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1. Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI

Author

Simona Bottani, Elina Thibeau-Sutre, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos, and APPRIMAGE Study Group

Subjects
Brain MRI, Clinical data warehouse, Image translation, Medical technology, R855-855.5
Abstract

Abstract Background Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging software tools for feature extraction are typically applied only to images without gadolinium. The objective of this work is to evaluate how image translation can be useful to exploit a highly heterogeneous data set containing both contrast-enhanced and non-contrast-enhanced images from a clinical data warehouse. Methods We propose and compare different 3D U-Net and conditional GAN models to convert contrast-enhanced T1-weighted (T1ce) into non-contrast-enhanced (T1nce) brain MRI. These models were trained using 230 image pairs and tested on 77 image pairs from the clinical data warehouse of the Greater Paris area. Results Validation using standard image similarity measures demonstrated that the similarity between real and synthetic T1nce images was higher than between real T1nce and T1ce images for all the models compared. The best performing models were further validated on a segmentation task. We showed that tissue volumes extracted from synthetic T1nce images were closer to those of real T1nce images than volumes extracted from T1ce images. Conclusion We showed that deep learning models initially developed with research quality data could synthesize T1nce from T1ce images of clinical quality and that reliable features could be extracted from the synthetic images, thus demonstrating the ability of such methods to help exploit a data set coming from a clinical data warehouse.

Published
2024
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2. Risk of Death in Individuals Hospitalized for COVID-19 With and Without Psychiatric Disorders: An Observational Multicenter Study in France

Author

Nicolas Hoertel, Marina Sánchez-Rico, Pedro de la Muela, Miriam Abellán, Carlos Blanco, Marion Leboyer, Céline Cougoule, Erich Gulbins, Johannes Kornhuber, Alexander Carpinteiro, Katrin Anne Becker, Raphaël Vernet, Nathanaël Beeker, Antoine Neuraz, Jesús M. Alvarado, Juan José Herrera-Morueco, Guillaume Airagnes, Cédric Lemogne, Frédéric Limosin, Pierre-Yves Ancel, Alain Bauchet, 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, 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, and Eric Zapletal

Subjects
Antidepressants, Comorbidity, COVID-19, Mental disorders, Mood disorders, Mortality, Psychiatry, RC435-571
Abstract

Background: Prior research suggests that psychiatric disorders could be linked to increased mortality among patients with COVID-19. However, whether all or specific psychiatric disorders are intrinsic risk factors of death in COVID-19 or whether these associations reflect the greater prevalence of medical risk factors in people with psychiatric disorders has yet to be evaluated. Methods: We performed an observational, multicenter, retrospective cohort study to examine the association between psychiatric disorders and mortality among patients hospitalized for laboratory-confirmed COVID-19 at 36 Greater Paris University hospitals. Results: Of 15,168 adult patients, 857 (5.7%) had an ICD-10 diagnosis of psychiatric disorder. Over a mean follow-up period of 14.6 days (SD = 17.9), 326 of 857 (38.0%) patients with a diagnosis of psychiatric disorder died compared with 1276 of 14,311 (8.9%) patients without such a diagnosis (odds ratio 6.27, 95% CI 5.40–7.28, p < .01). When adjusting for age, sex, hospital, current smoking status, and medications according to compassionate use or as part of a clinical trial, this association remained significant (adjusted odds ratio 3.27, 95% CI 2.78–3.85, p < .01). However, additional adjustments for obesity and number of medical conditions resulted in a nonsignificant association (adjusted odds ratio 1.02, 95% CI 0.84–1.23, p = .86). Exploratory analyses after the same adjustments suggested that a diagnosis of mood disorders was significantly associated with reduced mortality, which might be explained by the use of antidepressants. Conclusions: These findings suggest that the increased risk of COVID-19–related mortality in individuals with psychiatric disorders hospitalized for COVID-19 might be explained by the greater number of medical conditions and the higher prevalence of obesity in this population and not by the underlying psychiatric disease.

Published
2023
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3. Performance of AI-Based Automated Classifications of Whole-Body FDG PET in Clinical Practice: The CLARITI Project

Author

Arnaud Berenbaum, Hervé Delingette, Aurélien Maire, Cécile Poret, Claire Hassen-Khodja, Stéphane Bréant, Christel Daniel, Patricia Martel, Lamiae Grimaldi, Marie Frank, Emmanuel Durand, and Florent L. Besson

Subjects
FDG PET, artificial intelligence, deep learning, convolutional neural network, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Purpose: To assess the feasibility of a three-dimensional deep convolutional neural network (3D-CNN) for the general triage of whole-body FDG PET in daily clinical practice. Methods: An institutional clinical data warehouse working environment was devoted to this PET imaging purpose. Dedicated request procedures and data processing workflows were specifically developed within this infrastructure and applied retrospectively to a monocentric dataset as a proof of concept. A custom-made 3D-CNN was first trained and tested on an “unambiguous” well-balanced data sample, which included strictly normal and highly pathological scans. For the training phase, 90% of the data sample was used (learning set: 80%; validation set: 20%, 5-fold cross validation) and the remaining 10% constituted the test set. Finally, the model was applied to a “real-life” test set which included any scans taken. Text mining of the PET reports systematically combined with visual rechecking by an experienced reader served as the standard-of-truth for PET labeling. Results: From 8125 scans, 4963 PETs had processable cross-matched medical reports. For the “unambiguous” dataset (1084 PETs), the 3D-CNN’s overall results for sensitivity, specificity, positive and negative predictive values and likelihood ratios were 84%, 98%, 98%, 85%, 42.0 and 0.16, respectively (F1 score of 90%). When applied to the “real-life” dataset (4963 PETs), the sensitivity, NPV, LR+, LR− and F1 score substantially decreased (61%, 40%, 2.97, 0.49 and 73%, respectively), whereas the specificity and PPV remained high (79% and 90%). Conclusion: An AI-based triage of whole-body FDG PET is promising. Further studies are needed to overcome the challenges presented by the imperfection of real-life PET data.

Published
2023
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12. Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data

Author

Sophie Loizillon, Simona Bottani, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos, Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), and Loizillon, Sophie

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Motion, Deep Learning, Clinical Data Warehouse, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [INFO]Computer Science [cs], [INFO] Computer Science [cs], MRI, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted by these artefacts and may be unusable. Since their manual detection is impossible due to the large number of scans, it is necessary to develop tools to automatically exclude (or at least identify) images with motion in order to fully exploit CDWs. In this paper, we propose a novel transfer learning method for the automatic detection of motion in 3D T1-weighted brain MRI. The method consists of two steps: a pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model to clinical data, relying on the labelling of 4045 images. The objectives were both (1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced accuracy>80 %). However, for the second objective, the performance was weaker and substantially lower than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical imaging and highlight the importance of a clinical validation of models trained on research data.

Published
2022

13. Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse

Author

Simona Bottani, Ninon Burgos, Aurélien Maire, Dario Saracino, Sebastian Ströer, Didier Dormont, Olivier Colliot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-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], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Service de Neuroradiologie [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), The research leading to these results has received funding from the Abeona Foundation (project Brain@Scale), from the French government under man-agement of Agence Nationale de la Recherche as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Bottani, Simona, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, and Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

A variety of algorithms have been proposed for computer-aided diagnosis of dementia from anatomical brain MRI. These approaches achieve high accuracy when applied to research data sets but their performance on real-life clinical routine data has not been evaluated yet. The aim of this work was to study the performance of such approaches on clinical routine data, based on a hospital data warehouse, and to compare the results to those obtained on a research data set. The clinical data set was extracted from the hospital data warehouse of the Greater Paris area, which includes 39 different hospitals. The research set was composed of data from the Alzheimer's Disease Neuroimaging Initiative data set. In the clinical set, the population of interest was identified by exploiting the diagnostic codes from the 10th revision of the International Classification of Diseases that are assigned to each patient. We studied how the imbalance of the training sets, in terms of contrast agent injection and image quality, may bias the results. We demonstrated that computer-aided diagnosis performance was strongly biased upwards (over 17 percent points of balanced accuracy) by the confounders of image quality and contrast agent injection, a phenomenon known as the Clever Hans effect or shortcut learning. When these biases were removed, the performance was very poor. In any case, the performance was considerably lower than on the research data set. Our study highlights that there are still considerable challenges for translating dementia computer-aided diagnosis systems to clinical routine.

Published
2022

14. Homogenization of brain MRI from a clinical data warehouse using contrast-enhanced to non-contrast-enhanced image translation with U-Net derived models

Author

Bottani Simona, Elina Thibeau-Sutre, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos, Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre for Medical Image Computing (CMIC), University College of London [London] (UCL), The research leading to these results has received funding from the Abeona Foundation (project Brain@Scale), from the French government under management of Agence Nationale de la Recherche as part of the 'Investisse-ments d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6)., APPRIMAGE, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), Bottani, Simona, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, and Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [INFO]Computer Science [cs], [INFO] Computer Science [cs], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Clinical data warehouses provide access to massive amounts of medical images and thus offer unprecedented opportunities for research. However, they also pose important challenges, a major challenge being their heterogeneity. In particular, they contain patients with numerous different diseases. The exploration of some neurological diseases with magnetic resonance imaging (MRI) requires injecting a gadolinium-based contrast agent (for instance to detect tumors or other contrast-enhancing lesions) while other diseases do not require such injection. Image harmonization is a key factor to enable unbiased differential diagnosis in such context. Additionally, classical neuroimaging software tools that extract features used as inputs of classification algorithms are typically applied only to images without gadolinium. The objective of this work is to homogenize images from a clinical data warehouse and enable the extraction of consistent features from brain MR images, no matter the initial presence or absence of gadolinium. We propose a deep learning approach based on a 3D U-Net to translate contrast-enhanced into non-contrast-enhanced T1-weighted brain MRI. The approach was trained/validated using 230 image pairs and tested on 26 image pairs of good quality and 51 image pairs of low quality from the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-Hôpitaux de Paris [AP-HP]). We tested two different 3D U-Net architectures and we chose the one reaching the best image similarity metrics for a further validation for a segmentation task. We tested two 3D U-Net architectures with the addition either of residual connections or of attention mechanisms. The U-Net with attention mechanisms reached the best image similarity metrics and was further validated on a segmentation task. We showed that features extracted from the synthetic images (gray matter, white matter and cerebrospinal fluid volumes) were closer to those obtained from the non-contrast-enhanced T1-weighted brain MRI (considered as reference) than the original, contrast-enhanced, images.

Published
2022
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15. Risk of death in individuals hospitalized for COVID-19 with and without psychiatric disorders: an observational multicenter study in France

Author

Nicolas Hoertel, Marina Sánchez-Rico, Pedro de la Muela, Miriam Abellán, Carlos Blanco, Marion Leboyer, Céline Cougoule, Erich Gulbins, Johannes Kornhuber, Alexander Carpinteiro, Katrin Anne Becker, Raphaël Vernet, Nathanaël Beeker, Antoine Neuraz, Jesús M. Alvarado, Juan José Herrera-Morueco, Guillaume Airagnes, Cédric Lemogne, Frédéric Limosin, Pierre-Yves Ancel, Alain Bauchet, 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, 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, DMU Psychiatrie et Addictologie [Hôpital Corentin Celton, Issy-les-Moulineaux], 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)-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), National Institute on Drug Abuse [Bethesda] (NIDA), DMU psychiatrie et addictologie [CHU Henri Mondor], 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), University of Duisburg-Essen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), University Hospital [Essen, Germany], 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), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CHU Necker - Enfants Malades [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), AP-HP / Université de Paris / INSERM COVID-19 research collaboration and AP-HP COVID CDR Initiative, 'Entrepôt de Données de Santé' AP-HP Consortium, Martinez Rico, Clara, IMRB - 'Neuropsychiatrie translationnelle' [Créteil] (U955 Inserm - UPEC), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Universitätsklinikum Essen [Universität Duisburg-Essen] (Uniklinik Essen), 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), 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
obesity, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, [SDV]Life Sciences [q-bio], COVID-19, risk of death, General Medicine, SARS-COV-2, mood disorders, mortality, mental disorders, Archival Report, comorbidity, psychiatric disorders, antidepressants, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

International audience; BackgroundPrior research suggests that psychiatric disorders could be linked to increased mortality among patients with COVID-19. However, whether all or specific psychiatric disorders are intrinsic risk factors of death in COVID-19 or whether these associations reflect the greater prevalence of medical risk factors in people with psychiatric disorders has yet to be evaluated.MethodsWe performed an observational, multicenter, retrospective cohort study to examine the association between psychiatric disorders and mortality among patients hospitalized for laboratory-confirmed COVID-19 at 36 Greater Paris University hospitals.ResultsOf 15,168 adult patients, 857 (5.7%) had an ICD-10 diagnosis of psychiatric disorder. Over a mean follow-up period of 14.6 days (SD = 17.9), 326 of 857 (38.0%) patients with a diagnosis of psychiatric disorder died compared with 1276 of 14,311 (8.9%) patients without such a diagnosis (odds ratio 6.27, 95% CI 5.40–7.28, p < .01). When adjusting for age, sex, hospital, current smoking status, and medications according to compassionate use or as part of a clinical trial, this association remained significant (adjusted odds ratio 3.27, 95% CI 2.78–3.85, p < .01). However, additional adjustments for obesity and number of medical conditions resulted in a nonsignificant association (adjusted odds ratio 1.02, 95% CI 0.84–1.23, p = .86). Exploratory analyses after the same adjustments suggested that a diagnosis of mood disorders was significantly associated with reduced mortality, which might be explained by the use of antidepressants.ConclusionsThese findings suggest that the increased risk of COVID-19–related mortality in individuals with psychiatric disorders hospitalized for COVID-19 might be explained by the greater number of medical conditions and the higher prevalence of obesity in this population and not by the underlying psychiatric disease.

Published
2022
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16. Fully automated opportunistic screening of vertebral fractures and osteoporosis on more than 150 000 routine computed tomography scans

Author

Antoine Rozes, Ronen Gordon, Antoine Feydy, Stéphane Bréant, Aurélien Maire, Karine Briot, Daniel Reizine, Sami Kolta, Namik Taright, David Hajage, J. Fechtenbaum, Christel Daniel, Florence Tubach, and Christian Roux

Subjects
Male, medicine.medical_specialty, Osteoporosis, Computed tomography, Lumbar vertebrae, Lumbar, Absorptiometry, Photon, Rheumatology, Bone Density, Hounsfield scale, Medicine, Humans, Pharmacology (medical), Opportunistic screening, Aged, Lumbar Vertebrae, medicine.diagnostic_test, Receiver operating characteristic, business.industry, medicine.disease, medicine.anatomical_structure, Fully automated, Spinal Fractures, Female, Radiology, business, Tomography, X-Ray Computed, Osteoporotic Fractures
Abstract

Objective Osteoporosis is underdiagnosed and undertreated, although severe complications of osteoporotic fractures, including vertebral fractures, are well known. This study sought to assess the feasibility and results of an opportunistic screening of vertebral fractures and osteoporosis in a large database of lumbar or abdominal CT scans. Material and methods Data were analysed from CT scans obtained in 35 hospitals from patients aged 60 years or older and stored in a Picture Archiving and Communication System in Assistance-Publique-Hôpitaux de Paris, from 2007 to 2013. Dedicated software was used to analyse the presence or absence of at least 1 vertebral fracture (VF), and the radiodensity of the lumbar vertebrae was measured Hounsfield Units (HUs). A simulated T-score was calculated. Results Data were analysed from 152 268 patients [mean age (S.D.) = 73.2 (9.07) years]. Success rates for VF assessment and HUs measurements were 82 and 87%, respectively. The prevalence of VFs was 24.5% and increased with age. Areas under the receiver operating characteristic curves for the detection of VFs were 0.61 and 0.62 for the mean HUs of the lumbar vertebrae and the L1 HUs, respectively. In patients without VFs, HUs decreased with age, similarly in males and females. The prevalence of osteoporosis (sT-score ≤ –2.5) was 23.8% and 36.5% in patients without and with VFs, respectively. Conclusion It is feasible on a large scale to screen for VFs and osteoporosis during opportunistic screening in patients 60 years or older having lumbar or abdominal CT.

Published
2021

17. Développement d’un algorithme d’intelligence artificielle pour le diagnostic de mélanome à partir d’une banque d’images issues de la pratique courante

Author

C. Velter, Céleste Lebbé, Pauline Laly, Julie Delyon, Mathieu Galtier, Aurélien Maire, Brigitte Dréno, Benoît Schmauch, Barouyr Baroudjian, Clara Allayous, and Yannick Jacob

Subjects
Ocean Engineering, Safety, Risk, Reliability and Quality
Abstract

Introduction Le diagnostic de melanome est l’un des points les plus cruciaux de la prise en charge dermatologique. Recemment, le developpement d’algorithmes de deep learning pour le diagnostic de tumeurs cutanees malignes et en particulier de melanome amorce une revolution dans le diagnostic par le dermatologue. Cependant, la plupart des algorithmes des etudes publiees ont ete developpes sur des bases de donnees publiques. Nous avons developpe un algorithme destine au diagnostic de tumeurs cutanees malignes a partir des photographies de vie reelle, prises dans le cadre de soin. Materiel et methodes Les donnees utilisees pour le developpement et la validation de l’algorithme sont la banque d’images dermoscopiques constituees dans le cadre du soin a l’hopital Saint-Louis, comprenant 985 images dermoscopiques annotees pour le diagnostic par 2 medecins diplomes (aspect clinique, et resultat histologique pour les tumeurs malignes). La seconde base est la base publique de photos dermoscopiques ISIC comprenant 24 464 images dermoscopiques. Nous avons entraine un algorithme (un reseau de neurones convolutif de type EfficientNet-B1) a l’identification des tumeurs malignes (TM) (melanome, carcinomes) versus benignes (naevus, HCF, tumeurs vasculaires benignes) (TB), et au diagnostic de lesions melanocytaires ou non melanocytaires, au sein de la base locale. Nous avons evalue la performance de l’algorithme entre differentes bases de donnees en l’entrainant sur la base de donnees ISIC et en le testant sur la base locale. Resultats Notre algorithme entraine sur la base ISIC a une precision diagnostique (aire sous la courbe ROC) de 0,90 sur la base ISIC, et de 0,79 pour le diagnostic de TM vs TB sur notre base locale. Pour identifier les facteurs participant a cette baisse de performance, nous avons analyse 33 photos « mal classees » pour le diagnostic de tumeurs melanocytaires ou non par l’algorithme. Les principales sources d’erreur sont les lentigos (classes en non melanocytaire) et les CBC tatoues et les keratoses seborrheiques (classe en melanocytaire). L’analyse de 33 photos mal classees a permis d’identifier des erreurs recurrentes: biopsies anterieures remaniant la lesion (7), trait de crayon sur la photo (4), erreur d’annotation dans l’image (4), localisation muqueuse (1), erreur de diagnostic identique a celle du medecin prescripteur (8). Discussion Le developpement d’un algorithme sur une base de photos issues de la vie reelle permet d’identifier les facteurs pouvant alterer ses performances, et qu’il faudra prendre en compte lors du developpement de ce type d’outils aupres des dermatologues. Le developpement de l’algorithme va etre poursuivi par un apprentissage federe entre differentes bases de donnees hospitalieres pour ameliorer ses performances.

Published
2021
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18. Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse

Author

Sébastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos, Adam Wild, Aurélien Maire, Simona Bottani, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-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-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Bases de données sur la Biodiversité, Ecologie, Environnement et Sociétés (BBEES), Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), The research leading to these results has received funding from the Abeona Foundation (project Brain@Scale), from the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM(2010), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), APPRIMAGE, ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-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)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), 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)-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)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), 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), Bottani, Simona, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, and Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Computer science, Image quality, Computer Vision and Pattern Recognition (cs.CV), media_common.quotation_subject, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Computer Science - Computer Vision and Pattern Recognition, Health Informatics, [INFO] Computer Science [cs], computer.software_genre, Convolutional neural network, Clinical data warehouse, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 030218 nuclear medicine & medical imaging, Set (abstract data type), 03 medical and health sciences, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0302 clinical medicine, Data Warehousing, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, [INFO]Computer Science [cs], Radiology, Nuclear Medicine and imaging, Quality (business), media_common, Protocol (science), Radiological and Ultrasound Technology, business.industry, Deep learning, Image and Video Processing (eess.IV), Medical image computing, Brain, Quality control, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Electrical Engineering and Systems Science - Image and Video Processing, Magnetic Resonance Imaging, Computer Graphics and Computer-Aided Design, Data warehouse, Brain MRI, Neural Networks, Computer, Computer Vision and Pattern Recognition, Data mining, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-H\^opitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score \textgreater 90\%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score \textgreater 80\%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.

Published
2022
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19. Solutions pour le partage de données et d'outils dans le Système d’Information d'AnaEE-France

Author

Christian Pichot, Cecile Callou, Damien Maurice, Andre Chanzy, Philippe Clastre, Francois Lafolie, Arnaud Leturgie, Michel Loreau, Aurélien Maire, Chloé Martin, Ghislaine Monet, Helene Raynal, Antoine Schellenberger, Ecologie des Forêts Méditerranéennes (URFM), Institut National de la Recherche Agronomique (INRA), Bases de données sur la Biodiversité, Ecologie, Environnement et Sociétés (BBEES), Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN), Muséum national d'Histoire naturelle (MNHN), Ecologie et Ecophysiologie Forestières [devient SILVA en 2018] (EEF), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Station d’Ecologie Expérimentale du CNRS à Moulis (SEEM), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Colas Rail, Centre Alpin de Recherche sur les Réseaux Trophiques et Ecosystèmes Limniques (CARRTEL), Institut National de la Recherche Agronomique (INRA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), InfoSol (InfoSol), Ecologie des Forêts Méditerranéennes [Avignon] (URFM 629), US BBEES, Centre National de la Recherche Scientifique (CNRS), Theoretical and Experimental Ecology Station, Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut National de la Recherche Agronomique (INRA), and Unité INFOSOL (ORLEANS INFOSOL)

Subjects
ecosystem, partage des données, système d'information, [SDV]Life Sciences [q-bio], écosystème
Abstract

Solutions pour le partage de données et d'outils dans le Système d’Information d'AnaEE-France. 25. Journées francophones d'Ingénierie des Connaissances (IC)

Published
2014

20. Sémantique et métadonnées de l'infrastructure AnaEE-france

Author

Christian Pichot, Eric Aïvayan, Nicolas Beudez, Cecile Callou, Andre Chanzy, Philippe Clastre, Mohamed El-Hamadry, Lucile Greiveldinger, Francois Lafolie, Arnaud Leturgie, Aurélien Maire, Chloé Martin, Damien Maurice, Nicolas Moitrier, Ghislaine Monet, Helene Raynal, Antoine Schellenberger, Rachid Yahiaoui, Ecologie des Forêts Méditerranéennes (URFM), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD [France-Sud]), Bases de données sur la Biodiversité, Ecologie, Environnement et Sociétés (BBEES), Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN), Muséum national d'Histoire naturelle (MNHN), Ho Chi Minh City University of Technology (HCMUT), Colas Rail, Ecologie et Ecophysiologie Forestières [devient SILVA en 2018] (EEF), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Centre Alpin de Recherche sur les Réseaux Trophiques et Ecosystèmes Limniques (CARRTEL), Institut National de la Recherche Agronomique (INRA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), InfoSol (InfoSol), Fondation pour la Recherche sur la Biodiversité (FRB). FRA., Ecologie des Forêts Méditerranéennes [Avignon] (URFM 629), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), US BBEES, Centre National de la Recherche Scientifique (CNRS), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut National de la Recherche Agronomique (INRA), and Unité INFOSOL (ORLEANS INFOSOL)

Subjects
standard ontology, partage des données, métadonnées, [SDV]Life Sciences [q-bio], semantic data, donnée sémantique
Abstract

Sémantique et métadonnées de l'infrastructure AnaEE-france. Forum des utilisateurs de l’e-infrastructure ECOSCOPE - Portail de métadonnées

21. Transfer learning from synthetic to routine clinical data for motion artefact detection in brain T1-weighted MRI

Author

Sophie Loizillon, Simona Bottani, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos, Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre National de la Recherche Scientifique (CNRS), The research leading to these results has received funding from the Abeona Foundation (project Brain@Scale), from the French government under management of Agence Nationale de la Recherche as part of the 'Investisse-ments d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6)., APPRIMAGE, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), Loizillon, Sophie, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, and Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID

Subjects
Quality Control, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Motion, Deep Learning, Clinical Data Warehouse, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO]Computer Science [cs], Transfer Learning, [INFO] Computer Science [cs], MRI, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Clinical data warehouses (CDWs) contain the medical data of millions of patients and represent a great opportunity to develop computational tools. MRIs are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are unusable because corrupted by these artefacts. Since their manual detection is impossible due to the number of scans, it is necessary to develop a tool to automatically exclude images with motion in order to fully exploit CDWs. In this paper, we propose a CNN for the automatic detection of motion in 3D T1-weighted brain MRI. Our transfer learning approach, based on synthetic motion generation, consists of two steps: a pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model to clinical data, relying on the manual labelling of 5500 images. The objectives were both (1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced accuracy>80%). However, for the second objective, the performance was weaker and substantially lower than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical imaging and to highlight the importance of a clinical validation of models trained on research data.

22. 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
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23. 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
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24. Observational Study of Chlorpromazine in Hospitalized Patients with Covid-19 Running title: Chlorpromazine in hospitalized patients with Covid-19

Author

Nicolas Hoertel, Marina Sanchez-Rico, Raphaël Vernet, Anne-Sophie Jannot, Antoine Neuraz, Carlos Blanco, Cédric Lemogne, Guillaume Airagnes, Nicolas Paris, Christel Daniel, Alexandre Gramfort, Guillaume Lemaitre, Mélodie Bernaux, Ali Bellamine, Nathanael Beeker, Frederic Limosin, 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), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), Université Paris Cité (UPCité), Universidad Complutense de Madrid = Complutense University of Madrid [Madrid] (UCM), Département d'Informatique et Santé Publique [CHU HEGP] (HEGP - Informatique), 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), 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), 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), National Institute on Drug Abuse [Bethesda] (NIDA), AP-HP. Université Paris Saclay, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), 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, Université Paris-Saclay, Institut National de Recherche en Informatique et en Automatique (Inria), CEA- Saclay (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Hôpital Cochin [AP-HP], AP-HP/Universities/INSERM COVID-19 Research Collaboration and AP-HP COVID CDR Initiative: 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, Villié, Pascaline, Université Paris Cité - UFR Médecine [Santé] (UPCité UFR Médecine), École pratique des hautes études (EPHE), Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), 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), 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), National Institutes of Health [Bethesda] (NIH), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), 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)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay

Subjects
[SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, ComputingMilieux_MISCELLANEOUS, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

International audience; Introduction: Chlorpromazine has been suggested as being potentially useful in patients with coronavirus disease 2019 (COVID-19) on the grounds of its potential antiviral and anti-inflammatory effects.Objective: The aim of this study was to examine the association between chlorpromazine use and mortality among adult patients hospitalized for COVID-19.Methods: We conducted an observational, multicenter, retrospective study at Assistance Publique-Hôpitaux de Paris (AP-HP) Greater Paris University hospitals. Study baseline was defined as the date of first prescription of chlorpromazine during hospitalization for COVID-19. The primary endpoint was death. Among patients who had not been hospitalized in intensive care units (ICUs), we compared this endpoint between those who received chlorpromazine and those who did not, in time-to-event analyses adjusted for patient characteristics, clinical markers of disease severity, and other psychotropic medications. The primary analysis used a Cox regression model with inverse probability weighting. Multiple sensitivity analyses were performed.Results: Of the 14,340 adult inpatients hospitalized outside ICUs for COVID-19, 55 patients (0.4%) received chlorpromazine. Over a mean follow-up of 14.3 days (standard deviation [SD] 18.2), death occurred in 13 patients (23.6%) who received chlorpromazine and 1289 patients (9.0%) who did not. In the primary analysis, there was no significant association between chlorpromazine use and mortality (hazard ratio [HR] 2.01, 95% confidence interval [CI] 0.75-5.40; p = 0.163). Sensitivity analyses included a Cox regression in a 1:5 ratio matched analytic sample that showed a similar result (HR 1.67, 95% CI 0.91-3.06; p = 0.100) and a multivariable Cox regression that indicated a significant positive association (HR 3.10, 95% CI 1.31-7.34; p = 0.010).Conclusion: Our results suggest that chlorpromazine prescribed at a mean daily dose of 70.8 mg (SD 65.3) was not associated with reduced mortality.

Published
2021
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25. Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic

Author

Neuraz, Antoine, Lerner, Ivan, Digan, William, Paris, Nicolas, Tsopra, Rosy, Rogier, Alice, Baudoin, David, Cohen, Kevin Bretonnel, Burgun, Anita, Garcelon, Nicolas, Rance, Bastien, 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), Imagine - Institut des maladies génétiques (IMAGINE - U1163), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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 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), Université Paris Cité (UPCité), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CHU Necker - Enfants Malades [AP-HP], 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), University of Colorado [Denver], Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), AP-HP/Universities/INSERM COVID-19 Research Collaboration, AP-HP COVID CDR Initiative: 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 Duclos, 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, Neuraz, Antoine, TSOPRA, Rosy, Modélisation Conceptuelle des Connaissances Biomédicales, Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )

Subjects
MESH: Pandemics, Time Factors, MESH: Coronavirus Infections, [SDV]Life Sciences [q-bio], Pneumonia, Viral, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, MESH: Calcium Channel Blockers, [INFO] Computer Science [cs], lcsh:Computer applications to medicine. Medical informatics, MESH: Hypertension, MESH: Natural Language Processing, Betacoronavirus, Data Mining, Humans, informatics, MESH: COVID-19, [INFO]Computer Science [cs], MESH: SARS-CoV-2, natural language processing, Pandemics, MESH: Electronic Health Records, Original Paper, response, MESH: Humans, SARS-CoV-2, lcsh:Public aspects of medicine, MESH: Data Mining, public health, MESH: Time Factors, COVID-19, lcsh:RA1-1270, Calcium Channel Blockers, emergent disease, COVID-19 Drug Treatment, [SDV] Life Sciences [q-bio], [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, electronic health records, MESH: Pneumonia, Viral, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Hypertension, lcsh:R858-859.7, MESH: Betacoronavirus, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Coronavirus Infections, medication information
Abstract

BackgroundA novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. ObjectiveThe aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). MethodsWe explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. ResultsIn this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. ConclusionsIn our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

Published
2020
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