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1. Resilience of the quantum critical line in the Schmid transition

Author

Paris, Nicolas, Giacomelli, Luca, Daviet, Romain, Ciuti, Cristiano, Dupuis, Nicolas, and Mora, Christophe

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Superconductivity, Quantum Physics
Abstract

Schmid predicted that a single Josephson junction coupled to a resistive environment undergoes a quantum phase transition to an insulating phase when the shunt resistance $R$ exceeds the resistance quantum $h/(4 e^ 2)$. Recent measurements and theoretical studies have sparked a debate on whether the location of this transition depends on the ratio between the Josephson and the charging energies. We employ a combination of multiple innovative analytical and numerical techniques, never before explicitly applied to this problem, to decisively demonstrate that the transition line between superconducting and insulating behavior is indeed independent of this energy ratio. First, we apply field-theory renormalization group methods and find that the $\beta$ function vanishes along the critical line up to the third order in the Josephson energy. We then identify a simple fermionic model that precisely captures the low-energy physics on the critical line, regardless of the energy ratio. This conformally invariant fermionic model is verified by comparing the expected spectrum with exact diagonalization calculations of the resistively shunted Josephson junction, showing excellent agreement even for moderate system sizes. Importantly, this identification provides a rigorous non-perturbative proof that the transition line is maintained at $R=h/(4 e^ 2)$ for all ratios of Josephson to charging energies. The line is further resilient to other ultraviolet cutoffs such as the plasma frequency of the resistive environment. Finally, we implement an adiabatic approach to validate the duality at large Josephson energy., Comment: 11+5 pages, 7+2 figures

Published
2024

2. 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, and Rance, Bastien

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
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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5. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium

Author

Aaron, James R., Adam, Atif, Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., Angoulvant, François, Anthony, Li LLJ., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Panickan, Vidul Ayakulangara, Azevedo, Paula S., Badenes, Rafael, Balshi, James, Batugo, Ashley, Beaulieu-Jones, Brendin R., Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Cattelan, Anna Maria, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, García-Barrio, Noelia, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Goh, Rachel SJ., González, Tomás González, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Guzzi, Pietro H., Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Honerlaw, Jacqueline P., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kainth, Mundeep K., Kate, Kernan F., Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Kernan, Kate F., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Makwana, Simran, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Maripuri, Monika, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mazzotti, Diego R., Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku FM., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Ashley C., Pfaff, Emily R., Pillion, Danielle, Pizzimenti, Sara, Priya, Tanu, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Romero-Garcia, Nekane, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin A., Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Sperotto, Francesca, Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia LM., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma MS., Torti, Carlo, Trecarichi, Enrico M., Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zachariasse, Joany M., Zahner, Janet J., Zambelli, Alberto, Zhang, Harrison G., Zöller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Li, Xiudi, Rofeberg, Valerie N., Elias, Matthew D., Laird-Gion, Jessica, and Newburger, Jane W.

Published
2023
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6. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Author

Aaron, James R., Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., François Angoulvant, Anthony, Li L.L.J., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Azevedo, Paula S., Balshi, James, Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, Barrio, Noelia García, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Le, Trang T., Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku F.M., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Emily R., Pfaff, Ashley C., Pillion, Danielle, Pizzimenti, Sara, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin, Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia L.M., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma M.S., Torti, Carlo, Trecarichi, Enrico M., Tseng, Yi-Ju, Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison G., Zo¨ller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Mesa, Rebecca, and Verdy, Guillame

Published
2023
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7. Testing Sharpe ratio: luck or skill?

Author

Benhamou, Eric, Saltiel, David, Guez, Beatrice, and Paris, Nicolas

Subjects
Quantitative Finance - Risk Management
Abstract

Sharpe ratio (sometimes also referred to as information ratio) is widely used in asset management to compare and benchmark funds and asset managers. It computes the ratio of the (excess) net return over the strategy standard deviation. However, the elements to compute the Sharpe ratio, namely, the expected returns and the volatilities are unknown numbers and need to be estimated statistically. This means that the Sharpe ratio used by funds is likely to be error prone because of statistical estimation errors. In this paper, we provide various tests to measure the quality of the Sharpe ratios. By quality, we are aiming at measuring whether a manager was indeed lucky of skillful. The test assesses this through the statistical significance of the Sharpe ratio. We not only look at the traditional Sharpe ratio but also compute a modified Sharpe insensitive to used Capital. We provide various statistical tests that can be used to precisely quantify the fact that the Sharpe is statistically significant. We illustrate in particular the number of trades for a given Sharpe level that provides statistical significance as well as the impact of auto-correlation by providing reference tables that provides the minimum required Sharpe ratio for a given time period and correlation. We also provide for a Sharpe ratio of 0.5, 1.0, 1.5 and 2.0 the skill percentage given the auto-correlation level., Comment: 56 pages, 44 tables

Published
2019

8. Terminologies augmented recurrent neural network model for clinical named entity recognition

Author

Lerner, Ivan, Paris, Nicolas, and Tannier, Xavier

Subjects
Computer Science - Computation and Language
Abstract

We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities. We used a terminology-based system as baseline, built upon UMLS and SNOMED. Then, we evaluated a biGRU-CRF, and an hybrid system using the prediction of the terminology-based system as feature for the biGRU-CRF. In English, we evaluated the NER systems on the i2b2-2009 Medication Challenge for Drug name recognition, which contained 8,573 entities for 268 documents. In French, we built APcNER, a corpus of 147 documents annotated for 5 entities (drug name, sign or symptom, disease or disorder, diagnostic procedure or lab test and therapeutic procedure). We evaluated each NER systems using exact and partial match definition of F-measure for NER. The APcNER contains 4,837 entities which took 28 hours to annotate, the inter-annotator agreement was acceptable for Drug name in exact match (85%) and acceptable for other entity types in non-exact match (>70%). For drug name recognition on both i2b2-2009 and APcNER, the biGRU-CRF performed better that the terminology-based system, with an exact-match F-measure of 91.1% versus 73% and 81.9% versus 75% respectively. Moreover, the hybrid system outperformed the biGRU-CRF, with an exact-match F-measure of 92.2% versus 91.1% (i2b2-2009) and 88.4% versus 81.9% (APcNER). On APcNER corpus, the micro-average F-measure of the hybrid system on the 5 entities was 69.5% in exact match, and 84.1% in non-exact match. APcNER is a French corpus for clinical-NER of five type of entities which covers a large variety of document types. Extending supervised model with terminology allowed for an easy performance gain, especially in low regimes of entities, and established near state of the art results on the i2b2-2009 corpus.

Published
2019

9. BCMA-ES II: revisiting Bayesian CMA-ES

Author

Benhamou, Eric, Saltiel, David, Guez, Beatrice, and Paris, Nicolas

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning, 68T01, 68T20
Abstract

This paper revisits the Bayesian CMA-ES and provides updates for normal Wishart. It emphasizes the difference between a normal and normal inverse Wishart prior. After some computation, we prove that the only difference relies surprisingly in the expected covariance. We prove that the expected covariance should be lower in the normal Wishart prior model because of the convexity of the inverse. We present a mixture model that generalizes both normal Wishart and normal inverse Wishart model. We finally present various numerical experiments to compare both methods as well as the generalized method., Comment: 10 pages, 15 figures

Published
2019

10. Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials

Author

Tannier, Xavier, Paris, Nicolas, Cisneros, Hugo, Daniel, Christel, Doutreligne, Matthieu, Duclos, Catherine, Griffon, Nicolas, Hassen-Khodja, Claire, Lerner, Ivan, Parrot, Adrien, Sadou, Éric, Saussol, Cyrina, and Vaillant, Pascal

Subjects
Computer Science - Computation and Language
Abstract

Objective: Natural language processing can help minimize human intervention in identifying patients meeting eligibility criteria for clinical trials, but there is still a long way to go to obtain a general and systematic approach that is useful for researchers. We describe two methods taking a step in this direction and present their results obtained during the n2c2 challenge on cohort selection for clinical trials. Materials and Methods: The first method is a weakly supervised method using an unlabeled corpus (MIMIC) to build a silver standard, by producing semi-automatically a small and very precise set of rules to detect some samples of positive and negative patients. This silver standard is then used to train a traditional supervised model. The second method is a terminology-based approach where a medical expert selects the appropriate concepts, and a procedure is defined to search the terms and check the structural or temporal constraints. Results: On the n2c2 dataset containing annotated data about 13 selection criteria on 288 patients, we obtained an overall F1-measure of 0.8969, which is the third best result out of 45 participant teams, with no statistically significant difference with the best-ranked team. Discussion: Both approaches obtained very encouraging results and apply to different types of criteria. The weakly supervised method requires explicit descriptions of positive and negative examples in some reports. The terminology-based method is very efficient when medical concepts carry most of the relevant information. Conclusion: It is unlikely that much more annotated data will be soon available for the task of identifying a wide range of patient phenotypes. One must focus on weakly or non-supervised learning methods using both structured and unstructured data and relying on a comprehensive representation of the patients., Comment: 15 pages

Published
2019

11. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

Author

Brat, Gabriel A, Weber, Griffin M, Gehlenborg, Nils, Avillach, Paul, Palmer, Nathan P, Chiovato, Luca, Cimino, James, Waitman, Lemuel R, Omenn, Gilbert S, Malovini, Alberto, Moore, Jason H, Beaulieu-Jones, Brett K, Tibollo, Valentina, Murphy, Shawn N, Yi, Sehi L’, Keller, Mark S, Bellazzi, Riccardo, Hanauer, David A, Serret-Larmande, Arnaud, Gutierrez-Sacristan, Alba, Holmes, John J, Bell, Douglas S, Mandl, Kenneth D, Follett, Robert W, Klann, Jeffrey G, Murad, Douglas A, Scudeller, Luigia, Bucalo, Mauro, Kirchoff, Katie, Craig, Jean, Obeid, Jihad, Jouhet, Vianney, Griffier, Romain, Cossin, Sebastien, Moal, Bertrand, Patel, Lav P, Bellasi, Antonio, Prokosch, Hans U, Kraska, Detlef, Sliz, Piotr, Tan, Amelia LM, Ngiam, Kee Yuan, Zambelli, Alberto, Mowery, Danielle L, Schiver, Emily, Devkota, Batsal, Bradford, Robert L, Daniar, Mohamad, Daniel, Christel, Benoit, Vincent, Bey, Romain, Paris, Nicolas, Serre, Patricia, Orlova, Nina, Dubiel, Julien, Hilka, Martin, Jannot, Anne Sophie, Breant, Stephane, Leblanc, Judith, Griffon, Nicolas, Burgun, Anita, Bernaux, Melodie, Sandrin, Arnaud, Salamanca, Elisa, Cormont, Sylvie, Ganslandt, Thomas, Gradinger, Tobias, Champ, Julien, Boeker, Martin, Martel, Patricia, Esteve, Loic, Gramfort, Alexandre, Grisel, Olivier, Leprovost, Damien, Moreau, Thomas, Varoquaux, Gael, Vie, Jill-Jênn, Wassermann, Demian, Mensch, Arthur, Caucheteux, Charlotte, Haverkamp, Christian, Lemaitre, Guillaume, Bosari, Silvano, Krantz, Ian D, South, Andrew, Cai, Tianxi, and Kohane, Isaac S

Subjects
Health Services and Systems, Health Sciences, Good Health and Well Being, Databases, Outcomes research, Viral infection, Health services and systems
Abstract

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Published
2020

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

Author

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, Jannot, Anne Sophie, Lambert, Jerome, Layese, Richard, Leblanc, Judith, Lebouter, Léo, Lemaitre, Guillaume, 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, 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, Hoertel, Nicolas, Sánchez-Rico, Marina, de la Muela, Pedro, Abellán, Miriam, Blanco, Carlos, Leboyer, Marion, Cougoule, Céline, Gulbins, Erich, Kornhuber, Johannes, Carpinteiro, Alexander, Becker, Katrin Anne, Vernet, Raphaël, Alvarado, Jesús M., Herrera-Morueco, Juan José, Airagnes, Guillaume, Lemogne, Cédric, and Limosin, Frédéric

Published
2023
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14. Three remarkable properties of the Normal distribution

Author

Benhamou, Eric, Guez, Beatrice, and Paris, Nicolas

Subjects
Mathematics - Probability
Abstract

In this paper, we present three remarkable properties of the normal distribution: first that if two independent variables's sum is normally distributed, then each random variable follows a normal distribution (which is referred to as the Levy Cramer theorem), second a variation of the Levy Cramer theorem that states that if two independent symmetric random variables with finite variance have their sum and their difference independent, then each random variable follows a standard normal distribution, and third that the normal distribution is characterized by the fact that it is the only distribution for which the sample mean and variance are independent (which is a central property for deriving the Student distribution and referred as the Geary theorem). The novelty of this paper is to provide new, quicker or self contained proofs of theses theorems., Comment: 12 pages. arXiv admin note: text overlap with arXiv:1809.03774

Published
2018

17. Solving WCSP by Extraction of Minimal Unsatisfiable Cores

Author

Lecoutre, Christophe, Paris, Nicolas, Roussel, Olivier, and Tabary, Sébastien

Subjects
Computer Science - Artificial Intelligence
Abstract

Usual techniques to solve WCSP are based on cost transfer operations coupled with a branch and bound algorithm. In this paper, we focus on an approach integrating extraction and relaxation of Minimal Unsatisfiable Cores in order to solve this problem. We decline our approach in two ways: an incomplete, greedy, algorithm and a complete one.

Published
2013

19. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium

Author

Sperotto, Francesca, primary, Gutiérrez-Sacristán, Alba, additional, Makwana, Simran, additional, Li, Xiudi, additional, Rofeberg, Valerie N., additional, Cai, Tianxi, additional, Bourgeois, Florence T., additional, Omenn, Gilbert S., additional, Hanauer, David A., additional, Sáez, Carlos, additional, Bonzel, Clara-Lea, additional, Bucholz, Emily, additional, Dionne, Audrey, additional, Elias, Matthew D., additional, García-Barrio, Noelia, additional, González, Tomás González, additional, Issitt, Richard W., additional, Kernan, Kate F., additional, Laird-Gion, Jessica, additional, Maidlow, Sarah E., additional, Mandl, Kenneth D., additional, Ahooyi, Taha Mohseni, additional, Moraleda, Cinta, additional, Morris, Michele, additional, Moshal, Karyn L., additional, Pedrera-Jiménez, Miguel, additional, Shah, Mohsin A., additional, South, Andrew M., additional, Spiridou, Anastasia, additional, Taylor, Deanne M., additional, Verdy, Guillaume, additional, Visweswaran, Shyam, additional, Wang, Xuan, additional, Xia, Zongqi, additional, Zachariasse, Joany M., additional, Newburger, Jane W., additional, Avillach, Paul, additional, Aaron, James R., additional, Adam, Atif, additional, Agapito, Giuseppe, additional, Albayrak, Adem, additional, Albi, Giuseppe, additional, Alessiani, Mario, additional, Alloni, Anna, additional, Amendola, Danilo F., additional, Angoulvant, François, additional, Anthony, Li LLJ., additional, Aronow, Bruce J., additional, Ashraf, Fatima, additional, Atz, Andrew, additional, Panickan, Vidul Ayakulangara, additional, Azevedo, Paula S., additional, Badenes, Rafael, additional, Balshi, James, additional, Batugo, Ashley, additional, Beaulieu-Jones, Brendin R., additional, Beaulieu-Jones, Brett K., additional, Bell, Douglas S., additional, Bellasi, Antonio, additional, Bellazzi, Riccardo, additional, Benoit, Vincent, additional, Beraghi, Michele, additional, Bernal-Sobrino, José Luis, additional, Bernaux, Mélodie, additional, Bey, Romain, additional, Bhatnagar, Surbhi, additional, Blanco-Martínez, Alvar, additional, Boeker, Martin, additional, Booth, John, additional, Bosari, Silvano, additional, Bradford, Robert L., additional, Brat, Gabriel A., additional, Bréant, Stéphane, additional, Brown, Nicholas W., additional, Bruno, Raffaele, additional, Bryant, William A., additional, Bucalo, Mauro, additional, Burgun, Anita, additional, Cannataro, Mario, additional, Carmona, Aldo, additional, Cattelan, Anna Maria, additional, Caucheteux, Charlotte, additional, Champ, Julien, additional, Chen, Jin, additional, Chen, Krista Y., additional, Chiovato, Luca, additional, Chiudinelli, Lorenzo, additional, Cho, Kelly, additional, Cimino, James J., additional, Colicchio, Tiago K., additional, Cormont, Sylvie, additional, Cossin, Sébastien, additional, Craig, Jean B., additional, Cruz-Bermúdez, Juan Luis, additional, Cruz-Rojo, Jaime, additional, Dagliati, Arianna, additional, Daniar, Mohamad, additional, Daniel, Christel, additional, Das, Priyam, additional, Devkota, Batsal, additional, Duan, Rui, additional, Dubiel, Julien, additional, DuVall, Scott L., additional, Esteve, Loic, additional, Estiri, Hossein, additional, Fan, Shirley, additional, Follett, Robert W., additional, Ganslandt, Thomas, additional, Garmire, Lana X., additional, Gehlenborg, Nils, additional, Getzen, Emily J., additional, Geva, Alon, additional, Goh, Rachel SJ., additional, Gradinger, Tobias, additional, Gramfort, Alexandre, additional, Griffier, Romain, additional, Griffon, Nicolas, additional, Grisel, Olivier, additional, Guzzi, Pietro H., additional, Han, Larry, additional, Haverkamp, Christian, additional, Hazard, Derek Y., additional, He, Bing, additional, Henderson, Darren W., additional, Hilka, Martin, additional, Ho, Yuk-Lam, additional, Holmes, John H., additional, Honerlaw, Jacqueline P., additional, Hong, Chuan, additional, Huling, Kenneth M., additional, Hutch, Meghan R., additional, Jannot, Anne Sophie, additional, Jouhet, Vianney, additional, Kainth, Mundeep K., additional, Kate, Kernan F., additional, Kavuluru, Ramakanth, additional, Keller, Mark S., additional, Kennedy, Chris J., additional, Key, Daniel A., additional, Kirchoff, Katie, additional, Klann, Jeffrey G., additional, Kohane, Isaac S., additional, Krantz, Ian D., additional, Kraska, Detlef, additional, Krishnamurthy, Ashok K., additional, L'Yi, Sehi, additional, Leblanc, Judith, additional, Lemaitre, Guillaume, additional, Lenert, Leslie, additional, Leprovost, Damien, additional, Liu, Molei, additional, Will Loh, Ne Hooi, additional, Long, Qi, additional, Lozano-Zahonero, Sara, additional, Luo, Yuan, additional, Lynch, Kristine E., additional, Mahmood, Sadiqa, additional, Makoudjou, Adeline, additional, Malovini, Alberto, additional, Mao, Chengsheng, additional, Maram, Anupama, additional, Maripuri, Monika, additional, Martel, Patricia, additional, Martins, Marcelo R., additional, Marwaha, Jayson S., additional, Masino, Aaron J., additional, Mazzitelli, Maria, additional, Mazzotti, Diego R., additional, Mensch, Arthur, additional, Milano, Marianna, additional, Minicucci, Marcos F., additional, Moal, Bertrand, additional, Moore, Jason H., additional, Morris, Jeffrey S., additional, Mousavi, Sajad, additional, Mowery, Danielle L., additional, Murad, Douglas A., additional, Murphy, Shawn N., additional, Naughton, Thomas P., additional, Breda Neto, Carlos Tadeu, additional, Neuraz, Antoine, additional, Newburger, Jane, additional, Ngiam, Kee Yuan, additional, Njoroge, Wanjiku FM., additional, Norman, James B., additional, Obeid, Jihad, additional, Okoshi, Marina P., additional, Olson, Karen L., additional, Orlova, Nina, additional, Ostasiewski, Brian D., additional, Palmer, Nathan P., additional, Paris, Nicolas, additional, Patel, Lav P., additional, Pfaff, Ashley C., additional, Pfaff, Emily R., additional, Pillion, Danielle, additional, Pizzimenti, Sara, additional, Priya, Tanu, additional, Prokosch, Hans U., additional, Prudente, Robson A., additional, Prunotto, Andrea, additional, Quirós-González, Víctor, additional, Ramoni, Rachel B., additional, Raskin, Maryna, additional, Rieg, Siegbert, additional, Roig-Domínguez, Gustavo, additional, Rojo, Pablo, additional, Romero-Garcia, Nekane, additional, Rubio-Mayo, Paula, additional, Sacchi, Paolo, additional, Salamanca, Elisa, additional, Samayamuthu, Malarkodi Jebathilagam, additional, Sanchez-Pinto, L. Nelson, additional, Sandrin, Arnaud, additional, Santhanam, Nandhini, additional, Santos, Janaina C.C., additional, Sanz Vidorreta, Fernando J., additional, Savino, Maria, additional, Schriver, Emily R., additional, Schubert, Petra, additional, Schuettler, Juergen, additional, Scudeller, Luigia, additional, Sebire, Neil J., additional, Serrano-Balazote, Pablo, additional, Serre, Patricia, additional, Serret-Larmande, Arnaud, additional, Hossein Abad, Zahra Shakeri, additional, Silvio, Domenick, additional, Sliz, Piotr, additional, Son, Jiyeon, additional, Sonday, Charles, additional, Sperotto, Francesca, additional, Strasser, Zachary H., additional, Tan, Amelia LM., additional, Tan, Bryce W.Q., additional, Tan, Byorn W.L., additional, Tanni, Suzana E., additional, Terriza-Torres, Ana I., additional, Tibollo, Valentina, additional, Tippmann, Patric, additional, Toh, Emma MS., additional, Torti, Carlo, additional, Trecarichi, Enrico M., additional, Vallejos, Andrew K., additional, Varoquaux, Gael, additional, Vella, Margaret E., additional, Vie, Jill-Jênn, additional, Vitacca, Michele, additional, Wagholikar, Kavishwar B., additional, Waitman, Lemuel R., additional, Wassermann, Demian, additional, Weber, Griffin M., additional, Wolkewitz, Martin, additional, Wong, Scott, additional, Xiong, Xin, additional, Ye, Ye, additional, Yehya, Nadir, additional, Yuan, William, additional, Zahner, Janet J., additional, Zambelli, Alberto, additional, Zhang, Harrison G., additional, Zöller, Daniela, additional, Zuccaro, Valentina, additional, and Zucco, Chiara, additional

Published
2023
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21. Predictive factors of return‐to‐work trajectory after work‐related rotator cuff syndrome: A prospective study of 96 workers.

Author

Pichené‐Houard, Anne, Sirveaux, François, Clerc‐Urmès, Isabelle, Paris, Nicolas, Michel, Blaise, Jacquot, Adrien, Martinet, Noël, Claudon, Laurent, Paysant, Jean, and Wild, Pascal

Subjects
ROTATOR cuff, MEDICAL personnel, CAREGIVERS, LONGITUDINAL method, ARTHROSCOPY
Abstract

Objective: Sustained return to work after surgery for work‐related rotator cuff syndrome (WRRCS) remains quite difficult. The main purpose of the present study was to identify predictive factors of a return‐to‐work (RTW) trajectory. Methods: A total of 96 workers with WRRCS were identified by 4 surgeons. They were followed prospectively before and after the surgery, until 1 year after RTW, or for 20 months after surgery when they did not. Participants completed a series of standardized questionnaires related to working conditions, health, and beliefs, and performed functional tests at the inclusion time. During the follow‐up period, they were regularly asked about their working conditions (present or not at work), activity (normal or lightened physical duties) and schedules (full‐ or part‐time job). Statistical analysis was based on single‐ and multiple‐factor models of prediction of the workers' trajectory. Results: Three trajectories of RTW were distinguished, considering RTW and absenteeism that occurred during the follow‐up: stable, unstable, and non‐RTW. The median age of the sample was 49.5 [45.0–54.0], with 67.7% of workers employed in highly physically demanding jobs. In the multiple factor model, three factors were highly predictive of the trajectory: perceived health before surgery, having had a repaired ruptured‐rotator‐cuff tendinopathy, and the level of physical demand of the job. Conclusion: Three easy‐to‐collect predictive factors of RTW trajectory have been identified. They may be useful for healthcare professionals and care givers to identify vulnerable workers' risk of occupational dropout after arthroscopic surgery for rotator cuff tendinopathy. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Risk of Death in Individuals Hospitalized for COVID-19 With and Without Psychiatric Disorders: An Observational Multicenter Study in France

Author

Hoertel, Nicolas, primary, Sánchez-Rico, Marina, additional, de la Muela, Pedro, additional, Abellán, Miriam, additional, Blanco, Carlos, additional, Leboyer, Marion, additional, Cougoule, Céline, additional, Gulbins, Erich, additional, Kornhuber, Johannes, additional, Carpinteiro, Alexander, additional, Becker, Katrin Anne, additional, Vernet, Raphaël, additional, Beeker, Nathanaël, additional, Neuraz, Antoine, additional, Alvarado, Jesús M., additional, Herrera-Morueco, Juan José, additional, Airagnes, Guillaume, additional, Lemogne, Cédric, additional, Limosin, Frédéric, additional, Ancel, Pierre-Yves, additional, Bauchet, Alain, additional, Benoit, Vincent, additional, Bernaux, Mélodie, additional, Bellamine, Ali, additional, Bey, Romain, additional, Bourmaud, Aurélie, additional, Breant, Stéphane, additional, Burgun, Anita, additional, Carrat, Fabrice, additional, Caucheteux, Charlotte, additional, Champ, Julien, additional, Cormont, Sylvie, additional, Daniel, Christel, additional, Dubiel, Julien, additional, Ducloas, Catherine, additional, Esteve, Loic, additional, Frank, Marie, additional, Garcelon, Nicolas, additional, Gramfort, Alexandre, additional, Griffon, Nicolas, additional, Grisel, Olivier, additional, Guilbaud, Martin, additional, Hassen-Khodja, Claire, additional, Hemery, François, additional, Hilka, Martin, additional, Jannot, Anne Sophie, additional, Lambert, Jerome, additional, Layese, Richard, additional, Leblanc, Judith, additional, Lebouter, Léo, additional, Lemaitre, Guillaume, additional, Leprovost, Damien, additional, Lerner, Ivan, additional, Sallah, Kankoe Levi, additional, Maire, Aurélien, additional, Mamzer, Marie-France, additional, Martel, Patricia, additional, Mensch, Arthur, additional, Moreau, Thomas, additional, Orlova, Nina, additional, Paris, Nicolas, additional, Rance, Bastien, additional, Ravera, Hélène, additional, Rozes, Antoine, additional, Salamanca, Elisa, additional, Sandrin, Arnaud, additional, Serre, Patricia, additional, Tannier, Xavier, additional, Treluyer, Jean-Marc, additional, Van Gysel, Damien, additional, Varoquaux, Gaël, additional, Vie, Jill Jen, additional, Wack, Maxime, additional, Wajsburt, Perceval, additional, Wassermann, Demian, additional, and Zapletal, Eric, additional

Published
2023
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24. Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: an international multi-centre observational cohort study

Author

Tan, Byorn W.L., primary, Tan, Bryce W.Q., additional, Tan, Amelia L.M., additional, Schriver, Emily R., additional, Gutiérrez-Sacristán, Alba, additional, Das, Priyam, additional, Yuan, William, additional, Hutch, Meghan R., additional, García Barrio, Noelia, additional, Pedrera Jimenez, Miguel, additional, Abu-el-rub, Noor, additional, Morris, Michele, additional, Moal, Bertrand, additional, Verdy, Guillaume, additional, Cho, Kelly, additional, Ho, Yuk-Lam, additional, Patel, Lav P., additional, Dagliati, Arianna, additional, Neuraz, Antoine, additional, Klann, Jeffrey G., additional, South, Andrew M., additional, Visweswaran, Shyam, additional, Hanauer, David A., additional, Maidlow, Sarah E., additional, Liu, Mei, additional, Mowery, Danielle L., additional, Batugo, Ashley, additional, Makoudjou, Adeline, additional, Tippmann, Patric, additional, Zöller, Daniela, additional, Brat, Gabriel A., additional, Luo, Yuan, additional, Avillach, Paul, additional, Bellazzi, Riccardo, additional, Chiovato, Luca, additional, Malovini, Alberto, additional, Tibollo, Valentina, additional, Samayamuthu, Malarkodi Jebathilagam, additional, Serrano Balazote, Pablo, additional, Xia, Zongqi, additional, Loh, Ne Hooi Will, additional, Chiudinelli, Lorenzo, additional, Bonzel, Clara-Lea, additional, Hong, Chuan, additional, Zhang, Harrison G., additional, Weber, Griffin M., additional, Kohane, Isaac S., additional, Cai, Tianxi, additional, Omenn, Gilbert S., additional, Holmes, John H., additional, Ngiam, Kee Yuan, additional, Aaron, James R., additional, Agapito, Giuseppe, additional, Albayrak, Adem, additional, Albi, Giuseppe, additional, Alessiani, Mario, additional, Alloni, Anna, additional, Amendola, Danilo F., additional, Angoulvant, François, additional, Anthony, Li L.L.J., additional, Aronow, Bruce J., additional, Ashraf, Fatima, additional, Atz, Andrew, additional, Panickan, Vidul Ayakulangara, additional, Azevedo, Paula S., additional, Balshi, James, additional, Beaulieu-Jones, Brett K., additional, Beaulieu-Jones, Brendin R., additional, Bell, Douglas S., additional, Bellasi, Antonio, additional, Benoit, Vincent, additional, Beraghi, Michele, additional, Bernal-Sobrino, José Luis, additional, Bernaux, Mélodie, additional, Bey, Romain, additional, Bhatnagar, Surbhi, additional, Blanco-Martínez, Alvar, additional, Boeker, Martin, additional, Booth, John, additional, Bosari, Silvano, additional, Bourgeois, Florence T., additional, Bradford, Robert L., additional, Bréant, Stéphane, additional, Brown, Nicholas W., additional, Bruno, Raffaele, additional, Bryant, William A., additional, Bucalo, Mauro, additional, Bucholz, Emily, additional, Burgun, Anita, additional, Cannataro, Mario, additional, Carmona, Aldo, additional, Cattelan, Anna Maria, additional, Caucheteux, Charlotte, additional, Champ, Julien, additional, Chen, Jin, additional, Chen, Krista Y., additional, Cimino, James J., additional, Colicchio, Tiago K., additional, Cormont, Sylvie, additional, Cossin, Sébastien, additional, Craig, Jean B., additional, Cruz-Bermúdez, Juan Luis, additional, Cruz-Rojo, Jaime, additional, Daniar, Mohamad, additional, Daniel, Christel, additional, Devkota, Batsal, additional, Dionne, Audrey, additional, Duan, Rui, additional, Dubiel, Julien, additional, DuVall, Scott L., additional, Esteve, Loic, additional, Estiri, Hossein, additional, Fan, Shirley, additional, Follett, Robert W., additional, Ganslandt, Thomas, additional, García-Barrio, Noelia, additional, Garmire, Lana X., additional, Gehlenborg, Nils, additional, Getzen, Emily J., additional, Geva, Alon, additional, González, Tomás González, additional, Gradinger, Tobias, additional, Gramfort, Alexandre, additional, Griffier, Romain, additional, Griffon, Nicolas, additional, Grisel, Olivier, additional, Guzzi, Pietro H., additional, Han, Larry, additional, Haverkamp, Christian, additional, Hazard, Derek Y., additional, He, Bing, additional, Henderson, Darren W., additional, Hilka, Martin, additional, Honerlaw, Jacqueline P., additional, Huling, Kenneth M., additional, Issitt, Richard W., additional, Jannot, Anne Sophie, additional, Jouhet, Vianney, additional, Kavuluru, Ramakanth, additional, Keller, Mark S., additional, Kennedy, Chris J., additional, Kernan, Kate F., additional, Key, Daniel A., additional, Kirchoff, Katie, additional, Krantz, Ian D., additional, Kraska, Detlef, additional, Krishnamurthy, Ashok K., additional, L'Yi, Sehi, additional, Le, Trang T., additional, Leblanc, Judith, additional, Lemaitre, Guillaume, additional, Lenert, Leslie, additional, Leprovost, Damien, additional, Liu, Molei, additional, Will Loh, Ne Hooi, additional, Long, Qi, additional, Lozano-Zahonero, Sara, additional, Lynch, Kristine E., additional, Mahmood, Sadiqa, additional, Makwana, Simran, additional, Mandl, Kenneth D., additional, Mao, Chengsheng, additional, Maram, Anupama, additional, Maripuri, Monika, additional, Martel, Patricia, additional, Martins, Marcelo R., additional, Marwaha, Jayson S., additional, Masino, Aaron J., additional, Mazzitelli, Maria, additional, Mazzotti, Diego R., additional, Mensch, Arthur, additional, Milano, Marianna, additional, Minicucci, Marcos F., additional, Ahooyi, Taha Mohseni, additional, Moore, Jason H., additional, Moraleda, Cinta, additional, Morris, Jeffrey S., additional, Moshal, Karyn L., additional, Mousavi, Sajad, additional, Murad, Douglas A., additional, Murphy, Shawn N., additional, Naughton, Thomas P., additional, Breda Neto, Carlos Tadeu, additional, Newburger, Jane, additional, Njoroge, Wanjiku F.M., additional, Norman, James B., additional, Obeid, Jihad, additional, Okoshi, Marina P., additional, Olson, Karen L., additional, Orlova, Nina, additional, Ostasiewski, Brian D., additional, Palmer, Nathan P., additional, Paris, Nicolas, additional, Pedrera-Jiménez, Miguel, additional, Pfaff, Ashley C., additional, Pfaff, Emily R., additional, Pillion, Danielle, additional, Pizzimenti, Sara, additional, Priya, Tanu, additional, Prokosch, Hans U., additional, Prudente, Robson A., additional, Prunotto, Andrea, additional, Quirós-González, Víctor, additional, Ramoni, Rachel B., additional, Raskin, Maryna, additional, Rieg, Siegbert, additional, Roig-Domínguez, Gustavo, additional, Rojo, Pablo, additional, Rubio-Mayo, Paula, additional, Sacchi, Paolo, additional, Sáez, Carlos, additional, Salamanca, Elisa, additional, Sanchez-Pinto, L. Nelson, additional, Sandrin, Arnaud, additional, Santhanam, Nandhini, additional, Santos, Janaina C.C., additional, Sanz Vidorreta, Fernando J., additional, Savino, Maria, additional, Schubert, Petra, additional, Schuettler, Juergen, additional, Scudeller, Luigia, additional, Sebire, Neil J., additional, Serrano-Balazote, Pablo, additional, Serre, Patricia, additional, Serret-Larmande, Arnaud, additional, Shah, Mohsin, additional, Hossein Abad, Zahra Shakeri, additional, Silvio, Domenick, additional, Sliz, Piotr, additional, Son, Jiyeon, additional, Sonday, Charles, additional, Sperotto, Francesca, additional, Spiridou, Anastasia, additional, Strasser, Zachary H., additional, Tan, Byorn W.L., additional, Tanni, Suzana E., additional, Taylor, Deanne M., additional, Terriza-Torres, Ana I., additional, Toh, Emma M.S., additional, Torti, Carlo, additional, Trecarichi, Enrico M., additional, Vallejos, Andrew K., additional, Varoquaux, Gael, additional, Vella, Margaret E., additional, Vie, Jill-Jênn, additional, Vitacca, Michele, additional, Wagholikar, Kavishwar B., additional, Waitman, Lemuel R., additional, Wang, Xuan, additional, Wassermann, Demian, additional, Wolkewitz, Martin, additional, Wong, Scott, additional, Xiong, Xin, additional, Ye, Ye, additional, Yehya, Nadir, additional, Zachariasse, Joany M., additional, Zahner, Janet J., additional, Zambelli, Alberto, additional, Zuccaro, Valentina, additional, and Zucco, Chiara, additional

Published
2023
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25. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Author

Weber, Griffin, Hong, Chuan, Xia, Zongqi, Palmer, Nathan, Avillach, Paul, L’yi, Sehi, Keller, Mark, Murphy, Shawn, Gutiérrez-Sacristán, Alba, Bonzel, Clara-Lea, Serret-Larmande, Arnaud, Neuraz, Antoine, Omenn, Gilbert, Visweswaran, Shyam, Klann, Jeffrey, South, Andrew, Loh, Ne Hooi Will, Cannataro, Mario, Beaulieu-Jones, Brett, Bellazzi, Riccardo, Agapito, Giuseppe, Alessiani, Mario, Aronow, Bruce, Bell, Douglas, Benoit, Vincent, Bourgeois, Florence, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, Duvall, Scott, Barrio, Noelia García, Hanauer, David, Ho, Yuk-Lam, Holmes, John, Issitt, Richard, Liu, Molei, Luo, Yuan, Lynch, Kristine, Maidlow, Sarah, Malovini, Alberto, Mandl, Kenneth, Mao, Chengsheng, Matheny, Michael, Moore, Jason, Morris, Jeffrey, Morris, Michele, Mowery, Danielle, Ngiam, Kee Yuan, Patel, Lav, Pedrera Jiménez, Miguel, Ramoni, Rachel, Schriver, Emily, Schubert, Petra, Balazote, Pablo Serrano, Spiridou, Anastasia, Tan, Amelia, Tan, Byorn, Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico, Wang, Xuan, Aaron, James, Albayrak, Adem, Albi, Giuseppe, Balshi, James, Alloni, Anna, Amendola, Danilo, Angoulvant, François, Anthony, Li, Ashraf, Fatima, Atz, Andrew, Azevedo, Paula, Bellasi, Antonio, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Boeker, Martin, Booth, John, Bosari, Silvano, Bradford, Robert, Brat, Gabriel, Bréant, Stéphane, Brown, Nicholas, Bruno, Raffaele, Bryant, William, Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Krista, Chen, Jin, Chiudinelli, Lorenzo, Cimino, James, Colicchio, Tiago, Cormont, Sylvie, Cossin, Sébastien, Craig, Jean, Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Garmire, Lana, Dionne, Audrey, Duan, Rui, Dubiel, Julien, Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert, Ganslandt, Thomas, García-Barrio, Noelia, Gehlenborg, Nils, Getzen, Emily, Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Han, Larry, Haverkamp, Christian, Key, Daniel, Hazard, Derek, He, Bing, Henderson, Darren, Hilka, Martin, Huling, Kenneth, Hutch, Meghan, Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Kennedy, Chris, Kernan, Kate, Kirchoff, Katie, Kohane, Isaac, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, Le, Trang, Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Long, Qi, Lozano-Zahonero, Sara, Mahmood, Sadiqa, Makoudjou, Adeline, Maram, Anupama, Martel, Patricia, Martins, Marcelo, Marwaha, Jayson, Masino, Aaron, Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos, Moal, Bertrand, Ahooyi, Taha Mohseni, Moraleda, Cinta, Moshal, Karyn, Mousavi, Sajad, Murad, Douglas, Naughton, Thomas, Neto, Carlos Tadeu Breda, Newburger, Jane, Njoroge, Wanjiku, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Orlova, Nina, Ostasiewski, Brian, Paris, Nicolas, Pedrera-Jiménez, Miguel, Pfaff, Ashley, Pfaff, Emily, Pillion, Danielle, Pizzimenti, Sara, Prokosch, Hans, Prudente, Robson, Prunotto, Andrea, Quirós-González, Víctor, Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina, Sanz Vidorreta, Fernando, Savino, Maria, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil, Serrano-Balazote, Pablo, Serre, Patricia, Shah, Mohsin, Abad, Zahra Shakeri Hossein, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, Sperotto, Francesca, Strasser, Zachary, Tan, Bryce, Tanni, Suzana, Taylor, Deanne, Terriza-Torres, Ana, Tippmann, Patric, Toh, Emma, Tseng, Yi-Ju, Vallejos, Andrew, Varoquaux, Gael, Vella, Margaret, Verdy, Guillaume, Vie, Jill-Jênn, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wassermann, Demian, Wolkewitz, Martin, Wong, Scott, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison, Zöller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Harvard Medical School [Boston] (HMS), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Massachusetts General Hospital [Boston], 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), 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), University of Michigan [Ann Arbor], University of Michigan System, Wake Forest School of Medicine [Winston-Salem], Wake Forest Baptist Medical Center, National University Health System [Singapore] (NUHS), Università degli Studi 'Magna Graecia' di Catanzaro = University of Catanzaro (UMG), Università degli Studi di Pavia = University of Pavia (UNIPV), Istituti Clinici Scientifici Maugeri [Pavia] (IRCCS Pavia - ICS Maugeri), ASST Pavia, University of Cincinnati (UC), University of California [Los Angeles] (UCLA), University of California (UC), VA Boston Healthcare System, Hospital Universitario 12 de Octubre [Madrid], University of Pennsylvania, Great Ormond Street Hospital for Children [London] (GOSH), Harvard School of Public Health, Northwestern University [Chicago, Ill. USA], VA Salt Lake City Health Care System, Boston Children's Hospital, University of Kansas [Kansas City], and National University Hospital [Singapore] (NUH)

Subjects
Health Information Management, Medicine (miscellaneous), Health Informatics, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Computer Science Applications
Abstract

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Published
2022

26. Propagating Soft Table Constraints

Author

Lecoutre, Christophe, Paris, Nicolas, Roussel, Olivier, Tabary, Sébastien, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Milano, Michela, editor

Published
2012
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27. 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

28. Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study

Author

Lamer, Antoine, primary, Fruchart, Mathilde, additional, Paris, Nicolas, additional, Popoff, Benjamin, additional, Payen, Anaïs, additional, Balcaen, Thibaut, additional, Gacquer, William, additional, Bouzillé, Guillaume, additional, Cuggia, Marc, additional, Doutreligne, Matthieu, additional, and Chazard, Emmanuel, additional

Published
2022
Full Text
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29. 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

31. 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

32. Evolving phenotypes of non-hospitalized patients that indicate long COVID

Author

ESTIRI, Hossein, Strasser, Zachary, Brat, Gabriel, Semenov, Yevgeniy, Patel, Chirag, Murphy, Shawn, Aaron, James, Agapito, Giuseppe, Albayrak, Adem, Alessiani, Mario, Amendola, Danilo, Anthony, Li, Aronow, Bruce, Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Balshi, James, Beaulieu-Jones, Brett, Bell, Douglas, Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Sobrino, José Luis Bernal, Bernaux, Mélodie, Bey, Romain, Martínez, Alvar Blanco, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence, Bradford, Robert, Bréant, Stéphane, Brown, Nicholas, Bryant, William, Bucalo, Mauro, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista, Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James, Colicchio, Tiago, Cormont, Sylvie, COSSIN, Sébastien, Craig, Jean, Bermúdez, Juan Luis Cruz, Rojo, Jaime Cruz, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Davoudi, Anahita, Devkota, Batsal, Dubiel, Julien, Esteve, Loic, Fan, Shirley, Follett, Robert, Gaiolla, Paula, Ganslandt, Thomas, Barrio, Noelia García, Garmire, Lana, Gehlenborg, Nils, GEVA, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Hanauer, David, Haverkamp, Christian, He, Bing, Henderson, Darren, Hilka, Martin, Holmes, John, Hong, Chuan, Horki, Petar, Huling, Kenneth, HUTCH, Meghan, Issitt, Richard, Jannot, Anne Sophie, Jouhet, Vianney, Keller, Mark, Kirchoff, Katie, Klann, Jeffrey, Kohane, Isaac, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, L’Yi, Sehi, Le, Trang, Leblanc, Judith, Leite, Andressa, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, LOH, Ne Hooi Will, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine, Mahmood, Sadiqa, Maidlow, Sarah, Malovini, Alberto, Mandl, Kenneth, Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Masino, Aaron, Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos, Moal, Bertrand, Moore, Jason, Moraleda, Cinta, Morris, Jeffrey, MORRIS, Michele, Moshal, Karyn, Mousavi, Sajad, Mowery, Danielle, Murad, Douglas, Naughton, Thomas, Neuraz, Antoine, Ngiam, Kee Yuan, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Omenn, Gilbert, Orlova, Nina, Ostasiewski, Brian, Palmer, Nathan, Paris, Nicolas, Patel, Lav, Jimenez, Miguel Pedrera, Pfaff, Emily, Pillion, Danielle, Prokosch, Hans, Prudente, Robson, González, Víctor Quirós, Ramoni, Rachel, Raskin, Maryna, RIEG, Siegbert, Domínguez, Gustavo Roig, Rojo, Pablo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi, Sandrin, Arnaud, Santos, Janaina, Savino, Maria, SCHRIVER, Emily, Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil, Balazote, Pablo Serrano, Serre, Patricia, Serret-Larmande, Arnaud, Shakeri, Zahra, Silvio, Domenick, Sliz, Piotr, SON, Jiyeon, Sonday, Charles, South, Andrew, Spiridou, Anastasia, Tan, Amelia, 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, Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wang, Xuan, Wassermann, Demian, Weber, Griffin, XIA, Zongqi, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison, Zoeller, Daniel, Zucco, Chiara, Massachusetts General Hospital [Boston], Harvard Medical School [Boston] (HMS), Service d'informatique médicale et biostatistiques [CHU Necker], 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 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), 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 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), Université Paris Cité (UPC), This work was supported by the National Human Genome Research Institute grant 3U01HG008685-05S2 and the National Library of Medicine grant T15LM007092., 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), and Université Paris Cité (UPCité)

Subjects
medicine.medical_specialty, Neurological disorder, Chest pain, MESH: Phenotype, Article, 03 medical and health sciences, 0302 clinical medicine, Post-Acute COVID-19 Syndrome, Diabetes mellitus, Internal medicine, Machine learning, medicine, Chronic fatigue syndrome, Humans, Electronic health records, Post-acute sequelae of SARS-CoV-2, MESH: COVID-19, 030304 developmental biology, Retrospective Studies, 0303 health sciences, MESH: Humans, business.industry, Medical record, Type 2 Diabetes Mellitus, COVID-19, Retrospective cohort study, MESH: Retrospective Studies, General Medicine, medicine.disease, 3. Good health, Dysgeusia, Phenotypes, Phenotype, Medicine, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, medicine.symptom, business, 030217 neurology & neurosurgery, Research Article, Cohort study
Abstract

Background For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection. Methods In this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3–6 and 6–9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized. Results We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients’ medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94–3.46]), alopecia (OR 3.09, 95% CI [2.53–3.76]), chest pain (OR 1.27, 95% CI [1.09–1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22–2.10]), shortness of breath (OR 1.41, 95% CI [1.22–1.64]), pneumonia (OR 1.66, 95% CI [1.28–2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22–1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65. Conclusions The findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults.

Published
2021

35. 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

36. Comparison of pesticide residue and specific nutrient levels in peeled and unpeeled apples.

Author

Lecerf, Jean‐Michel, Périquet, Alain, Carlin, Frédéric, Lanckriet, Suzanne, Paris, Nicolas, Robaglia, Christophe, Gleizer, Bethsabée, Belzunces, Luc, Cravedi, Jean‐Pierre, and Calvarin, Johanna

Subjects
PESTICIDE residues in food, PESTICIDE pollution, PESTICIDES, NUTRITIONAL value, FOOD consumption, VITAMIN C, APPLES
Abstract

BACKGROUND: Studies have shown that the consumption of apples has a beneficial effect on cardiovascular diseases and some cancers, largely as a result of their micronutrient and phytoconstituent contents. Apple peel not only contains more polyphenols than the flesh, but also is likely to contain pesticide residues. The present study aimed to compare the contents of certain micronutrients and residual pesticide levels in peeled and unpeeled apples. RESULTS: Peeled apples contained fewer pesticide residues at lower concentrations than unpeeled apples. However, whether samples were peeled or not, the exposure values for pesticide residues in apples never exceeded the acceptable daily intake (ADI), but ranged between 0.04% and 2.10% of the ADI in adults for food intake estimated at the 95th percentile (277 g per person per day). Determination of polyphenol, fibre, magnesium and vitamin C levels showed that the nutritional differences observed between peeled and unpeeled apples were marginal. CONCLUSION: The consumption of apples, such as the apples tested in the present study, results in an exposure to pesticides that is low for unpeeled apples, and lower for peeled apples. Moreover, there was no significant loss of nutritional value from eating peeled apples based on the nutrients investigated. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Published
2023
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38. International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries

Author

Bourgeois, Florence, Gutiérrez-Sacristán, Alba, Keller, Mark, Liu, Molei, Hong, Chuan, Bonzel, Clara-Lea, Tan, Amelia, Aronow, Bruce, Boeker, Martin, Booth, John, Cruz Rojo, Jaime, Devkota, Batsal, García Barrio, Noelia, Gehlenborg, Nils, Geva, Alon, Hanauer, David, Hutch, Meghan, Issitt, Richard, Klann, Jeffrey, Luo, Yuan, Mandl, Kenneth, Mao, Chengsheng, Moal, Bertrand, Moshal, Karyn, Murphy, Shawn, Neuraz, Antoine, Ngiam, Kee Yuan, Omenn, Gilbert, Patel, Lav, Jiménez, Miguel Pedrera, Sebire, Neil, Balazote, Pablo Serrano, Serret-Larmande, Arnaud, South, Andrew, Spiridou, Anastasia, Taylor, Deanne, Tippmann, Patric, Visweswaran, Shyam, Weber, Griffin, Kohane, Isaac, Cai, Tianxi, Avillach, Paul, Cruz-Rojo, Jaime, García-Barrio, Noelia, Pedrera-Jiménez, Miguel, Serrano-Balazote, Pablo, Aaron, James, Agapito, Giuseppe, Albayrak, Adem, Alessiani, Mario, Amendola, Danilo, Angoulvant, François, Anthony, Li Llj, Atz, Andrew, Balshi, James, Beaulieu-Jones, Brett, Bell, Douglas, Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Blanco Martínez, Alvar, Bosari, Silvano, 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, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Davoudi, Anahita, Dubiel, Julien, Duvall, Scott, Esteve, Loic, Fan, Shirley, Follett, Robert, Gaiolla, Paula Sa, Ganslandt, Thomas, Garmire, Lana, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Haverkamp, Christian, He, Bing, Henderson, Darren, Hilka, Martin, Holmes, John, Horki, Petar, Huling, Kenneth, Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Kirchoff, Katie, Krantz, Ian, Kraska, Detlef, Krishnamurthy, Ashok, L'Yi, Sehi, Le, Trang, Leblanc, Judith, Leite, Andressa Rr, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Loh, Ne Hooi Will, Lynch, Kristine, Mahmood, Sadiqa, Maidlow, Sarah, Malovini, Alberto, Maram, Anupama, Martel, Patricia, Masino, Aaron, Matheny, Michael, Maulhardt, Thomas, Mazzitelli, Maria, Mcduffie, Michael, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos, Moore, Jason, Moraleda, Cinta, Morris, Jeffrey, Morris, Michele, Mousavi, Sajad, Mowery, Danielle, Murad, Douglas, Naughton, Thomas, Norman, James, Obeid, Jihad, Okoshi, Marina, Olson, Karen, Orlova, Nina, Ostasiewski, Brian, Palmer, Nathan, Paris, Nicolas, 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, Samayamuthu, Malarkodi, Sandrin, Arnaud, Santos, Janaina Cc, Savino, Maria, Schriver, Emily, Schuettler, Juergen, Scudeller, Luigia, Serre, Patricia, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, Tan, Bryce Wq, Tan, Byorn Wl, Tanni, Suzana, Terriza Torres, Ana, Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico, Tseng, Yi-Ju, Vallejos, Andrew, Varoquaux, Gael, Vie, Jill-Jênn, Vitacca, Michele, Wagholikar, Kavishwar, Waitman, Lemuel, Wassermann, Demian, William, Yuan, Xia, Zongqi, Yehya, Nadir, Zambelli, Alberto, Zhang, Harrison, Zucco, Chiara, 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), 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), Dr Bourgeois was funded by a grant from the Burroughs Wellcome Fund and supported by the Harvard-MIT Center for Regulatory Science. Mr Keller was funded by grant 5T32HG002295-18 from the National Human Genome Research Institute (NHGRI). Dr Aronow was funded by grant U24 HL148865 from the National Heart, Lung, and Blood Institute (NHLBI). Ms García Barrio was supported by grant PI18/00981 from the Carlos III Health Institute. Dr Gehlenborg was funded by grant T15 LM007092 from the NIH National Library of Medicine. Dr Geva was funded by grant K12 HD047349 from the NIH and Eunice Kennedy Shriver National Institute of Child Health and Human Development. Dr Hanauer was funded by grant UL1TR002240 from the National Center for Advancing Translational Sciences (NCATS). Drs Klann and Murphy were funded by grant 5UL1TR001857-05 from the NCATS and grant 5R01HG009174-04 from the NHGRI. Dr Luo was funded by grant R01LM013337 from the NLM. Mr Patel was funded by grant UL1TR002366 from the NCATS. Dr Gutiérrez-Sacristán was funded by grants K23HL148394 and L40HL148910 from the NIH NHLBI and grant UL1TR001420 from the NIH NCATS. Dr Visweswaran was funded by grant R01LM012095 from the NLM and grant UL1TR001857 from the NCATS. Dr Weber was supported by grants UL1TR002541 and UL1TR000005 from the NIH-NCATS, and grant R01LM013345 from the NLM., CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), 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), 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
medicine.medical_specialty, MESH: Pandemics, education, Health Informatics, MESH: Global Health, MESH: Hospitalization, Procalcitonin, 03 medical and health sciences, 0302 clinical medicine, 030225 pediatrics, Internal medicine, MESH: Child, Epidemiology, medicine, Infection control, MESH: COVID-19, MESH: SARS-CoV-2, 030212 general & internal medicine, health care economics and organizations, MESH: Electronic Health Records, Original Investigation, MESH: Adolescent, Disease surveillance, MESH: Humans, business.industry, Research, MESH: Infant, Newborn, MESH: Child, Preschool, Retrospective cohort study, MESH: Retrospective Studies, General Medicine, medicine.disease, MESH: Infant, MESH: Male, 3. Good health, Online Only, Respiratory failure, Viral pneumonia, Cohort, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, MESH: Female
Abstract

This cohort study aims to describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19., Key Points Question What are international trends in hospitalizations for children and youth with SARS-CoV-2, and what are the epidemiological and clinical features of these patients? Findings This cohort study of 671 children and youth found discrete surges in hospitalizations with variable trends and timing across countries. Common complications included cardiac arrhythmias and viral pneumonia, and laboratory findings included elevations in markers of inflammation and abnormalities of coagulation; few children and youth were treated with medications directed specifically at SARS-CoV-2. Meaning These findings suggest large-scale informatics-based approaches used to incorporate electronic health record data across health care systems can provide an efficient source of information to monitor disease activity and define epidemiological and clinical features of pediatric patients hospitalized with SARS-CoV-2 infections., Importance Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients. Objective To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19. Design, Setting, and Participants This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study. Main Outcomes and Measures Patient characteristics, clinical features, and medication use. Results There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study’s cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19–directed medications. Conclusions and Relevance This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.

Published
2021

40. Paradoxical reactions and biologic agents: a French cohort study of 9303 patients.

Author

Bataille, Pauline, Layese, Richard, Claudepierre, Pascal, Paris, Nicolas, Dubiel, Julien, Amiot, Aurélien, and Sbidian, Emilie

Subjects
BIOLOGICALS, INFLAMMATORY bowel diseases, ANTIRHEUMATIC agents, COHORT analysis, NATURAL language processing, IMMUNE reconstitution inflammatory syndrome
Abstract

Background: Paradoxical reactions (PRs) are defined as the occurrence during biologic therapy of a pathological condition that usually responds to these drugs. Objectives: To estimate the incidence of PRs and identify risk factors. Methods: Multicentre study of the database for the Greater Paris University Hospitals, including biologic‐naive patients receiving anti‐tumour necrosis factor‐α, anti‐interleukin‐12/23, anti‐interleukin‐17 or anti‐α4β7‐integrin agents for psoriasis, inflammatory rheumatism or inflammatory bowel disease (IBD). We used natural language processing algorithms to extract data. A cohort and a case–control study nested in the cohort with controls selected by incidence density sampling was used to identify risk factors. Results: Most of the 9303 included patients (median age 43·0, 53·8% women) presented an IBD (3773, 40·6%) or a chronic inflammatory rheumatic disease (3708, 39·9%), and 8489 (91·3%) received anti‐TNF‐α agents. A total of 297 (3·2%) had a PR. The global incidence rate was 7·6 per 1000 person‐years [95% confidence interval (CI) 6·8–8·5]. The likelihood of PR was associated with IBD [adjusted odds ratio (aOR) 1·9, 95% CI 1·1–3·2, P = 0·021] and a combination of at least two inflammatory diseases (aOR 6·1, 95% CI 3·6–10·6, P < 0·001) and was reduced with conventional synthetic disease‐modifying antirheumatic drugs (csDMARDs) and corticosteroids (aOR 0·6, 95% CI 0·4–0·8, P = 0·002; and OR 0·4, 95% CI 0·2–0·6, P = 0·002, respectively). Conclusions: The likelihood of PRs was associated with IBD or a combination of a least two inflammatory diseases. More studies are needed to assess the benefit of systematically adding csDMARDs for such high‐risk patients. What is already known about this topic?Most published studies about paradoxical reactions concern paradoxical psoriasis in patients receiving anti‐tumour necrosis factor‐α agents.Few data are available for other paradoxical reactions and the most recent biologics. What does this study add?Risk of paradoxical reactions was increased with inflammatory bowel disease and a combination of at least two inflammatory diseases.Risk of paradoxical reactions was reduced with conventional synthetic disease‐modifying antirheumatic drugs or corticosteroid therapy, which could be added for high‐risk patients. [ABSTRACT FROM AUTHOR]

Published
2022
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41. Rapid Improvement after Starting Elexacaftor–Tezacaftor–Ivacaftor in Patients with Cystic Fibrosis and Advanced Pulmonary Disease

Author

Burgel, Pierre-Régis, primary, Durieu, Isabelle, additional, Chiron, Raphaël, additional, Ramel, Sophie, additional, Danner-Boucher, Isabelle, additional, Prevotat, Anne, additional, Grenet, Dominique, additional, Marguet, Christophe, additional, Reynaud-Gaubert, Martine, additional, Macey, Julie, additional, Mely, Laurent, additional, Fanton, Annlyse, additional, Quetant, Sébastien, additional, Lemonnier, Lydie, additional, Paillasseur, Jean-Louis, additional, Da Silva, Jennifer, additional, Martin, Clémence, additional, Andrejak, Claire, additional, Becourt, Arnaud, additional, Mounard, Julie, additional, Poulet, Claire, additional, Rames, Cinthia, additional, Talleux, Marie, additional, Chevalier, Marie-Chantal, additional, Darviot, Estelle, additional, Jouvenot, Marie, additional, Marien, Caroline, additional, Paris, Audrey, additional, Pelatan, Cécile, additional, Person, Christine, additional, Priou, Pascaline, additional, Troussier, Françoise, additional, Urban, Thierry, additional, Dalphin, Marie-Laure, additional, Dalphin, Jean-Charles, additional, Ladaurade, Alice, additional, Pernet, Didier, additional, Richaud-Thiriez, Bénédicte, additional, Roux-Claude, Pauline, additional, Blanc, Nathalia, additional, Boisserie-Lacroix, Vincent, additional, Bui, Stephanie, additional, Collet, Cyrielle, additional, Debelleix, Stéphane, additional, Bergot, Emmanuel, additional, Brouard, Jacques, additional, Campbell, Karine, additional, Laurans, Muriel, additional, Ribault, Virginie, additional, Borderon, Corinne, additional, Heraud, Marie-Christine, additional, Labbe, Guillaume, additional, Montcouquiol, Sylvie, additional, Petit, Isabelle, additional, Ruivard, Marc, additional, Delestrain, Céline, additional, Douvry, Benoit, additional, Epaud, Ralph, additional, Maitre, Bernard, additional, Remus, Natascha, additional, Beltramo, Guillaume, additional, Houzel, Anne, additional, Huet, Frédéric, additional, Perez, Stéphanie, additional, Boldron-Ghaddar, Amale, additional, Scalbert, Manuela, additional, Bouzioukh, Rabah, additional, Simon, Charles, additional, Camara, Boubou, additional, Hamidfar, Rébecca, additional, Llerena, Catherine, additional, Pin, Isabelle, additional, Deschildre, Antoine, additional, Gicquello, Alice, additional, Le Rouzic, Olivier, additional, Leroy, Clara, additional, Paris, Nicolas, additional, Perez, Thierry, additional, Thumerelle, Caroline, additional, Turck, Dominique, additional, Wizla, Nathalie, additional, Dupuy-Grasset, Magali, additional, Languepin, Jane, additional, Masson-Rouchaud, Alexandra, additional, Menetrey, Céline, additional, Durupt, Stéphane, additional, L’Excellent, Sophie, additional, Nove-Josserand, Raphaele, additional, Ohlmann, Camille, additional, Reix, Phillipe, additional, Reynaud, Quitterie, additional, Werck-Gallois, Marie-Christine, additional, Baravalle, Mélissandre, additional, Coltey, Bérangère, additional, Desmazes-Dufeu, Nadine, additional, Dubus, Jean-Christophe, additional, Gautier, Clarisse, additional, Rey, Jean-Baptiste, additional, Stremler, Nathalie, additional, Caimmi, Davide, additional, Devrait, Margot, additional, Moreau, Johan, additional, Billon, Yves, additional, Blondé, Aurore, additional, Guillaumot, Anne, additional, Kiefer, Sébastien, additional, Peretti, Laura, additional, Tatopoulos, Aurélie, additional, Tiotiu, Angélica, additional, Benhamida, Myriam, additional, Bihouee, Tiphaine, additional, Eschapasse, Emmanuel, additional, Tissot, Adrien, additional, Giannantonio, Marie, additional, Leroy, Sylvie, additional, Piccini-Bailly, Carole, additional, Pradelli, Johana, additional, Messika, Jonathan, additional, Boussaud, Véronique, additional, Burgel, Pierre-Régis, additional, Carlier, Nicolas, additional, Honoré, Isabelle, additional, Hubert, Dominique, additional, Kanaan, Reem, additional, Bailly-Botuha, Céline, additional, Chedevergne, Frédérique, additional, De Blic, Jacques, additional, Delacourt, Christophe, additional, Drummond, David, additional, Fauroux, Brigitte, additional, Karila, Chantal, additional, Le Bourgeois, Muriel, additional, Sermet, Isabelle, additional, Delaisi, Bertrand, additional, Gerardin, Michèle, additional, Houdouin, Veronique, additional, Leclainche, Laurence, additional, Aubertin, Guillaume, additional, Berdah, Laura, additional, Clement, Annick, additional, Corvol, Harriet, additional, Nathan, Nadia, additional, Prevost, Blandine, additional, Richard, Nicolas, additional, Tamalet, Aline, additional, Taytard, Jessica, additional, Thouvenin, Guillaume, additional, Tourniaire, Barbara, additional, Abely, Michel, additional, Bessaci-Kabouya, Katia, additional, Dury, Sandra, additional, Ravoninjatovo, Bruno, additional, Dabadie, Alain, additional, Dagorne, Michel, additional, Deneuville, Eric, additional, Jamin, Marie, additional, Ribault, Mélanie, additional, Vigier, Clémentine, additional, Belleguic, Chantal, additional, Brinchault, Graziella, additional, Desrues, Benoit, additional, Barzic, Audrey, additional, Dirou-Prigent, Anne, additional, Le Bihan, Jean, additional, Revert, Krista, additional, Ropars, Thomas, additional, Couderc, Laure, additional, Dominique, Stéphane, additional, Morisse-Pradier, Hélène, additional, Pramil, Stéphanie, additional, Thiberville, Luc, additional, Allou, Nathalie, additional, Enaud, Laurent, additional, Gachelin, Elsa, additional, Huchot, Eric, additional, Payet, Annabelle, additional, Perisson, Caroline, additional, Piyaraly, Saguiraly, additional, Valois, Sophie, additional, Herzog, Audrey, additional, Kessler, Romain, additional, Porzio, Michele, additional, Weiss, Laurence, additional, Beaumont, Laurence, additional, Brugiere, Olivier, additional, Colin, Sylvie, additional, Verdiere, de, additional, Cuquemelle, Elise, additional, De Miranda, Sandra, additional, Monem Hamid, Adbdul, additional, Parquin, François, additional, Picard, Clément, additional, Roux, Antoine, additional, Roy, Charlotte, additional, Bremont, François, additional, Didier, Alain, additional, Dupuis, Marion, additional, Faviez, Guillaume, additional, Labouret, Géraldine, additional, Mittaine, Marie, additional, Murris-Espin, Marlène, additional, Roditis, Léa, additional, Cosson, Laure, additional, Diot, Patrice, additional, Flament, Thomas, additional, Giraut, Charlotte, additional, Mankikian, Julie, additional, Arnouat, Baptiste, additional, Mousset, Gaétane, additional, Storni, Véronique, additional, Vigneron, Philippe, additional, Coirier-Duet, Emmanuelle, additional, and Gabsi, Asma, additional

Published
2021
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43. L’impact du dirigisme économique de Vichy sur le droit administratif

Author

Paris, Nicolas, STAR, ABES, Centre de Recherche et d'Etude en Droit et Science Politique [Dijon] (CREDESPO), Université de Bourgogne (UB)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Université Bourgogne Franche-Comté, Bernard Quiriny, and Yan Laidié

Subjects
[SHS.DROIT]Humanities and Social Sciences/Law, Droit administratif, [SHS.DROIT] Humanities and Social Sciences/Law, Dirigisme économique, Régime de Vichy, State-Directed economy, Vichy government, Administrative law
Abstract

To cope with the circumstances of the Second World War and to satisfy the new ideology in power, the Vichy government (1940-1944) implements a policy directing the economy which confuses the jurist. Facing the silence of the legislator and in order to determine the applicable law and the competent jurisdiction, the authors, both publicists and privatists, divide themselves as well as the judges, whether administrative or judicial, on the legal categories in which the realities of state-controlled economy should be classified. The conditions are favorable for a paradigmatic revolution in administrative law because no concept in this field seems able to withstand the shock of the events. However, the issues of qualifying the new economic realities are addressed in continuity. The hypothesis of a third (economic or professional) law appearing, with authority over management bodies and activities, is rejected. To support a sound administration of justice and to foster the necessary balance between public and private interests, the Conseil d’Etat, followed by the Cour de Cassation, confirms the value of traditional concepts and accepts only certain developments in definying them. Vichy’s State-directed economy thus reveals how strongly the traditional concepts of administrative law may resist and how its content, which is never definitively fixed, appears able to adjust to changes in social life. But above all, the phenomenon highlights the importance of past authority : jurists, conservative by nature, refuse to break with tradition. They prefer to maintain imperfect classifications, but of which they know the effects, rather than replacing them with new ones, which seem better, but whose real effectiveness they can doubt., Pour faire face aux circonstances de la Seconde guerre mondiale et satisfaire la nouvelle idéologie au pouvoir, le régime de Vichy (1940-1944) met en place une politique de direction de l’économie qui désempare le juriste. Dans le silence du législateur et afin de déterminer le droit applicable et la juridiction compétente, les auteurs, publicistes et privatistes, se divisent tout comme les juges, administratifs et judiciaires, sur les catégories juridiques dans lesquelles classer les réalités du dirigisme. Les conditions sont propices à une révolution paradigmatique du droit administratif car aucun des concepts de cette matière ne semble capable de résister au choc des événements. Pourtant, les questions de qualification sont résolues dans la continuité. L’hypothèse de l’apparition d’un troisième droit (économique ou professionnel), dont auraient relevé les organismes et les activités de direction, est rejetée. Au nom de la bonne administration de la justice et de la nécessité de préserver un équilibre entre les intérêts publics et privés, le Conseil d’Etat, suivi par la Cour de cassation, confirme la valeur des notions traditionnelles et n’accepte que certaines évolutions de définitions. Le dirigisme de Vichy révèle ainsi la force de résistance des concepts traditionnels du droit administratif, dont le contenu, jamais définitivement fixé, s’avère capable de s’adapter aux évolutions de la vie sociale. Mais le phénomène met surtout en lumière l’importance de l’autorité du passé : les juristes, conservateurs par nature, refusent de rompre avec la tradition. Ils préfèrent maintenir des classifications imparfaites, mais dont ils connaissent les effets, plutôt que de les remplacer par de nouvelles, d’apparence meilleures, mais dont ils peuvent douter de la véritable efficacité.

Published
2020

44. Accurate and reproducible prediction of ICU readmissions

Author

Nguyen, Dinh-Phong, Paris, Nicolas, and Parrot, Adrien

Subjects
Boosting (machine learning), Recall, business.industry, Computer science, Scoring methods, Benchmarking, After discharge, Machine learning, computer.software_genre, Intensive care unit, law.invention, law, Artificial intelligence, business, computer
Abstract

Readmission in the intensive care unit (ICU) is associated with poor clinical outcomes and high costs. Traditional scoring methods to help clinicians deciding whether a patient is ready for discharge have failed to meet expectations, paving the way for machine learning based approaches. Freely available datasets such as MIMIC-III have served as benchmarking media to compare such tools. We used the OMOP-CDM version of MIMIC-III (MIMIC-OMOP) to train and evaluate a lightweight tree boosting method to predict readmission in ICU at different time points after discharge (3, 7, 15 and 30 days), outperforming existing solutions with an AUROC of 0.802 (SD=0.011) and a recall of 0.837 (SD=0.016) for 3-days readmission.

Published
2019

45. AP-HP Health Data Space (AHDS) to the Test of the Covid-19 Pandemic.

Author

DANIEL, Christel, PARIS, Nicolas, PIERRE, Olivier, GRIFFON, Nicolas, BREANT, Stéphane, ORLOVA, Nina, SERRE, Patricia, LEPROVOST, Damien, DENGLOS, Stéphane, MOUCHET, Alexandre, DUBIEL, Julien, GOZLAN, Rafael, CHATELLIER, Gilles, BEY, Romain, FRANK, Marie, HASSEN-KHODJA, Claire, MAMZER, Marie-France, and HILKA, Martin

Abstract

Sharing observational and interventional health data within a common data space enables university hospitals to leverage such data for biomedical discovery and moving towards a learning health system. Objective: To describe the AP-HP Health Data Space (AHDS) and the IT services supporting piloting, research, innovation and patient care. Methods: Built on three pillars – governance and ethics, technology and valorization – the AHDS and its major component, the Clinical Data Warehouse (CDW) have been developed since 2015. Results: The AP-HP CDW has been made available at scale to AP-HP both healthcare professionals and public or private partners in January 2017. Supported by an institutional secured and high-performance cloud and an ecosystem of tools, mostly open source, the AHDS integrates a large amount of massive healthcare data collected during care and research activities. As of December 2021, the AHDS operates the electronic data capture for almost +840 clinical trials sponsored by AP-HP, the CDW is enabling the processing of health data from more than 11 million patients and generated +200 secondary data marts from IRB authorized research projects. During the Covid-19 pandemic, AHDS has had to evolve quickly to support administrative professionals and caregivers heavily involved in the reorganization of both patient care and biomedical research. Conclusion: The AP-HP Data Space is a key facilitator for data-driven evidence generation and making the health system more efficient and personalized. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Association between Hydroxyzine Use and Reduced Mortality in Patients Hospitalized for Coronavirus Disease 2019: Results from a multicenter observational study

Author

Hoertel, Nicolas, primary, Sánchez, Marina, additional, Vernet, Raphaël, additional, Beeker, Nathanaël, additional, Neuraz, Antoine, additional, Blanco, Carlos, additional, Olfson, Mark, additional, Lemogne, Cédric, additional, Meneton, Pierre, additional, Daniel, Christel, additional, Paris, Nicolas, additional, Gramfort, Alexandre, additional, Lemaitre, Guillaume, additional, Salamanca, Elisa, additional, Bernaux, Mélodie, additional, Bellamine, Ali, additional, Burgun, Anita, additional, and Limosin, Frédéric, additional

Published
2020
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