. The best couple was TFIDF/K-Means, providing a sentence coverage of 89 % on our dataset; and allowing to identify 7 main categories of items to include in breast cancer operative reports. These results are encouraging for a document preset creation task and should then be validated and implemented in real life.
Published: 2022
Full Text: View/download PDF

18. Low-income neighbourhood was a key determinant of severe COVID-19 incidence during the first wave of the epidemic in Paris

Author: Anne-Sophie, Jannot, Hector, Countouris, Alexis, Van Straaten, Anita, Burgun, Sandrine, Katsahian, Bastien, Rance, Eric, Zapletal, Département d’informatique médicale, de biostatistique et de santé publique [AP-HP Hôpital européen Georges-Pompidou], Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Université de Paris (UP), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), AP-HP/Universities/Inserm COVID-19 research collaboration: Pierre-Yves Ancel, Alain Bauchet, Vincent Benoit, Mélodie Bernaux, Ali Bellamine, Romain Bey, Aurélie Bourmaud, Stéphane Bréant, Fabrice Carrat, Julien Champ, Sylvie Cormont, Christel Daniel, Julien Dubiel, Catherine Ducloas, Loic Esteve, Marie Frank, Nicolas Garcelon, Alexandre Gramfort, Nicolas Griffon, Olivier Grisel, Martin Guilbaud, Claire Hassen-Khodja, François Hemery, Martin Hilka, Jerome Lambert, Richard Layese, Judith Leblanc, Léo Lebouter, Guillaume Lemaitre, Damien Leprovost, Ivan Lerner, Kankoe Levi Sallah, Aurélien Maire, Marie-France Mamzer, Patricia Martel, Arthur Mensch, Thomas Moreau, Antoine Neuraz, Nina Orlova, Nicolas Paris, Hélène Ravera, Antoine Rozes, Elisa Salamanca, Arnaud Sandrin, Patricia Serre, Xavier Tannier, Jean-Marc Treluyer, Damien Van Gysel, Gael Varoquaux, Jill Jen Vie, Maxime Wack, Perceval Wajsburt, Demian Wassermann, Eric Zapletal, Université Paris Cité (UPCité), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), and Rance, Bastien
Subjects: Paris, Adolescent, Epidemiology, Population, communicable diseases, deprivation, Decile, 03 medical and health sciences, 0302 clinical medicine, cohort studies, Humans, Medicine, 030212 general & internal medicine, Epidemics, education, Socioeconomic status, Neighbourhood (mathematics), 030304 developmental biology, 0303 health sciences, education.field_of_study, SARS-CoV-2, business.industry, Incidence, Incidence (epidemiology), 1. No poverty, Public Health, Environmental and Occupational Health, COVID-19, Middle Aged, Census, healthcare disparities, 3. Good health, Socioeconomic Factors, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Female, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Residence, business, Demography, Cohort study
Abstract: BackgroundPrevious studies have demonstrated that socioeconomic factors are associated with COVID-19 incidence. In this study, we analysed a broad range of socioeconomic indicators in relation to hospitalised cases in the Paris area.MethodsWe extracted 303 socioeconomic indicators from French census data for 855 residential units in Paris and assessed their association with COVID-19 hospitalisation risk.FindingsThe indicators most associated with hospitalisation risk were the third decile of population income (OR=9.10, 95% CI 4.98 to 18.39), followed by the primary residence rate (OR=5.87, 95% CI 3.46 to 10.61), rate of active workers in unskilled occupations (OR=5.04, 95% CI 3.03 to 8.85) and rate of women over 15 years old with no diploma (OR=5.04, 95% CI 3.03 to 8.85). Of note, population demographics were considerably less associated with hospitalisation risk. Among these indicators, the rate of women aged between 45 and 59 years (OR=2.17, 95% CI 1.40 to 3.44) exhibited the greatest level of association, whereas population density was not associated. Overall, 86% of COVID-19 hospitalised cases occurred within the 45% most deprived areas.InterpretationStudying a broad range of socioeconomic indicators using census data and hospitalisation data as a readily available and large resource can provide real-time indirect information on populations with a high incidence of COVID-19.
Published: 2021
Full Text: View/download PDF

19. CO3.2 - Communauté interactive et participative en ligne, menée avec les pairs, comme outil de promotion de la santé sexuelle et reproductive des adolescents et jeunes adultes: la recherche-action Sexpairs

Author: P. Martin, C. Alberti, A. Bourmaud, A. Burgun, and E. De la Rochebrochard
Subjects: Epidemiology, Public Health, Environmental and Occupational Health
Published: 2023
Full Text: View/download PDF

20. Des critères de communion à la lumière d’Amoris laetitia

Author: Cédric Burgun
Abstract: Le debat sur l’acces a la communion eucharistique pour les couples « irreguliers » s’est trouve relance avec le chapitre 8 d’Amoris laetitia. La question est finalement de determiner des criteres concrets de discernement pour ouvrir a la communion. L’article propose 5 criteres objectifs et subjectifs, qui s’appuient sur la mediation ecclesiale et la conviction que le mariage est une authentique histoire de salut.
Published: 2021
Full Text: View/download PDF

21. Dexamethasone use and mortality in hospitalized patients with coronavirus disease 2019: A multicentre retrospective observational study

Author: Alexandre Gramfort, Marina Sánchez-Rico, Ali Bellamine, Nicolas Hoertel, Christel Daniel, Nicolas Paris, Mélodie Bernaux, Guillaume Lemaitre, Nathanaël Beeker, Raphaël Vernet, Antoine Neuraz, Jesús M. Alvarado, Frédéric Limosin, Elisa Salamanca, Anita Burgun, Neuraz, Antoine, Hôpital Corentin Celton [Issy-les-Moulineaux], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Institut de psychiatrie et neurosciences de Paris (IPNP - U1266 Inserm), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Universidad Complutense de Madrid = Complutense University of Madrid [Madrid] (UCM), Hôpital Européen Georges Pompidou [APHP] (HEGP), Université Paris Cité (UPCité), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), CHU Necker - Enfants Malades [AP-HP], Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CEA- Saclay (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris-Saclay, and École Pratique des Hautes Études (EPHE)
Subjects: Adult, medicine.medical_specialty, Efficacy, medicine.medical_treatment, 030226 pharmacology & pharmacy, SARS‐CoV‐2, Dexamethasone, 03 medical and health sciences, 0302 clinical medicine, COVID‐19, Internal medicine, medicine, Clinical endpoint, Humans, Pharmacology (medical), 030212 general & internal medicine, Mortality, Retrospective Studies, Pharmacology, Mechanical ventilation, SARS-CoV-2, Cumulative dose, business.industry, Proportional hazards model, Hazard ratio, COVID-19, Retrospective cohort study, Original Articles, medicine.disease, Comorbidity, Ventilation, COVID-19 Drug Treatment, Hospitalization, Oxygen, Treatment, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Original Article, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Coronavirus Infections, business, medicine.drug
Abstract: International audience; Background Health care professionals are caught between the wish of patients to speed up health-related communication via emails and the need for protecting health information. Objective We aimed to analyze the demographic characteristics of patients providing an email, and study the distribution of emails’ domain names. Methods We used the information system of the European Hospital Georges Pompidou (HEGP) to identify patients who provided an email address. We used a 1:1 matching strategy to study the demographic characteristics of the patients associated with the presence of an email, and described the characteristics of the emails used (in terms of types of emails—free, business, and personal). Results Overall, 4.22% (41,004/971,822) of the total population of patients provided an email address. The year of last contact with the patient is the strongest driver of the presence of an email address (odds ratio [OR] 20.8, 95% CI 18.9-22.9). Patients more likely to provide an email address were treated for chronic conditions and were more likely born between 1950 and 1969 (taking patients born before 1950 as reference [OR 1.60, 95% CI 1.54-1.67], and compared to those born after 1990 [OR 0.56, 95% CI 0.53-0.59]). Of the 41,004 email addresses collected, 37,779 were associated with known email providers, 31,005 email addresses were associated with Google, Microsoft, Orange, and Yahoo!, 2878 with business emails addresses, and 347 email addresses with personalized domain names. Conclusions Emails have been collected only recently in our institution. The importance of the year of last contact probably reflects this recent change in contact information collection policy. The demographic characteristics and especially the age distribution are likely the result of a population bias in the hospital: patients providing email are more likely to be treated for chronic diseases. A risk analysis of the use of email revealed several situations that could constitute a breach of privacy that is both likely and with major consequences. Patients treated for chronic diseases are more likely to provide an email address, and are also more at risk in case of privacy breach. Several common situations could expose their private information. We recommend a very restrictive use of the emails for health communication.
Published: 2021
Full Text: View/download PDF

22. Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications

Author: Christina Khnaisser, Luc Lavoie, Benoit Fraikin, Adrien Barton, Samuel Dussault, Anita Burgun, Jean-François Ethier, Université de Sherbrooke (UdeS), Centre National de la Recherche Scientifique (CNRS), MEthodes et ingénierie des Langues, des Ontologies et du DIscours (IRIT-MELODI), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse Capitole (UT Capitole), Université Fédérale Toulouse Midi-Pyrénées, UdeS - Université de Sherbrooke, INSERM UMRS 1138 Team 22, Université de Paris, Fonds de recherche du Québec - Nature et technologie, Health Data Research Network Canada, Unité de Soutien SRAP du Québec, Centre de recherche interdisciplinaire en informatique de la santé de l’Université de Sherbrooke, and Barton, Adrien
Subjects: Advanced and Specialized Nursing, Health Information Management, Health Information Interoperability, Data and Metadata Management, Ontologies, Data integration, [INFO]Computer Science [cs], Health Informatics, Heterogeneous Databases, [INFO] Computer Science [cs]
Abstract: Background A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible. A recent integration approach using knowledge models has been developed with ontologies that provide a strong semantic foundation. Nonetheless, deriving a data model that captures the richness of the ontology to store data with their full semantic remains a challenging task. Objectives This article addresses the following question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and their semantic to support various applications? Method This article describes a method using an ontological knowledge model to automatically generate a data model for a domain of interest. The model can then be implemented in a relational database which efficiently enables the collection, storage, and retrieval of data while keeping semantic ontological annotations so that the same data can be extracted for various applications for further processing. Results This article (1) presents a comparison of existing methods for generating a relational data model from an ontology using 23 criteria, (2) describes standard conversion rules, and (3) presents O n t o R e l a , a prototype developed to demonstrate the conversion rules. Conclusion This work is a first step toward automating and refining the generation of sharable and interoperable relational data models using ontologies with a freely available tool. The remaining challenges to cover all the ontology richness in the relational model are pointed out.
Published: 2022
Full Text: View/download PDF

23. Design of an Ontology-Based Triage System for Patients with Chronic Pain

Author: Alexandre, Saadi, Alice, Rogier, Anita, Burgun, and Rosy, Tsopra
Subjects: Humans, Systematized Nomenclature of Medicine, Chronic Pain, Triage, Referral and Consultation
Abstract: Waiting time for a consultation for chronic pain is a widespread health problem. This paper presents the design of an ontology use to assess patients referred to a consultation for chronic pain.We designed OntoDol, an ontology of pain domain for patient triage based on priority degrees. Terms were extracted from clinical practice guidelines and mapped to SNOMED-CT concepts through the Python module Owlready2. Selected SNOMED-CT concepts, relationships, and the TIME ontology, were implemented in the ontology using Protégé. Decision rules were implemented with SWRL. We evaluated OntoDol on 5 virtual cases.OntoDol contains 762 classes, 92 object properties and 18 SWRL rules to assign patients to 4 categories of priority. OntoDol was able to assert every case and classify them in the right category of priority.Further works will extend OntoDol to other diseases and assess OntoDol with real world data from the hospital.
Published: 2022

24. Using Deep Learning to Improve Phenotyping from Clinical Reports

Author: Marc Vincent, Maxime Douillet, Ivan Lerner, Antoine Neuraz, Anita Burgun, Nicolas Garcelon, Neuraz, Antoine, Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), CHU Necker - Enfants Malades [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)
Subjects: Phenotype, Deep Learning, Databases, Factual, Data Warehousing, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Humans, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Child, Natural Language Processing
Abstract: International audience; With the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require frameworks with natural language processing capabilities in order to process them and extract information not found in other types of documents. In the following work we implement a data processing pipeline performing phenotyping, disambiguation, negation and subject prediction on such reports. We compare it to an existing solution routinely used in a children’s hospital with special focus on genetic diseases. We show that by replacing components based on rules and pattern matching with components leveraging deep learning models and fine-tuned word embeddings we obtain performance improvements of 7%, 10% and 27% in terms of F1 measure for each task. The solution we devised will help build more reliable decision support systems.
Published: 2022
Full Text: View/download PDF

25. Towards a Clinical Decision Support System for Helping Medical Students in Emergency Call Centers

Author: Edouard Michot, Jules Woo, Louis Mouline, Catherine Sinnappan, Adrien Boukobza, Florence Campeotto, Laurent Dupic, Anita Burgun, Benoît Vivien, Rosy Tsopra, GHU AP-HP Centre Université de Paris, CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Funding This work is part of an innovative program named 'Artificial Intelligence for Clinical Decision Support', dedicated to undergraduate medical students, at Université de Paris., École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), and TSOPRA, Rosy
Subjects: MESH: Pandemics, MESH: Humans, Students, Medical, Clinical Decision Support System, [SDV]Life Sciences [q-bio], [INFO] Computer Science [cs], Phone call triage, Decision Support Systems, Clinical, [SDV] Life Sciences [q-bio], MESH: Call Centers, MESH: Students, Medical, MESH: Child, MESH: Decision Support Systems, Clinical, Digital Health, Humans, [INFO]Computer Science [cs], Pediatric Emergency, Triage, Child, Call Centers, Pandemics, MESH: Triage
Abstract: International audience; In critical situations such as pandemic, medical students are often called to help in emergency call centers. However, they may encounter difficulties in phone triage because of a lack of medical skills. Here, we aim at developing a Clinical Decision Support System for helping medical students in phone call triage of pediatric patients. The system is based on the PAT (Pediatric Assessment Triangle) and local guidelines. It is composed of two interfaces. The first allows a quick assessment of severity signs, and the second provides recommendations and additional elements such as “elements to keep in mind” or “medical advice to give to patient”. The system was evaluated by 20 medical students, with two fictive clinical cases. 75% of them found the content useful and clear, and the navigation easy. 65% would feel more reassured to have this system in emergency call centers. Further works are planned to improve the system before implementation in real-life.
Published: 2022
Full Text: View/download PDF

26. Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome

Author: Carole Faviez, Marc Vincent, Nicolas Garcelon, Caroline Michot, Genevieve Baujat, Valerie Cormier-Daire, Sophie Saunier, Xiaoyi Chen, Anita Burgun, Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Laboratoire des Maladies Rénales Héréditaires = Laboratory of Hereditary Kidney Diseases (Equipe Inserm U1163 ), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), This work was supported by the French National Research Agency (ANR) under the C’IL-LICO project (ANR-17-RHUS-0002), approved by the French ethics and scientific committee for health research (CESREES) (#2201437), and as part of the 'Investissements d’avenir' (ANR-19-P3IA-0001) (PRAIRIE 3IA Institute)., Garcelon, Nicolas, and École pratique des hautes études (EPHE)
Subjects: MESH: Deep Learning, MESH: Rare Diseases, Ellis-Van Creveld Syndrome, [SDV]Life Sciences [q-bio], MESH: Algorithms, Deep phenotyping, Unified Medical Language System, [SDV] Life Sciences [q-bio], Named entity recognition, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Deep Learning, Rare Diseases, Electronic Health Records, Humans, MESH: Unified Medical Language System, Rare disease, MESH: Electronic Health Records, Algorithms
Abstract: International audience; The wide adoption of Electronic Health Records (EHR) in hospitals provides unique opportunities for high throughput phenotyping of patients. The phenotype extraction from narrative reports can be performed by using either dictionary-based or data-driven methods. We developed a hybrid pipeline using deep learning to enrich the UMLS Metathesaurus for automatic detection of phenotypes from EHRs. The pipeline was evaluated on a French database of patients with a rare disease characterized by skeletal abnormalities, Jeune syndrome. The results showed a 2.5-fold improvement regarding the number of detected skeletal abnormalities compared to the baseline extraction using the standard release of UMLS. Our method can help enrich the coverage of the UMLS and improve phenotyping, especially for languages other than English.
Published: 2022
Full Text: View/download PDF

27. viroCapt: A Bioinformatics Pipeline for Identifying Viral Insertion in Human Host Genome

Author: Maxime Wack, David Veyer, Camille Peneau, Sonia Lameiras, William Digan, Alain Nicolas, Jessica Zucman-Rossi, Sandrine Imbeaud, Anita Burgun, Hélène Péré, Bastien Rance, Zucman-Rossi, Jessica, Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Génomique fonctionnelle des tumeurs solides = Functional Genomics of Solid Tumors [CRC] (FunGeST), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), and Institut Curie [Paris]
Subjects: HPV, Genome, Human, Computational Biology, High-Throughput Nucleotide Sequencing, integration, [SDV.MHEP.HEG]Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology, bioinformatics, virus, [SDV.MHEP.HEG] Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology, AAV2, Viruses, HBV, Humans, cancer, Algorithms
Abstract: International audience; Introduction: The implication of viruses in human cancers, as well as the emergence of next generation sequencing has permitted to investigate further their role and pathophysiology in the development of this disease. One such mechanism is the integration of portions of viral genomes in the human genome, as well as the specific action of viral oncogenes.inding integration sites and preserved oncogenes is still relying on heavy manual intervention. Methods: We developed an analysis and interpretation pipeline to determine viral insertions. Using data from directed viral capture, the pipeline conducts a crude genotyping phase to select reference viral genomes, identifies chimeric reads, extracts the putative human sequences to locate in the human reference genome, scores and ranks candidate junctions, and exports tabular and visual results. Results: We leverage common bioinformatics tools (bowtie2, samtools, blat), and a dedicated filtering and ranking algorithm, implemented in R, to infer candidate junctions and insertions. Static results (tables, figures) are produced, as well as an interactive interpretation tool developed as a shiny web app. Discussion: We validated this pipeline against published results of HPV, HBV, and AAV2 insertions and show good information retrieval.
Published: 2022
Full Text: View/download PDF

28. Correction: Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS) (Preprint)

Author: Ivan Lerner, Arnaud Serret-Larmande, Bastien Rance, Nicolas Garcelon, Anita Burgun, Laurent Chouchana, and Antoine Neuraz
Subjects: Data_MISCELLANEOUS, ComputerSystemsOrganization_PROCESSORARCHITECTURES, Hardware_ARITHMETICANDLOGICSTRUCTURES, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries)
Abstract: UNSTRUCTURED Not applicable
Published: 2022
Full Text: View/download PDF

29. Obesity Doubles Mortality in Patients Hospitalized for Severe Acute Respiratory Syndrome Coronavirus 2 in Paris Hospitals, France: A Cohort Study on 5,795 Patients

Author: Czernichow, Sébastien, Beeker, Nathanael, Rives‐Lange, Claire, Guerot, Emmanuel, Diehl, Jean‐Luc, Katsahian, Sandrine, Hulot, Jean‐Sébastien, Poghosyan, Tigran, Carette, Claire, Jannot, Anne‐Sophie, Ancel, Pierre‐Yves, Bauchet, Alain, Benoit, Vincent, Bernaux, Mélodie, Bellamine, Ali, Bey, Romain, Bourmaud, Aurélie, Bréant, Stéphane, Burgun, Anita, Carrat, Fabrice, Caucheteux, Charlotte, Champ, Julien, Cormont, Sylvie, Daniel, Christel, Dubiel, Julien, Ducloas, Catherine, Esteve, Loic, Frank, Marie, Garcelon, Nicolas, Gramfort, Alexandre, Griffon, Nicolas, Grisel, Olivier, Guilbaud, Martin, Hassen‐Khodja, Claire, Hemery, François, Hilka, Martin, Jannot, Anne Sophie, Lambert, Jerome, Layese, Richard, Leblanc, Judith, Lebouter, Léo, Lemaitre, Guillaume, Leprovost, Damien, Lerner, Ivan, Levi Sallah, Kankoe, Maire, Aurélien, Mamzer, Marie‐France, Martel, Patricia, Mensch, Arthur, Moreau, Thomas, Neuraz, Antoine, Orlova, Nina, Paris, Nicolas, Rance, Bastien, Ravera, Hélène, Rozes, Antoine, Salamanca, Elisa, Sandrin, Arnaud, Serre, Patricia, Tannier, Xavier, Treluyer, Jean‐Marc, Van Gysel, Damien, Varoquaux, Gael, Vie, Jill Jen, Wack, Maxime, Wajsburt, Perceval, Wassermann, Demian, Zapletal, Eric, CIC - HEGP (CIC 1418), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Européen Georges Pompidou [APHP] (HEGP), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPC)
Subjects: Adult, Male, Paris, medicine.medical_specialty, Adolescent, Developing country, [SDV]Life Sciences [q-bio], Endocrinology, Diabetes and Metabolism, Medicine (miscellaneous), 030209 endocrinology & metabolism, Severity of Illness Index, Body Mass Index, Cohort Studies, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Endocrinology, Risk Factors, COVID‐19, Internal medicine, Severity of illness, Odds Ratio, Humans, Medicine, Hospital Mortality, Obesity, Prospective Studies, 030212 general & internal medicine, Letters to the Editor, Prospective cohort study, Letter to the Editor, ComputingMilieux_MISCELLANEOUS, Aged, Retrospective Studies, Nutrition and Dietetics, SARS-CoV-2, business.industry, Mortality rate, COVID-19, Retrospective cohort study, Odds ratio, Middle Aged, medicine.disease, Female, France, business, Body mass index, Cohort study
Abstract: Objective Preliminary data from different cohorts of small sample size or with short follow-up indicate poorer prognosis in people with obesity compared with other patients. This study aims to precisely describe the strength of association between obesity in patients hospitalized with coronavirus disease 2019 (COVID-19) and mortality and to clarify the risk according to usual cardiometabolic risk factors in a large cohort. Methods This is a prospective cohort study including 5,795 patients aged 18 to 79 years hospitalized from February 1 to April 30, 2020, in the Paris area, with confirmed infection by severe acute respiratory syndrome coronavirus 2. Adjusted regression models were used to estimate the odds ratios (ORs) and 95% CIs for the mortality rate at 30 days across BMI classes, without and with imputation for missing BMI values. Results Eight hundred ninety-one deaths had occurred at 30 days. Mortality was significantly raised in people with obesity, with the following ORs for BMI of 30 to 35 kg/m2 , 35 to 40 kg/m2 , and >40 kg/m2 : 1.89 (95% CI: 1.45-2.47), 2.79 (95% CI: 1.95-3.97), and 2.55 (95% CI: 1.62-3.95), respectively (18.5-25 kg/m2 was used as the reference class). This increase holds for all age classes. Conclusions Obesity doubles mortality in patients hospitalized with COVID-19.
Published: 2020
Full Text: View/download PDF

30. Electronic health records for the diagnosis of rare diseases

Author: Antoine Neuraz, Rémi Salomon, Anita Burgun, Nicolas Garcelon, Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Université de Paris (UP), Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Service de néphrologie pédiatrique [CHU Necker], CCSD, Accord Elsevier, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Université Paris Cité - UFR Médecine [Santé] (UPCité UFR Médecine), Université Paris Cité (UPCité), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)
Subjects: MESH: Rare Diseases, 0301 basic medicine, Decision support system, [INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO], Computer science, media_common.quotation_subject, 030232 urology & nephrology, MESH: Algorithms, Translational research, Reuse, Health records, Health informatics, Adaptability, Machine Learning, MESH: Natural Language Processing, 03 medical and health sciences, 0302 clinical medicine, pediatric nephrology, Electronic Health Records, Humans, MESH: Electronic Health Records, Repurposing, Natural Language Processing, media_common, education, MESH: Humans, MESH: Machine Learning, business.industry, rare diseases, electronic health record, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], artificial intelligence, Data science, Data warehouse, 3. Good health, 030104 developmental biology, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Nephrology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, Algorithms
Abstract: International audience; With the emergence of electronic health records, the reuse of clinical data offers new perspectives for the diagnosis and management of patients with rare diseases. However, there are many obstacles to the repurposing of clinical data. The development of decision support systems depends on the ability to recruit patients, extract and integrate the patients' data, mine and stratify these data, and integrate the decision support algorithm into patient care. This last step requires an adaptability of the electronic health records to integrate learning health system tools. In this literature review, we examine the research that provides solutions to unlock these barriers and accelerate translational research: structured electronic health records and free-text search engines to find patients, data warehouses and natural language processing to extract phenotypes, machine learning algorithms to classify patients, and similarity metrics to diagnose patients. Medical informatics is experiencing an impellent request to develop decision support systems, and this requires ethical considerations for clinicians and patients to ensure appropriate use of health data.
Published: 2020
Full Text: View/download PDF

31. Putting undergraduate medical students in AI-CDSS designers’ shoes: An innovative teaching method to develop digital health critical thinking

Author: Rosy Tsopra, Nathan Peiffer-Smadja, Caroline Charlier, Florence Campeotto, Cédric Lemogne, Philippe Ruszniewski, Benoît Vivien, and Anita Burgun
Subjects: Health Informatics
Published: 2023
Full Text: View/download PDF

32. Effect of sodium incorporation into Fe-Zn catalyst for Fischer- Tropsch synthesis to light olefins

Author: Yasemin Fatih, Utku Burgun, Alper Sarioglan, and Hüsnü Atakül
Subjects: History, Polymers and Plastics, Process Chemistry and Technology, Business and International Management, Physical and Theoretical Chemistry, Industrial and Manufacturing Engineering, Catalysis
Published: 2023
Full Text: View/download PDF

33. Management of patients with multiple myeloma in the era of COVID-19 pandemic: how hospital at home changes our medical practice

Author: Burgun S, Benedicte Mittaine-Marzac, Jeremie Zerbit, N. Laporte, L. Willems, Claire Breal, Benedicte Deau-Fischer, Marguerite Vignon, G. Fouquet, Franchi P, Ammar F, Ihaddadene H, Justine Decroocq, Arsene Zogo, and Bouscary D
Subjects: medicine.medical_specialty, 2019-20 coronavirus outbreak, Coronavirus disease 2019 (COVID-19), business.industry, Pain medicine, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Nursing research, Medical practice, medicine.disease, Oncology, Family medicine, Pandemic, medicine, business, Multiple myeloma
Published: 2021
Full Text: View/download PDF

34. Association of Antihypertensive Agents with the Risk of In-Hospital Death in Patients with Covid-19

Author: Elisa Salamanca, Nathanaël Beeker, Elisabeth Polard, Jean-Marc Treluyer, Bastien Rance, Anita Burgun, Nicolas Garcelon, Laurent Chouchana, Nicolas Paris, Antoine Neuraz, Département de Pharmacologie Clinique [CHU Cochin], Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Unité de recherche clinique / Centre d'investigation clinique [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Centre Régional de Pharmacovigilance [Rennes] (CRPV), CHU Pontchaillou [Rennes], Service d'informatique médicale et biostatistiques [CHU Necker], AP-HP/Universities/Inserm COVID-19 research collaboration, AP-HP Covid CDR Initiative, and 'Entrepôt de Données de Santé' AP-HP Consortium': Pierre-Yves Ancel, Alain Bauchet, Nathanaël Beeker, Vincent Benoit, Mélodie Bernaux, Ali Bellamine, Romain Bey, Aurélie Bourmaud, Stéphane Breant, Anita Burgun, Fabrice Carrat, Charlotte Caucheteux, Julien Champ, Sylvie Cormont, Christel Daniel, Julien Dubiel, Catherine Ducloas, Loic Esteve, Marie Frank, Nicolas Garcelon, Alexandre Gramfort, Nicolas Griffon, Olivier Grisel, Martin Guilbaud, Claire Hassen-Khodja, François Hemery, Martin Hilka, Anne Sophie Jannot, Jerome Lambert, Richard Layese, Judith Leblanc, Leo Lebouter, Guillaume Lemaitre, Damien Leprovost, Ivan Lerner, Kankoe Levi Sallah, Aurelien Maire, Marie-France Mamzer, Patricia Martel, Arthur Mensch, Thomas Moreau, Antoine Neuraz, Nina Orlova, Nicolas Paris, Bastien Rance, Helene Ravera, Antoine Rozes, Elisa Salamanca, Arnaud Sandrin, Patricia Serre, Xavier Tannier, Jean-Marc Treluyer, Damien Van Gysel, Gaël Varoquaux, Jill Jen Vie, Maxime Wack, Perceval Wajsburt, Demian Wassermann, Eric Zapletal, Neuraz, Antoine, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE)
Subjects: 0301 basic medicine, Male, medicine.medical_specialty, [INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO], Coronavirus disease 2019 (COVID-19), Hospitalized patients, Short Communication, Population, Adrenergic beta-Antagonists, Angiotensin-Converting Enzyme Inhibitors, 030204 cardiovascular system & hematology, Cohort Studies, 03 medical and health sciences, Angiotensin Receptor Antagonists, 0302 clinical medicine, Renin-angiotensin-aldosterone inhibitors, Internal medicine, Risk of mortality, medicine, Humans, Pharmacology (medical), In patient, Hospital Mortality, Drug safety, education, Antihypertensive Agents, Aged, Retrospective Studies, Pharmacology, In hospital death, education.field_of_study, SARS-CoV-2, business.industry, COVID-19, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], General Medicine, Calcium Channel Blockers, 3. Good health, 030104 developmental biology, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Hypertension, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Female, Angiotensin Receptor Blockers, Cardiology and Cardiovascular Medicine, business, Cohort study
Abstract: Purpose The role of angiotensin receptor blockers (ARB), angiotensin-converting enzyme inhibitors (ACEi), or other antihypertensive agents in the case of Covid-19 remains controversial. We aimed to investigate the association between antihypertensive agent exposure and in-hospital mortality in patients with Covid-19. Methods We performed a retrospective multicenter cohort study on patients hospitalized between February 1 and May 15, 2020. All patients had been followed up for at least 30 days. Results Of the 8078 hospitalized patients for Covid-19, 3686 (45.6%) had hypertension and were included in the study. In this population, the median age was 75.4 (IQR, 21.5) years and 57.1% were male. Overall in-hospital 30-day mortality was 23.1%. The main antihypertensive pharmacological classes used were calcium channel blockers (CCB) (n=1624, 44.1%), beta-blockers (n=1389, 37.7%), ARB (n=1154, 31.3%), and ACEi (n=998, 27.1%). The risk of mortality was lower in CCB (aOR, 0.83 [0.70–0.99]) and beta-blockers (aOR, 0.80 [0.67–0.95]) users and non-significant in ARB (aOR, 0.88 [0.72–1.06]) and ACEi (aOR, 0.83 [0.68–1.02]) users, compared to non-users. These results remain consistent for patients receiving CCB, beta-blocker, or ARB as monotherapies. Conclusion This large multicenter retrospective of Covid-19 patients with hypertension found a reduced mortality among CCB and beta-blockers users, suggesting a putative protective effect. Our findings did not show any association between the use of renin-angiotensin-aldosterone system inhibitors and the risk of in-hospital death. Although they need to be confirmed in further studies, these results support the continuation of antihypertensive agents in patients with Covid-19, in line with the current guidelines. Supplementary Information The online version contains supplementary material available at 10.1007/s10557-021-07155-5.
Published: 2021
Full Text: View/download PDF

35. Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS) (Preprint)

Author: Ivan Lerner, Arnaud Serret-Larmande, Bastien Rance, Nicolas Garcelon, Anita Burgun, Laurent Chouchana, and Antoine Neuraz
Abstract: BACKGROUND Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. OBJECTIVE With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. METHODS We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. RESULTS A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.1) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively. CONCLUSIONS Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.
Published: 2021
Full Text: View/download PDF

36. Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set

Author: Adrien Boukobza, Anita Burgun, Bertrand Roudier, Rosy Tsopra, TSOPRA, Rosy, Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Service d'informatique médicale, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), ESIEE Paris, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE)
Subjects: [SDV] Life Sciences [q-bio], Health Information Management, Natural language processing, [SDV]Life Sciences [q-bio], COVID-19, Deep learning, [INFO]Computer Science [cs], Health Informatics, Explainable artificial intelligence, [INFO] Computer Science [cs], Neural network, Decision support
Abstract: Background Public engagement is a key element for mitigating pandemics, and a good understanding of public opinion could help to encourage the successful adoption of public health measures by the population. In past years, deep learning has been increasingly applied to the analysis of text from social networks. However, most of the developed approaches can only capture topics or sentiments alone but not both together. Objective Here, we aimed to develop a new approach, based on deep neural networks, for simultaneously capturing public topics and sentiments and applied it to tweets sent just after the announcement of the COVID-19 pandemic by the World Health Organization (WHO). Methods A total of 1,386,496 tweets were collected, preprocessed, and split with a ratio of 80:20 into training and validation sets, respectively. We combined lexicons and convolutional neural networks to improve sentiment prediction. The trained model achieved an overall accuracy of 81% and a precision of 82% and was able to capture simultaneously the weighted words associated with a predicted sentiment intensity score. These outputs were then visualized via an interactive and customizable web interface based on a word cloud representation. Using word cloud analysis, we captured the main topics for extreme positive and negative sentiment intensity scores. Results In reaction to the announcement of the pandemic by the WHO, 6 negative and 5 positive topics were discussed on Twitter. Twitter users seemed to be worried about the international situation, economic consequences, and medical situation. Conversely, they seemed to be satisfied with the commitment of medical and social workers and with the collaboration between people. Conclusions We propose a new method based on deep neural networks for simultaneously extracting public topics and sentiments from tweets. This method could be helpful for monitoring public opinion during crises such as pandemics.
Published: 2021
Full Text: View/download PDF

37. Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set (Preprint)

Author: Adrien Boukobza, Anita Burgun, Bertrand Roudier, and Rosy Tsopra
Abstract: BACKGROUND Public engagement is a key element for mitigating pandemics, and a good understanding of public opinion could help to encourage the successful adoption of public health measures by the population. In past years, deep learning has been increasingly applied to the analysis of text from social networks. However, most of the developed approaches can only capture topics or sentiments alone but not both together. OBJECTIVE Here, we aimed to develop a new approach, based on deep neural networks, for simultaneously capturing public topics and sentiments and applied it to tweets sent just after the announcement of the COVID-19 pandemic by the World Health Organization (WHO). METHODS A total of 1,386,496 tweets were collected, preprocessed, and split with a ratio of 80:20 into training and validation sets, respectively. We combined lexicons and convolutional neural networks to improve sentiment prediction. The trained model achieved an overall accuracy of 81% and a precision of 82% and was able to capture simultaneously the weighted words associated with a predicted sentiment intensity score. These outputs were then visualized via an interactive and customizable web interface based on a word cloud representation. Using word cloud analysis, we captured the main topics for extreme positive and negative sentiment intensity scores. RESULTS In reaction to the announcement of the pandemic by the WHO, 6 negative and 5 positive topics were discussed on Twitter. Twitter users seemed to be worried about the international situation, economic consequences, and medical situation. Conversely, they seemed to be satisfied with the commitment of medical and social workers and with the collaboration between people. CONCLUSIONS We propose a new method based on deep neural networks for simultaneously extracting public topics and sentiments from tweets. This method could be helpful for monitoring public opinion during crises such as pandemics.
Published: 2021
Full Text: View/download PDF

38. Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19

Author: Le, Trang, Gutiérrez-Sacristán, Alba, Son, Jiyeon, Hong, Chuan, South, Andrew, Beaulieu-Jones, Brett, Loh, Ne Hooi Will, Luo, Yuan, Morris, Michele, Ngiam, Kee Yuan, Patel, Lav, Samayamuthu, Malarkodi, Schriver, Emily, Tan, Amelia, Moore, Jason, Cai, Tianxi, Omenn, Gilbert, Avillach, Paul, Kohane, Isaac, Visweswaran, Shyam, Mowery, Danielle, Xia, Zongqi, Aaron, James, Agapito, Giuseppe, Albayrak, Adem, Alessiani, Mario, Amendola, Danilo, Angoulvant, François, Anthony, Li, Aronow, Bruce, Atz, Andrew, Balshi, James, Bell, Douglas, Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Blanco Martínez, Alvar, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence, Bradford, Robert, Brat, Gabriel, Bréant, Stéphane, Brown, Nicholas, Bryant, William, Bucalo, Mauro, Burgun, Anita, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Krista, Chen, Jin, Chiovato, Luca, Chiudinelli, Lorenzo, Cimino, James, Colicchio, Tiago, Cormont, Sylvie, Cossin, Sébastien, Craig, Jean, Cruz Bermúdez, Juan Luis, Cruz Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Davoudi, Anahita, Devkota, Batsal, Dubiel, Julien, Esteve, Loic, Fan, Shirley, Follett, Robert, Gaiolla, Paula, Ganslandt, Thomas, García Barrio, Noelia, Garmire, Lana, Gehlenborg, Nils, Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Hanauer, David, Haverkamp, Christian, He, Bing, Henderson, Darren, Hilka, Martin, Holmes, John, Horki, Petar, Huling, Kenneth, Hutch, Meghan, Issitt, Richard, Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Keller, Mark, Kirchoff, Katie, Klann, Jeffrey, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, L’yi, Sehi, Leblanc, Judith, Leite, Andressa, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Lozano-Zahonero, Sarah, Lynch, Kristine, Mahmood, Sadiqa, Maidlow, Sarah, Makoudjou Tchendjou, Adeline, Malovini, Alberto, Mandl, Kenneth, Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Masino, Aaron, Matheny, Michael, Maulhardt, Thomas, Mazzitelli, Maria, Mcduffie, Michael, Mensch, Arthur, Ashraf, Fatima, Milano, Marianna, Minicucci, Marcos, Moal, Bertrand, Moraleda, Cinta, Morris, Jeffrey, Moshal, Karyn, Mousavi, Sajad, Murad, Douglas, Murphy, Shawn, Naughton, Thomas, Neuraz, Antoine, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Orlova, Nina, Ostasiewski, Brian, Palmer, Nathan, Paris, Nicolas, Pedrera Jimenez, Miguel, Pfaff, Emily, Pillion, Danielle, Prokosch, Hans, Prudente, Robson, Quirós González, Víctor, Ramoni, Rachel, Raskin, Maryna, Rieg, Siegbert, Roig Domínguez, Gustavo, Rojo, Pablo, Sáez, Carlos, Salamanca, Elisa, Sandrin, Arnaud, Santos, Janaina, Savino, Maria, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil, Balazote, Pablo Serrano, Serre, Patricia, Serret-Larmande, Arnaud, Shakeri, Zahra, Silvio, Domenick, Sliz, Piotr, Sonday, Charles, Spiridou, Anastasia, Tan, Bryce, Tan, Byorn, Tanni, Suzana, Taylor, Deanne, Terriza-Torres, Ana, Tibollo, Valentina, Tippmann, Patric, Torti, Carlo, Trecarichi, Enrico, Tseng, Yi-Ju, Vallejos, Andrew, Varoquaux, Gael, Vella, Margaret, Vie, Jill-Jênn, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wassermann, Demian, Weber, Griffin, William, Yuan, Yehya, Nadir, Zambelli, Alberto, Zhang, Harrison, Zoeller, Daniela, Zucco, Chiara, Unité d'informatique médicale, CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), Université Paris Cité (UPCité), AS is funded by National Institutes of Health (NIH) National Heart Lung, and Blood Institute (NHLBI) K23HL148394 and L40HL148910, and NIH-National Center for Advancing Translational Sciences (NCATS) UL1TR001420. JM is funded by NIH-National Institute of Allergy and Infectious Disease (NIAD) AI11679. LP is funded by NCATS Clinical and Translational Science Award (CTSA) Number UL1TR002366. GO is funded by NIH National Institute of Environmental Health Sciences (NIEHS) P30ES017885 and National Cancer Institute (NCI) U24CA210967. SV is funded by NIH-National Library of Medicine (NLM) R01LM012095 and NCATS UL1TR001857. DM is funded by NCATS CTSA Number UL1-TR001878. ZX is funded by NIH National Institute of Neurological Disorders and Stroke (NINDS) R01NS098023., Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), National Cancer Institute, École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Université de Paris (UP), University of Pennsylvania Perelman School of Medicine, Harvard Medical School, University of Pittsburgh, Wake Forest School of Medicine, National University Health Systems, Northwestern University, University of Kansas Medical Center, University of Pennsylvania Health System, University of Michigan, University of Kentucky, University Magna Graecia of Catanzaro, INC., Lombardia Region Health System, Universidade Estadual Paulista (UNESP), Assistance Publique-Hôpitaux de Paris, Tan Tock Seng Hospital, University of Cincinnati, Medical University of South Carolina, St. Luke’s University Health Network, David Geffen School of Medicine at UCLA, ASST Papa Giovanni XXIII, University of Pavia, APHP Greater Paris University Hospital, ASST Pavia, Hospital Universitario, University of Freiburg, Informatics and Virtual Environments (DRIVE), IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano, University of North Carolina, BIOMERIS (BIOMedical Research Informatics Solutions), CEA, LIRMM, Boston Children’s Hospital, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, University of Alabama at Birmingham, Bordeaux University Hospital/ERIAS-Inserm U1219 BPH, Children’s Hospital of Philadelphia, Inria Centre de Paris, Heidelberg University, and Pain Medicine Boston Children’s Hospital, University of Michigan Medical School, MSHI Medical University of South Carolina, Massachusetts General Hospital, The Children’s Hospital of Philadelphia, University Hospital, Clevy.io, Harvard T.H. Chan School of Public Health, VA Salt Lake City Health Care System, Veterans Affairs Medical Center, PSL Université Paris, School of Biomedical Informatics, Great Ormond Street Hospital for Children, University of Erlangen-Nürnberg, Office of Research and Development, Universitat Politècnica de València, Nurse Department of FMB-Medicine School of Botucatu, FAU Erlangen-Nürnberg, National University Hospital, Chang Gung University, Medical College of Wisconsin, McGill University, Inria Lille, ICS S Maugeri IRCCS, University of Missouri, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC), Université Paris Cité - UFR Médecine Paris Centre [Santé] (UPC Médecine Paris Centre), and Université Paris Cité (UPC)
Subjects: Male, Epidemiology, Cross-sectional study, Disease, Severity of Illness Index, MESH: Aged, 80 and over, 0302 clinical medicine, MESH: Child, Prevalence, MESH: COVID-19, 030212 general & internal medicine, Young adult, Child, Aged, 80 and over, MESH: Aged, MESH: Middle Aged, Multidisciplinary, MESH: Infant, Newborn, Middle Aged, MESH: Infant, 3. Good health, Neurology, MESH: Young Adult, Child, Preschool, Medicine, Female, Encephalitis, Adult, MESH: Pandemics, medicine.medical_specialty, Adolescent, Science, Myelitis, MESH: Nervous System Diseases, Article, Young Adult, 03 medical and health sciences, Medical research, MESH: Cross-Sectional Studies, MESH: Severity of Illness Index, Internal medicine, Severity of illness, medicine, Humans, Pandemics, MESH: Prevalence, Aged, MESH: Adolescent, MESH: Humans, business.industry, MESH: Child, Preschool, Infant, Newborn, COVID-19, Infant, MESH: Adult, medicine.disease, MESH: Male, Confidence interval, Cross-Sectional Studies, Relative risk, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Nervous System Diseases, business, MESH: Female, Neurological disorders, 030217 neurology & neurosurgery
Abstract: Made available in DSpace on 2022-04-29T08:35:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-12-01 Division of Intramural Research, National Institute of Allergy and Infectious Diseases Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases National Institute of Allergy and Infectious Diseases National Center for Advancing Translational Sciences National Heart, Lung, and Blood Institute National Institute of Environmental Health Sciences U.S. National Library of Medicine National Institute of Neurological Disorders and Stroke Division of Cancer Prevention, National Cancer Institute Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Department of Biomedical Informatics Harvard Medical School Department of Neurology University of Pittsburgh, Biomedical Science Tower 3, Suite 7014, 3501 5th Avenue Department of Pediatrics Wake Forest School of Medicine Department of Critical Care National University Health Systems Department of Preventive Medicine Northwestern University Department of Biomedical Informatics University of Pittsburgh Department of Surgery National University Health Systems Department of Internal Medicine University of Kansas Medical Center Data Analytics Center University of Pennsylvania Health System Department of Computational Medicine and Bioinformatics University of Michigan Department of Biomedical Informatics University of Kentucky Department of Legal Economic and Social Sciences University Magna Graecia of Catanzaro Health Catalyst INC. Department of Surgery ASST Pavia Lombardia Region Health System Clinical Research Unit of Botucatu Medical School São Paulo State University Pediatric Emergency Department Hôpital Necker-Enfants Malades Assistance Publique-Hôpitaux de Paris National Center for Infectious Diseases Tan Tock Seng Hospital Departments of Biomedical Informatics Pediatrics Cincinnati Children’s Hospital Medical Center University of Cincinnati Department of Pediatrics Medical University of South Carolina Department of Surgery St. Luke’s University Health Network Department of Medicine David Geffen School of Medicine at UCLA UOC Ricerca Innovazione e Brand Reputation ASST Papa Giovanni XXIII Department of Electrical Computer and Biomedical Engineering University of Pavia IT Department Innovation & Data APHP Greater Paris University Hospital I.T. Department ASST Pavia Health Informatics Hospital Universitario, 12 de Octubre Strategy and Transformation Department APHP Greater Paris University Hospital Faculty of Medicine and Medical Center University of Freiburg Digital Research Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children Scientific Direction IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano North Carolina Translational and Clinical Sciences (NC TraCS) Institute University of North Carolina BIOMERIS (BIOMedical Research Informatics Solutions) Department of Biomedical Informatics HEGP APHP Greater Paris University Hospital Department of Medical and Surgical Sciences Data Analytics Research Center University Magna Graecia of Catanzaro Department of Anesthesia St. Luke’s University Health Network Université Paris-Saclay Inria CEA INRIA Sophia-Antipolis–ZENITH Team LIRMM Computational Health Informatics Program Boston Children’s Hospital Department of Internal Medicine University of Kentucky Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Department of Internal Medicine and Therapeutics University of Pavia Informatics Institute University of Alabama at Birmingham IAM Unit Bordeaux University Hospital/ERIAS-Inserm U1219 BPH Biomedical Informatics Center Medical University of South Carolina Clinical Research Informatics Boston Children’s Hospital Department of Biomedical and Health Informatics Children’s Hospital of Philadelphia SED/SIERRA Inria Centre de Paris Health Information Technology & Services University of Michigan Internal Medicine Department Botucatu Medical School São Paulo State University Heinrich-Lanz-Center for Digital Health University Medicine Mannheim Heidelberg University Department of Anesthesiology Critical Care and Pain Medicine Boston Children’s Hospital Department of Learning Health Sciences University of Michigan Medical School MSHI Medical University of South Carolina Department of Medicine Massachusetts General Hospital Division of Human Genetics Department of Pediatrics The Children’s Hospital of Philadelphia Center for Medical Information and Communication Technology University Hospital Renaissance Computing Institute/Department of Computer Science University of North Carolina Clinical Research Unit Saint Antoine Hospital APHP Greater Paris University Hospital Clevy.io Department of Biostatistics Harvard T.H. Chan School of Public Health VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System MICHR Informatics University of Michigan Laboratory of Informatics and Systems Engineering for Clinical Research Istituti Clinici Scientifici Maugeri SpA SB IRCCS Harvard Catalyst Harvard Medical School Clinical Research Unit Paris Saclay APHP Greater Paris University Hospital Department of Anesthesiology and Critical Care Children’s Hospital of Philadelphia VA Informatics and Computing Infrastructure Tennessee Valley Healthcare System Veterans Affairs Medical Center École Normale Supérieure PSL Université Paris BIG-ARC The University of Texas Health Science Center at Houston School of Biomedical Informatics Pediatric Infectious Disease Department Hospital Universitario, 12 de Octubre Department of Infectious Diseases Great Ormond Street Hospital for Children Department of Neurology Massachusetts General Hospital Internal Medicine Department of Botucatu Medical School São Paulo State University Department of Pediatrics Boston Children’s Hospital Center for Biomedical Informatics Wake Forest School of Medicine Department of Medical Informatics University of Erlangen-Nürnberg Department of Veterans Affairs Office of Research and Development Biomedical Data Science Lab ITACA Institute Universitat Politècnica de València Nurse Department of FMB-Medicine School of Botucatu Management Engineering ASST Pavia Lombardia Region Health System Department of Anesthesiology University Hospital Erlangen FAU Erlangen-Nürnberg Critical Care Medicine Department of Medicine St. Luke’s University Health Network Department of Medicine National University Hospital Department of Information Management Chang Gung University Clinical & Translational Science Institute Medical College of Wisconsin Montréal Neurological Institute McGill University SequeL Inria Lille Respiratory Department ICS S Maugeri IRCCS Department of Health Management and Informatics University of Missouri Department of Oncology ASST Papa Giovanni XXIII Clinical Research Unit of Botucatu Medical School São Paulo State University Internal Medicine Department Botucatu Medical School São Paulo State University Internal Medicine Department of Botucatu Medical School São Paulo State University Division of Intramural Research, National Institute of Allergy and Infectious Diseases: AI11679 Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases: AI11679 National Institute of Allergy and Infectious Diseases: AI11679 National Center for Advancing Translational Sciences: CTSA Award #UL1TR001878 National Center for Advancing Translational Sciences: CTSA Award #UL1TR002366 National Heart, Lung, and Blood Institute: K23HL148394 National Institute of Environmental Health Sciences: P30ES017885 U.S. National Library of Medicine: R01LM012095 National Institute of Neurological Disorders and Stroke: R01NS098023 Division of Cancer Prevention, National Cancer Institute: U24CA210967 National Center for Advancing Translational Sciences: UL1TR001420 National Center for Advancing Translational Sciences: UL1TR001857
Published: 2021
Full Text: View/download PDF

39. Prevalence of Contraindications to Nirmatrelvir-Ritonavir Among Hospitalized Patients With COVID-19 at Risk for Progression to Severe Disease

Author: Nicolas Hoertel, David R. Boulware, Marina Sánchez-Rico, Anita Burgun, Frédéric Limosin, Université Paris Cité (UPCité), Institut de psychiatrie et neurosciences de Paris (IPNP - U1266 Inserm), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), University of Minnesota Medical School, University of Minnesota System, Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Service d'informatique médicale, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)
Subjects: Ritonavir, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Contraindications, Prevalence, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Humans, COVID-19, General Medicine, COVID-19 Drug Treatment, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; This cohort study examines the prevalence of contraindications to nirmatrelvir-ritonavir in patients hospitalized with COVID-19.
Published: 2022
Full Text: View/download PDF

40. Photographie clinique par smartphone en chirurgie plastique et protection des données personnelles : développement d’une plateforme sécurisée et application sur 979 patients

Author: J. Levy, J. Djian, Laurent Lantieri, C. Botter, Mikael Hivelin, Alexandre G. Lellouch, and A. Burgun
Subjects: 03 medical and health sciences, 0302 clinical medicine, Clinical photography, Image database, media_common.quotation_subject, Surgery, Art, 030230 surgery, Humanities, media_common
Abstract: Resume Objectifs La photographie clinique en chirurgie plastique et reconstructrice a connu une revolution numerique. Le stockage des donnees en ligne, les moyens d’analyse massive tels que les algorithmes de reconnaissance faciale posent le probleme de la protection des donnees personnelles. Nous evaluons l’interet d’une plateforme, en lien avec le dossier medical informatise, permettant d’indexer et de centraliser de maniere securisee et intelligente ce contenu photographique. Patients et methode Nous avons interroge 300 plasticiens francais sur leur usage des smartphones dans leur pratique quotidienne. Nous avons parallelement developpe une suite logicielle appelee Surgeon©, permettant de maniere securisee d’indexer, trier, envoyer a partir d’un smartphone, des photographies sur le serveur securise de notre hopital universitaire. Chaque sequence photographique etait qualifiee grâce a un formulaire dedie. Nous avons etudie prospectivement, entre le 1er mai 2017 et le 30 mars 2018, le nombre de patients photographies, le nombre de sequences et photographies prises, le nombre moyen de sequences par patient. Resultats Sur 86 plasticiens repondants, 81 ne pourraient se passer de leur smartphone. Les photographies etaient stockees dans leur smartphone (50 %) ou synchronisees vers des stockages en ligne (25,6 %). Ils etaient une majorite (80,2 %) a etre favorable a l’utilisation d’une application securisee dediee. Notre application a permis de photographier 979 patients, soit 2345 sequences et 8112 photographies, avec une moyenne de 2,28 sequences par patient. Conclusion L’ergonomie et la securite de cette plateforme sur site, qui ont autorise son implantation dans un service hospitalier, permettent d’envisager une diffusion large.
Published: 2019
Full Text: View/download PDF

41. Performance analysis of a novel building integrated low concentration photovoltaic skylight with seasonal solar control

Author: Daniel Valencia-Caballero, Ya-Brigitte Assoa, Werther Cambarau, Didier Therme, Asier Sanz, Françoise Burgun, Iván Flores-Abascal, and Eduardo Román-Medina
Subjects: Mechanics of Materials, Architecture, Building and Construction, Safety, Risk, Reliability and Quality, Civil and Structural Engineering
Published: 2022
Full Text: View/download PDF

42. A COVID-19 Decision Support System for Phone Call Triage, Designed by and for Medical Students

Author: Rosy Tsopra, Anita Burgun, Adrien Boukobza, Jean-Patrick Vrel, Samy Oulmane, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), TSOPRA, Rosy, École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE)
Subjects: MESH: Pandemics, Decision support system, medicine.medical_specialty, Students, Medical, Computer science, [SDV]Life Sciences [q-bio], Decision tree, Medical equipment, [INFO] Computer Science [cs], Clinical decision support system, Task (project management), MESH: Students, Medical, medicine, MESH: COVID-19, Humans, MESH: SARS-CoV-2, [INFO]Computer Science [cs], Pandemics, Medical Simulation, Medical education, MESH: Humans, Clinical Decision Support System, SARS-CoV-2, Medical simulation, COVID-19, Digital health, Triage, [SDV] Life Sciences [q-bio], Medical Education, Emergency, Digital Health, MESH: Triage
Abstract: International audience; During spring 2020, SARS-CoV-2 pandemic induced shortage of medical equipment, hospital capacity and staff. To tackle this issue, medical students have been strongly involved in early patient triage through medical phone call regulation. Here, we present an intelligent web-based decision support system for COVID-19 phone call regulation, developed by and for, medical students to help them during this difficult but crucial task. The system is divided into 5 tabs. The first tab displays administrative information, clinical data related to life-threatening emergency, and personalized recommendations for patient management. The second tab displays a PDF report summarizing the clinical situation; the third tab displays the guidelines used for establishing the recommendations, and the fourth tab displays the overall algorithm in the form of a decision tree. The fifth tab provided a short user guide. The system was assessed by 21 medical staff. More than 90% of them appreciated the navigation and the interface, and found the content relevant. 90,5% of them would like to use it during the medical regulation. In the future, we plan to use this system during simulation-based medical learning for the initial medical training of medical students.
Published: 2021
Full Text: View/download PDF

43. Fuzzy Matching for Symptom Detection in Tweets: Application to Covid-19 During the First Wave of the Pandemic in France

Author: Carole, Faviez, Pierre, Foulquié, Xiaoyi, Chen, Adel, Mebarki, Sophie, Quennelle, Nathalie, Texier, Sandrine, Katsahian, Stéphane, Schuck, and Anita, Burgun
Subjects: SARS-CoV-2, COVID-19, Humans, France, Pandemics, Social Media
Abstract: The exhaustive automatic detection of symptoms in social media posts is made difficult by the presence of colloquial expressions, misspellings and inflected forms of words. The detection of self-reported symptoms is of major importance for emergent diseases like the Covid-19. In this study, we aimed to (1) develop an algorithm based on fuzzy matching to detect symptoms in tweets, (2) establish a comprehensive list of Covid-19-related symptoms and (3) evaluate the fuzzy matching for Covid-19-related symptom detection in French tweets. The Covid-19-related symptom list was built based on the aggregation of different data sources. French Covid-19-related tweets were automatically extracted using a dedicated data broker during the first wave of the pandemic in France. The fuzzy matching parameters were finetuned using all symptoms from MedDRA and then evaluated on a subset of 5000 Covid-19-related tweets in French for the detection of symptoms from our Covid-19-related list. The fuzzy matching improved the detection by the addition of 42% more correct matches with an 81% precision.
Published: 2021

44. Additional file 1 of Reorganisation of GP surgeries during the COVID-19 outbreak: analysis of guidelines from 15 countries

Author: Tsopra, Rosy, Frappe, Paul, Streit, Sven, Neves, Ana Luisa, Persijn J. Honkoop, Espinosa-Gonzalez, Ana Belen, Geroğlu, Berk, Jahr, Tobias, Lingner, Heidrun, Nessler, Katarzyna, Pesolillo, Gabriella, Sivertsen, Øyvind Stople, Thulesius, Hans, Zoitanu, Raluca, Burgun, Anita, and Shérazade Kinouani
Abstract: Additional file 1. Materials sent to each GP-reviewer - Initial set of key recommendations collected from the World Health Organisation (WHO) [14], the Centers for Disease Control and prevention (CDC) [15] and based on health professional resources from health care facilities (GP: general practitioner).
Published: 2021
Full Text: View/download PDF

45. List of contributors

Author: Jeffery B. Alvarez, Jean-Emmanuel Bibault, Anita Burgun, Jinzheng Cai, Zhidong Cao, Ken Chang, Jonathan H. Chen, William C. Chen, Mildred Cho, Peter Jaeho Cho, Toby C. Cornish, Anthony Costa, Andre Dekker, Karen Drukker, Jessilyn Dunn, Okyaz Eminaga, Bradley J. Erickson, Laure Fournier, Sanjiv Sam Gambhir, Efstathios D. Gennatas, Maryellen L. Giger, Iva Halilaj, Adam P. Harrison, Bryan He, Julian C. Hong, Dakai Jin, Michael C. Jin, Arthur Jochems, Jayashree Kalpathy-Cramer, Daniel S. Kapp, Mehran Karimzadeh, William Karnes, Philippe Lambin, Curtis P. Langlotz, Joonsang Lee, Hui Li, Joseph C. Liao, Anthony L. Lin, Rebecca Y. Lin, Yun Liu, Le Lu, David Magnus, Chris McIntosh, Shun Miao, James K. Min, Daniel B. Neill, Eric Karl Oermann, David Ouyang, Lily Peng, Sonia Phene, Maarten G. Poirot, Jennifer L. Quon, Daniel Ranti, Arvind Rao, Ramesh Raskar, Christopher Rombaoa, Daniel L. Rubin, Jason Samarasena, Jayne Seekins, Karthik Seetharam, Emily Shearer, Adam Sibley, Karnika Singh, Praveer Singh, Margarita Sordo, Duminda Suraweera, Aly Al-Amyn Valliani, Yvonka van Wijk, Praneeth Vepakomma, Bo Wang, Ge Wang, Nicholas Wang, Yirui Wang, Elisa Warner, Mattea Welch, Kimberly Wong, Zhenqin Wu, Fuyong Xing, Lei Xing, Ke Yan, Pingkun Yan, Lu Yang, Kristen W. Yeom, Robin Zachariah, Daniel Zeng, Lin Zhang, Ling Zhang, Xuhong Zhang, Li Zhou, and James Zou
Published: 2021
Full Text: View/download PDF

46. Artificial intelligence in oncology

Author: Philippe Lambin, Jean-Emmanuel Bibault, Anita Burgun, Andre Dekker, and Laure Fournier
Subjects: Decision support system, Computer science, business.industry, Radiation oncology, Artificial intelligence, business, Imaging data, Cognitive load
Abstract: Medical decisions can rely on a very large number of parameters, but it is traditionally considered that our cognitive capacity can only integrate up to five factors in order to take a decision. Oncologists will need to combine vast amount of clinical, biological, and imaging data to achieve state-of-the-art treatments. Data science and artificial intelligence (AI) will have an important role in the generation of models to predict outcome and guide treatments. A new paradigm of data-driven decision-making, reusing routine health-care data to provide decision support is emerging. This chapter explores the studies published in imaging, medical and radiation oncology and explains the technical challenges that need to be addressed before AI can be routinely used to treat cancer patients.
Published: 2021
Full Text: View/download PDF

47. Additional file 2 of Reorganisation of GP surgeries during the COVID-19 outbreak: analysis of guidelines from 15 countries

Author: Tsopra, Rosy, Frappe, Paul, Streit, Sven, Neves, Ana Luisa, Persijn J. Honkoop, Espinosa-Gonzalez, Ana Belen, Geroğlu, Berk, Jahr, Tobias, Lingner, Heidrun, Nessler, Katarzyna, Pesolillo, Gabriella, Sivertsen, Øyvind Stople, Thulesius, Hans, Zoitanu, Raluca, Burgun, Anita, and Shérazade Kinouani
Subjects: Data_FILES
Abstract: Additional file 2. Guidelines used for this study.
Published: 2021
Full Text: View/download PDF

48. Correction: Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS)

Author: Ivan Lerner, Arnaud Serret-Larmande, Bastien Rance, Nicolas Garcelon, Anita Burgun, Laurent Chouchana, and Antoine Neuraz
Subjects: Health Information Management, Health Informatics
Published: 2022
Full Text: View/download PDF

49. Association of antihypertensive agents with the risk of in-hospital death in patients with Covid-19

Author: Nicolas Garcelon, Antoine Neuraz, Nathanaël Beeker, Elisa Salamanca, Jean-Marc Treluyer, Elisabeth Polard, Anita Burgun, Universities, Bastien Rance, Ap-Hp Covid Cdr Initiative, Nicolas Paris, and Laurent Chouchana
Subjects: In hospital death, 2019-20 coronavirus outbreak, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), business.industry, Hospitalized patients, Internal medicine, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Risk of mortality, Medicine, In patient, business, Cohort study
Abstract: In this retrospective multicenter cohort study, we aimed to investigate the association between antihypertensive agent exposure and in-hospital mortality in patients with Covid-19. Of 8,078 hospitalized patients for Covid-19, 3,686 (45.6%) had hypertension including 2043 (55.4%) patients exposed to a renin-angiotensin-aldosterone inhibitors (RAASi), 1624 (44.1%) to calcium channel blockers (CCB) and 1154 (37.7%) to beta-blockers. Overall in-hospital 30-day mortality was 23.1%. Compared to non-users, the risk of mortality was lower in CCB (aOR, 0.83 [0.70-0.99]) and beta-blockers users (aOR, 0.80 [0.67-0.95]), and not different in RAASi users. These findings support the continuation of antihypertensive agents in patients with Covid-19.
Published: 2020
Full Text: View/download PDF

50. COVID-19, a social disease in Paris: a socio-economic wide association study on hospitalized patients highlights low-income neighbourhood as a key determinant of severe COVID-19 incidence during the first wave of the epidemic

Author: Sandrine Katsahian, Bastien Rance, Anne-Sophie Jannot, Hector Countouris, and Anita Burgun
Subjects: education.field_of_study, business.industry, Incidence (epidemiology), Population, Disease, Overcrowding, Census, Decile, Medicine, business, education, Association (psychology), Neighbourhood (mathematics), Demography
Abstract: BackgroundStudies have already shown that many environmental factors are associated with COVID-19 incidence. However, none have studied a very large set of socio-economic indicators and analysed to what extent these factors could highlight populations at high risk for COVID-19. We propose here a new approach, a socio-economic wide study, to pinpoint subgroups with a high incidence of COVID-19, and illustrated this approach using hospitalized cases in Paris area.MethodsWe extracted 303 socio-economic indicators from French census data for the 855 residential units in Paris and assessed their association with COVID-19 hospitalization risk. We then fitted a predictive model using a penalized regression on these indicators to predict the incidence of patient hospitalization for COVID-19 in Paris.FindingsThe most associated indicator was income, corresponding to the 3rd decile of the population (OR= 0.11, CI95% [0.05; 0.20]). A model including only income achieves a high performance in both the training set (AUC=0.78, CI95%: 0.72-0.85) and the test set (AUC=0.79 (CI95%: 0.71-0.87). Overall, the 45% most deprived areas gathered 86% of the areas with a high incidence of COVID-19 hospitalized cases.InterpretationDuring the first wave of the epidemic, income predicted Paris areas at risk for a high incidence of patients hospitalized for COVID-19 with a high performance. Socio-economic wide association studies, collecting passively data from hospitalized cases, therefore not necessitating any effort for health caregivers, is of particular interest in such a period of hospital overcrowding as it provides real-time indirect information on populations having high COVID-19 incidence.
Published: 2020
Full Text: View/download PDF

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Journal

Database

Publisher

357 results on '"Burgun, A."'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources