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702 results on '"Burgun, A."'

3. Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies

Author

Carole Faviez, Xiaoyi Chen, Nicolas Garcelon, Mohamad Zaidan, Katy Billot, Friederike Petzold, Hassan Faour, Maxime Douillet, Jean-Michel Rozet, Valérie Cormier-Daire, Tania Attié-Bitach, Stanislas Lyonnet, Sophie Saunier, and Anita Burgun

Subjects
Ciliopathy, Clinical decision support, Rare diseases, Electronic health record, Artificial intelligence, External evaluation, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Background There are approximately 8,000 different rare diseases that affect roughly 400 million people worldwide. Many of them suffer from delayed diagnosis. Ciliopathies are rare monogenic disorders characterized by a significant phenotypic and genetic heterogeneity that raises an important challenge for clinical diagnosis. Diagnosis support systems (DSS) applied to electronic health record (EHR) data may help identify undiagnosed patients, which is of paramount importance to improve patients’ care. Our objective was to evaluate three online-accessible rare disease DSSs using phenotypes derived from EHRs for the diagnosis of ciliopathies. Methods Two datasets of ciliopathy cases, either proven or suspected, and two datasets of controls were used to evaluate the DSSs. Patient phenotypes were automatically extracted from their EHRs and converted to Human Phenotype Ontology terms. We tested the ability of the DSSs to diagnose cases in contrast to controls based on Orphanet ontology. Results A total of 79 cases and 38 controls were selected. Performances of the DSSs on ciliopathy real world data (best DSS with area under the ROC curve = 0.72) were not as good as published performances on the test set used in the DSS development phase. None of these systems obtained results which could be described as “expert-level”. Patients with multisystemic symptoms were generally easier to diagnose than patients with isolated symptoms. Diseases easily confused with ciliopathy generally affected multiple organs and had overlapping phenotypes. Four challenges need to be considered to improve the performances: to make the DSSs interoperable with EHR systems, to validate the performances in real-life settings, to deal with data quality, and to leverage methods and resources for rare and complex diseases. Conclusion Our study provides insights into the complexities of diagnosing highly heterogenous rare diseases and offers lessons derived from evaluation existing DSSs in real-world settings. These insights are not only beneficial for ciliopathy diagnosis but also hold relevance for the enhancement of DSS for various complex rare disorders, by guiding the development of more clinically relevant rare disease DSSs, that could support early diagnosis and finally make more patients eligible for treatment.

Published
2024
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5. Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity

Author

Carole Faviez, Marc Vincent, Nicolas Garcelon, Olivia Boyer, Bertrand Knebelmann, Laurence Heidet, Sophie Saunier, Xiaoyi Chen, and Anita Burgun

Subjects
Diagnosis support, Electronic health record, Supervised machine learning, Semantic similarity, Imbalanced dataset, Rare disease, Medicine
Abstract

Abstract Background Rare diseases affect approximately 400 million people worldwide. Many of them suffer from delayed diagnosis. Among them, NPHP1-related renal ciliopathies need to be diagnosed as early as possible as potential treatments have been recently investigated with promising results. Our objective was to develop a supervised machine learning pipeline for the detection of NPHP1 ciliopathy patients from a large number of nephrology patients using electronic health records (EHRs). Methods and results We designed a pipeline combining a phenotyping module re-using unstructured EHR data, a semantic similarity module to address the phenotype dependence, a feature selection step to deal with high dimensionality, an undersampling step to address the class imbalance, and a classification step with multiple train-test split for the small number of rare cases. The pipeline was applied to thirty NPHP1 patients and 7231 controls and achieved good performances (sensitivity 86% with specificity 90%). A qualitative review of the EHRs of 40 misclassified controls showed that 25% had phenotypes belonging to the ciliopathy spectrum, which demonstrates the ability of our system to detect patients with similar conditions. Conclusions Our pipeline reached very encouraging performance scores for pre-diagnosing ciliopathy patients. The identified patients could then undergo genetic testing. The same data-driven approach can be adapted to other rare diseases facing underdiagnosis challenges.

Published
2024
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9. 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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10. 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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11. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortiumResearch in context

Author

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

Subjects
Multisystem inflammatory syndrome, Paediatric inflammatory multisystem syndrome, COVID-19, SARS-CoV-2, Variants, Pediatrics, Medicine (General), R5-920
Abstract

Summary: Background: Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infection. It remains unclear how MIS-C phenotypes vary across SARS-CoV-2 variants. We aimed to investigate clinical characteristics and outcomes of MIS-C across SARS-CoV-2 eras. Methods: We performed a multicentre observational retrospective study including seven paediatric hospitals in four countries (France, Spain, U.K., and U.S.). All consecutive confirmed patients with MIS-C hospitalised between February 1st, 2020, and May 31st, 2022, were included. Electronic Health Records (EHR) data were used to calculate pooled risk differences (RD) and effect sizes (ES) at site level, using Alpha as reference. Meta-analysis was used to pool data across sites. Findings: Of 598 patients with MIS-C (61% male, 39% female; mean age 9.7 years [SD 4.5]), 383 (64%) were admitted in the Alpha era, 111 (19%) in the Delta era, and 104 (17%) in the Omicron era. Compared with patients admitted in the Alpha era, those admitted in the Delta era were younger (ES −1.18 years [95% CI −2.05, −0.32]), had fewer respiratory symptoms (RD −0.15 [95% CI −0.33, −0.04]), less frequent non-cardiogenic shock or systemic inflammatory response syndrome (SIRS) (RD −0.35 [95% CI −0.64, −0.07]), lower lymphocyte count (ES −0.16 × 109/uL [95% CI −0.30, −0.01]), lower C-reactive protein (ES −28.5 mg/L [95% CI −46.3, −10.7]), and lower troponin (ES −0.14 ng/mL [95% CI −0.26, −0.03]). Patients admitted in the Omicron versus Alpha eras were younger (ES −1.6 years [95% CI −2.5, −0.8]), had less frequent SIRS (RD −0.18 [95% CI −0.30, −0.05]), lower lymphocyte count (ES −0.39 × 109/uL [95% CI −0.52, −0.25]), lower troponin (ES −0.16 ng/mL [95% CI −0.30, −0.01]) and less frequently received anticoagulation therapy (RD −0.19 [95% CI −0.37, −0.04]). Length of hospitalization was shorter in the Delta versus Alpha eras (−1.3 days [95% CI −2.3, −0.4]). Interpretation: Our study suggested that MIS-C clinical phenotypes varied across SARS-CoV-2 eras, with patients in Delta and Omicron eras being younger and less sick. EHR data can be effectively leveraged to identify rare complications of pandemic diseases and their variation over time. Funding: None.

Published
2023
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12. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort studyResearch in Context

Author

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

Subjects
Post-acute sequelae of SARS-CoV-2, PASC, COVID-19, SARS-CoV-2, Electronic health records, Medicine (General), R5-920
Abstract

Summary: Background: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. Methods: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. Findings: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. Interpretation: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. Funding: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.

Published
2023
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13. The genetic landscape and clinical spectrum of nephronophthisis and related ciliopathies

Author

Attié-Bitach, Tania, Comier-Daire, Valerie, Rozet, Jean-Michel, Frishberg, Yaacov, Llanas, Brigitte, Broyer, Michel, Mohsin, Nabil, Macher, Marie-Alice, Philip, Nicole, Baudouin, Véronique, Brackman, Damian, Loirat, Chantal, Charbit, Marina, Dehennault, Maud, Guyot, Claude, Bataille, Pierre, Elting, Mariet, Deschenes, Georges, Gropman, Andrea, Guest, Geneviève, Gagnadoux, Marie-France, Nicoud, Philippe, Cochat, Pierre, Ranchin, Bruno, Bensman, Albert, Guerrot, Anne-Marie, Knebelmann, Bertrand, Bilge, Ilmay, Bruno, Danièle, Burtey, Stéphane, Rouvière, Caroline Rousset, Caudwell, Valérie, Morin, Denis, Dollfus, Hélène, Maisin, Anne, Hamel, Christian, Bieth, Eric, Gie, Sophie, Goodship, Judith, Roussey, Gwenaelle, La Selve, Hermine, Nivet, Hubert, Bessenay, Lucie, Caillez, Mathilde, Palcoux, Jean Bernard, Benoît, Stéphane, Dubot, Philippe, Fila, Marc, Giuliano, Fabienne, Iftene, Daouya, Kessler, Michele, Kwon, Theresa, Lahoche, Anine, Laurent, Audrey, Leclerc, Anne-Laure, Milford, David, Neuhaus, Thomas, Odent, Sylvie, Eckart, Philippe, Chauveau, Dominique, Niaudet, Patrick, Repetto, Horacio, Taque, Sophie, Bruel, Alexandra, Noel-Botte, Alexandra, Launay, Emma Allain, Allard, Lisa, Anlicheau, Dany, Adra, Anne-Laure, Garnier, Arnaud, Nagra, Arvind, Baatard, Remy, Bacchetta, Justine, Sadikoglu, Banu, Barnerias, Christine, Barthelemy, Anne, Basel, Lina, Bassilios, Nader, Ben Maiz, Hedi, Ben Moussa, Fatma, Benmati, Faïza, Berthaud, Romain, Bertholet, Aurélia, Blanchier, Dominique, Boffa, Jean Jacques, Bouchireb, Karim, Bouhabel, Ihab, Boukerroucha, Zakaria, Bourdat-Michel, Guylhène, Boute, Odile, Brochard, Karine, Caumes, Roseline, Elalaoui, Siham Chafai, Chamontin, Bernard, Chastang, Marie Caroline, Pietrement, Christine, Richer, Christine, Legendre, Christophe, Dahan, Karin, Dalla-Vale, Fabienne, Thibaudin, Damien, Dauvergne, Maxime, Davourie, Salandre, Debeukelaer, Martin, Delbet, Jean Daniel, Deltas, Constantinos, Graber, Denis, Devillars, Nadège, Diouf, Boucar, Fenzy, Martine Doco, André, Jean-Luc, Joly, Dominique, Fryer, Alan, Albano, Laetitia, Cassuto, Elisabeth, Pincon, Aline, Medeira, Ana, Chaussenot, Annabelle, Mensire-Marinier, Anne, Bouissou, Francois, Decramer, Stephane, Bottani, Armand, Hummel, Aurélie, Karras, Alexandre, Katz, Avi, Azema, Christine, Janbon, Bénédicte, Roussel, Bernard, Bonniol, Claude, Mariat, Christiophe, Champion, Gérard, Chantreuil, Deborah, Chassaing, Nicolas, Mousson, Christiane, Baudeau, Christine, Cuntz, Delphine Hafdar, Mignot, Cyril, Dehoux, Laurene, Lacombe, Didier, Hannedouche, Thierry, Mérieau, Elodie, Charlin, Emmanuelle, Gauthier, Eric, Plasse, Florent, Faguer, Stanislas, Lebas, Fanny, Demurger, Florence, Emma, Francesco, Cartault, François, Dumont, Geneviève, Godefroid, Nathalie, Guigonis, Vincent, Hillaire, Sophie, Groothoff, Jaap, Dudley, Jan, Jourde-Chiche, Noémie, El Karoui, Khalil, Krid, Saoussen, Coudert, Krier, Bencheick, Larbi, Yver, Laurent, Lavocat, Marie-Pierre, De Sagazan, Le Monies, Leroy, Valerie, Thibaudin, Lise, Ingulli, Liz, Gwanmesia, Lorraine, Burglen, Lydie, Saïd-Menthon, Marie-Hélène, Carrera, Marta, Nizon, Mathilde, Melander, Catherine, Foulard, Michel, Blayo, Monique, Prinseau, Jacques, Jay, Nadine, Brun, Nathalie, Camille, Nicolas, Nobili, François, Devuyst, Olivier, Ben Brahim, Ouafa, Parvex, Paloma, Sabourin, Laurence Perrin, Blanc, Philippe, Vanhille, Philippe, Galichon, Pierre, Pierrepont, Sophie, Planquois, Vincent, Poussard, Gwenaelle, Noble, Claire Pouteil, Allal, Radia, Bernard, Raphaelle, Mounet, Raynaud, Cahen, Rémi, Touraine, Renaud, Rigothier, Claire, Ryckewaert, Amélie, Sacquepee, Mathieu, El Chehadeh, Salima, Samaille, Charlotte, Haq, Shuman, Simckes, Ari, Lanoiselée, Stéphanie, Tellier, Stephanie, Subra, Jean-François, Cloarec, Sylvie, Tenenbam, Julie, Lamy, Thomas, Garraud, Valérie Drouin, Valette, Huguette, Meyssonnier, Vanina, Vargas-Poussou, Rosa, Snajer, Yves, Durault, Sandrine, Plaisier, Emmanuelle, Berard, Etienne, Fakhouri, Fadi, Louillet, Ferielle, Finielz, Paul, Fischbach, Michel, Foliguet, Bernard, Francois-Pradier, Hélène, Garaix, Florentine, Gerard, Marion, Rizzoni, Gianfranco, Gilbert, Brigitte, Glotz, Denis, Dubrasquet, Astrid Godron, Grünfeld, Jean-Pierre, Bollee, Guillaume, Hall, Michelle, Hansson, Sverker, Haye, Damien, Taffin, Hélène, Hildebrandt, Friedhelm, Hourmand, Maryvonne, Kayserili, Hümya, Tack, Ivan, Jacquemont, Marie Line, Fabre-Teste, Jennifer, Kashtan, Cliff, Van Hoeck, Kkoen, Klein, Alexandre, Knefati, Yannick, Knoers, Nine, Konrad, Martin, Lachaux, Alain, Landru, Isabelle, Landthaler, Gilbert, Lang, Philippe, Le Pogamp, Patrick, Legris, Tristan, Didailler, Catherine, Lobbedez, Thierry, de Parscau, Loïc, Pinson, Lucile, Maheut, Hervé, Duval-Arnould, Marc, Rio, Marlène, Gubler, Marie-Claire, Merville, Pierre, Mestrallet, Guillaume, Meunier, Maite, Moreau, Karine, Harambat, Jérôme, Morgan, Graeme, Mourad, Georges, Stuber, Niksic, Boespflug-Tanguy, Odile, Dunand, Olivier, Niel, Olivier, Ouali, Nacera, Malvezzi, Paolo, Jaoude, Pauline Abou, Pelletier, Solenne, Peltier, Julie, Petersen, M.B., Michel, Philippe, Rémy, Philippe, Philit, Jean-Baptiste, Pichault, Valérie, Billette de Villemeur, Thierry, Boudailliez, Bernard, Leheup, Bruno, Dossier, Claire, Djeddi, Djamal-Dine, Berland, Yves, Hurault de Ligny, Bruno, Rigden, Susan, Robino, Christophe, Rossi, Annick, Sarnacki, Sabine, Saidani, Messaoud, Sartorius, Albane Brodin, Schäfer, Elise, Laszlo, Sztriha, Thouret, Marie-Christine, Thuillier-Lecouf, Angélique, Trachtman, Howard, Trivin, Claire, Tsimaratos, Michel, Van Damme-Lombaerts, Rita, Willems, Marjolaine, Youssef, Michel, Zaloszyc, Ariane, Zawodnik, Alexis, Ziliotis, Marie-Julia, Petzold, Friederike, Billot, Katy, Chen, Xiaoyi, Henry, Charline, Filhol, Emilie, Martin, Yoann, Avramescu, Marina, Douillet, Maxime, Morinière, Vincent, Krug, Pauline, Jeanpierre, Cécile, Tory, Kalman, Boyer, Olivia, Burgun, Anita, Servais, Aude, Salomon, Remi, Benmerah, Alexandre, Heidet, Laurence, Garcelon, Nicolas, Antignac, Corinne, Zaidan, Mohamad, and Saunier, Sophie

Published
2023
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14. 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

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

Author

Jenny Jia, Rachel Burgun, Alexa Reilly, Ross Sonnenblick, Lauren Fiechtner, Rachel M. Zack, Bianca Porneala, and Anne N. Thorndike

Subjects
Food pantries, Charitable food system, Nudge interventions, Healthy food choices, Public aspects of medicine, RA1-1270
Abstract

Abstract Background The Greater Boston Food Bank’s (GBFB) Healthy Pantry Program (HPP) is an online training that teaches food pantry staff to implement behavioral nudges (e.g., traffic-light nutrition labels, choice architecture) to promote healthier client choices. This study assessed if HPP was associated with healthier food bank orders by food pantries and identified implementation facilitators and barriers. Methods This mixed methods study collected quantitative data from a matched cohort of 10 HPP food pantries and 99 matched control food pantries in eastern Massachusetts that allow clients to choose their own food, and qualitative data from structured individual interviews with 8 HPP pantry staff. A difference-in-differences analysis compared changes in percentage of pantries’ food bank orders (by weight) of foods labeled green/yellow (healthier choices) and fresh produce from baseline to 6 and 10 months between HPP and control pantries. Interviews were coded for implementation facilitators and barriers. Results Before starting HPP, green-yellow ordering was 92.0% (SD 4.9) in control and 87.4% (SD 5.4) in HPP pantries. Participation in HPP was not associated with changes in green-yellow or fresh produce ordering at 6 or 10 months. HPP implementation facilitators included HPP training being accessible (sub-themes: customizable, motivating) and compatible with client-choice values. Barriers included resource limitations (sub-themes: staff shortage, limited space) and concerns about stigmatizing client food choices with use of labels for unhealthy foods. Conclusions An online program to help pantries promote healthier client choices was not associated with changes in how much healthy food pantries ordered from the food bank, suggesting it did not substantially change client choices. Implementation challenges and high baseline healthy ordering may have influenced HPP’s effectiveness.

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

Author

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

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

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

Published
2023
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20. 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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21. Mortality and vigour based indicators for an early diagnosis of vineyard decline

Author

Anne Merot, Guillaume Coulouma, Nathalie Smits, Elsa Robelot, Christian Gary, Lucia Guerin Dubrana, Jouanel Poulmarch, Xavier Burgun, Anne Pellegrino, and Marc Fermaud

Subjects
Agriculture (General), S1-972, Plant culture, SB1-1110
Abstract

Similar to the forestry industry, the winegrowing sector has experienced a grapevine decline phenomenon over the last twenty years, so that decline is now considered an increasingly widespread problem in many vineyards across the world (De la Fuente et al., 2016). In this work, the relationships between yield, mortality and vegetative vigour were investigated, in both temporal and spatial terms, to identify early diagnosis indicators of vine decline.

Published
2023
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26. A systemic approach to grapevine decline diagnosed using three key indicators: plant mortality, yield loss and vigour decrease

Author

Anne MEROT, Guillaume Coulouma, Nathalie Smits, Elsa Robelot, Christian Gary, Lucia Guerin-Dubrana, Jouanel Poulmach, Xavier Burgun, Anne Pellegrino, and Marc Fermaud

Subjects
decline, dieback, yield loss, vine mortality, vigour, diagnosis, Agriculture, Botany, QK1-989
Abstract

Grapevine decline, a major global viticulture issue, is defined as a multi-year decrease in vine productivity and/or increase in vine mortality. Although grapevine trunk diseases are one of the most-studied causes, decline is multifactorial and associated with more than 70 factors, including abiotic and biotic hazards. With so many factors to consider, the phenomenon difficult to understand, especially for winegrowers. Our study aims to make it easier to determine and assess grapevine decline by focusing on three key indicators: yield, mortality and vegetative vigour. We investigated the relationships between these indicators from both a temporal and spatial perspective to propose a set of diagnostic indicators. Thus, we conducted a winegrowers’ survey, an historical analysis of grapevine decline and field measurements of the abovementioned indicators on plot networks in three major French winegrowing regions (see graphical abstract): Bordeaux, Cognac and Languedoc. We found that farmers’ perceptions of decline were consistent with an objective characterisation of decline based on in-field measurements of the indicators. Although vine mortality progressively spread over the years, neither the survey nor the historical analysis showed a direct link between decline and yield loss. Rather, large yearly fluctuations in yield, which did not systematically decrease over time, account for this finding. As a result, the mortality rate and the normalised difference vegetation index (NDVI) indicators were shown to be earlier indicators of grapevine decline than yield loss (yield achievement ratio, YAR). We performed a multifactorial analysis of the overall data set from the three regions to deepen our understanding of the variety of declining situations and the underlying environmental and management factors contributing to decline. Finally, two ground-based NDVI indicators and an image-analysis methodology using aerial photographs were proposed as easy-to-obtain indicators of grapevine decline. NDVI indicators were linearly correlated to both YAR and mortality rate. This study provides a better understanding and promising tools for early diagnosis of grapevine decline.

Published
2023
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27. Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort studyResearch in context

Author

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

Subjects
COVID-19, Acute kidney injury, SARS-CoV-2, Chronic kidney disease, Electronic health records, Medicine (General), R5-920
Abstract

Summary: Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1–365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53–3.04, p

Published
2023
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28. A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

Author

Rosy Tsopra, Xose Fernandez, Claudio Luchinat, Lilia Alberghina, Hans Lehrach, Marco Vanoni, Felix Dreher, O.Ugur Sezerman, Marc Cuggia, Marie de Tayrac, Edvins Miklasevics, Lucian Mihai Itu, Marius Geanta, Lesley Ogilvie, Florence Godey, Cristian Nicolae Boldisor, Boris Campillo-Gimenez, Cosmina Cioroboiu, Costin Florian Ciusdel, Simona Coman, Oliver Hijano Cubelos, Alina Itu, Bodo Lange, Matthieu Le Gallo, Alexandra Lespagnol, Giancarlo Mauri, H.Okan Soykam, Bastien Rance, Paola Turano, Leonardo Tenori, Alessia Vignoli, Christoph Wierling, Nora Benhabiles, and Anita Burgun

Subjects
Artificial intelligence, Precision medicine, Personalized medicine, Computerized decision support systems, Cancer, Oncology, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Background Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European “ITFoC (Information Technology for the Future Of Cancer)” consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the “ITFoC Challenge”. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Published
2021
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31. Reorganisation of GP surgeries during the COVID-19 outbreak: analysis of guidelines from 15 countries

Author

Rosy Tsopra, Paul Frappe, Sven Streit, Ana Luisa Neves, Persijn J. Honkoop, Ana Belen Espinosa-Gonzalez, Berk Geroğlu, Tobias Jahr, Heidrun Lingner, Katarzyna Nessler, Gabriella Pesolillo, Øyvind Stople Sivertsen, Hans Thulesius, Raluca Zoitanu, Anita Burgun, and Shérazade Kinouani

Subjects
COVID-19, General Practitioner, Primary care, Clinical Practice Guidelines, Pandemic, Medicine (General), R5-920
Abstract

Abstract Background General practitioners (GPs) play a key role in managing the COVID-19 outbreak. However, they may encounter difficulties adapting their practices to the pandemic. We provide here an analysis of guidelines for the reorganisation of GP surgeries during the beginning of the pandemic from 15 countries. Methods A network of GPs collaborated together in a three-step process: (i) identification of key recommendations of GP surgery reorganisation, according to WHO, CDC and health professional resources from health care facilities; (ii) collection of key recommendations included in the guidelines published in 15 countries; (iii) analysis, comparison and synthesis of the results. Results Recommendations for the reorganisation of GP surgeries of four types were identified: (i) reorganisation of GP consultations (cancelation of non-urgent consultations, follow-up via e-consultations), (ii) reorganisation of GP surgeries (area partitioning, visual alerts and signs, strict hygiene measures), (iii) reorganisation of medical examinations by GPs (equipment, hygiene, partial clinical examinations, patient education), (iv) reorganisation of GP staff (equipment, management, meetings, collaboration with the local community). Conclusions We provide here an analysis of guidelines for the reorganisation of GP surgeries during the beginning of the COVID-19 outbreak from 15 countries. These guidelines focus principally on clinical care, with less attention paid to staff management, and the area of epidemiological surveillance and research is largely neglected. The differences of guidelines between countries and the difficulty to apply them in routine care, highlight the need of advanced research in primary care. Thereby, primary care would be able to provide recommendations adapted to the real-world settings and with stronger evidence, which is especially necessary during pandemics.

Published
2021
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32. Facing new challenges to informed consent processes in the context of translational research: the case in CARPEM consortium

Author

Elise Jacquier, Pierre Laurent-Puig, Cécile Badoual, Anita Burgun, and Marie-France Mamzer

Subjects
Dynamic consent, Informed consent, Translational research, Biobank research, Patient participation, Partnership in research, Medical philosophy. Medical ethics, R723-726
Abstract

Abstract Background In the context of translational research, researchers have increasingly been using biological samples and data in fundamental research phases. To explore informed consent practices, we conducted a retrospective study on informed consent documents that were used for CARPEM’s translational research programs. This review focused on detailing their form, their informational content, and the adequacy of these documents with the international ethical principles and participants’ rights. Methods Informed consent forms (ICFs) were collected from CARPEM investigators. A content analysis focused on information related to biological samples and data treatment (context of sampling and collect, aims, reuse, consent renewal), including the type of consent. An automatic assessment of the readability of the ICFs were performed with the IT program “Flesch Score”. Results 29 ICFs from 25 of 49 studies were analyzed after selection criteria were applied. Three types of consent were identified: 11 broad consents, six specific consents, and two opt-out consents. The Flesch Scores showed that most of the documents were too complex to be fully understood by most of the potential research participants. Most of the biological samples were collected during the healthcare routine, but the information content about secondary use of biological samples varied between ICFs. All documents mentioned personal data treatment but information about their reuse was not standardized in the ICFs. Conclusions Our review of current IC procedures of CARPEM showed that practices could be improved considering new translational research methods. “Old fashion written ICFs” should be adapted to the translational research approach, to better respect individual rights and international research ethics principles. In this context, theoretically, a digital tool allowing dynamic information and consent of participants, through an electronic interactive platform may be a good way to promote more active participation in research. Nevertheless, its feasibility in the complex environment of biological samples and data research remains to prove. The way of a combination of a broad consent followed by dynamic information may be alternatively tested.

Published
2021
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34. Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set

Author

Adrien Boukobza, Anita Burgun, Bertrand Roudier, and Rosy Tsopra

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

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

Published
2022
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38. International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

Author

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

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

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
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39. Diagnosis support systems for rare diseases: a scoping review

Author

Carole Faviez, Xiaoyi Chen, Nicolas Garcelon, Antoine Neuraz, Bertrand Knebelmann, Rémi Salomon, Stanislas Lyonnet, Sophie Saunier, and Anita Burgun

Subjects
Scoping review, Rare disease, Genetic diseases, Diagnosis, Clinical decision support, Artificial intelligence, Medicine
Abstract

Abstract Introduction Rare diseases affect approximately 350 million people worldwide. Delayed diagnosis is frequent due to lack of knowledge of most clinicians and a small number of expert centers. Consequently, computerized diagnosis support systems have been developed to address these issues, with many relying on rare disease expertise and taking advantage of the increasing volume of generated and accessible health-related data. Our objective is to perform a review of all initiatives aiming to support the diagnosis of rare diseases. Methods A scoping review was conducted based on methods proposed by Arksey and O’Malley. A charting form for relevant study analysis was developed and used to categorize data. Results Sixty-eight studies were retained at the end of the charting process. Diagnosis targets varied from 1 rare disease to all rare diseases. Material used for diagnosis support consisted mostly of phenotype concepts, images or fluids. Fifty-seven percent of the studies used expert knowledge. Two-thirds of the studies relied on machine learning algorithms, and one-third used simple similarities. Manual algorithms were encountered as well. Most of the studies presented satisfying performance of evaluation by comparison with references or with external validation. Fourteen studies provided online tools, most of which aimed to support the diagnosis of all rare diseases by considering queries based on phenotype concepts. Conclusion Numerous solutions relying on different materials and use of various methodologies are emerging with satisfying preliminary results. However, the variability of approaches and evaluation processes complicates the comparison of results. Efforts should be made to adequately validate these tools and guarantee reproducibility and explicability.

Published
2020
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40. Healthcare trajectory of children with rare bone disease attending pediatric emergency departments

Author

David Dawei Yang, Geneviève Baujat, Antoine Neuraz, Nicolas Garcelon, Claude Messiaen, Arnaud Sandrin, Gérard Cheron, Anita Burgun, Zagorka Pejin, Valérie Cormier-Daire, and François Angoulvant

Subjects
Rare disease/pathology, Bone disease/pathology, Healthcare delivery, Pediatric emergency medicine, Multiple chronic medical conditions, Medicine
Abstract

Abstract Background Children with rare bone diseases (RBDs), whether medically complex or not, raise multiple issues in emergency situations. The healthcare burden of children with RBD in emergency structures remains unknown. The objective of this study was to describe the place of the pediatric emergency department (PED) in the healthcare of children with RBD. Methods We performed a retrospective single-center cohort study at a French university hospital. We included all children under the age of 18 years with RBD who visited the PED in 2017. By cross-checking data from the hospital clinical data warehouse, we were able to trace the healthcare trajectories of the patients. The main outcome of interest was the incidence (IR) of a second healthcare visit (HCV) within 30 days of the index visit to the PED. The secondary outcomes were the IR of planned and unplanned second HCVs and the proportion of patients classified as having chronic medically complex (CMC) disease at the PED visit. Results The 141 visits to the PED were followed by 84 s HCVs, giving an IR of 0.60 [95% CI: 0.48–0.74]. These second HCVs were planned in 60 cases (IR = 0.43 [95% CI: 0.33–0.55]) and unplanned in 24 (IR = 0.17 [95% CI: 0.11–0.25]). Patients with CMC diseases accounted for 59 index visits (42%) and 43 s HCVs (51%). Multivariate analysis including CMC status as an independent variable, with adjustment for age, yielded an incidence rate ratio (IRR) of second HCVs of 1.51 [95% CI: 0.98–2.32]. The IRR of planned second HCVs was 1.20 [95% CI: 0.76–1.90] and that of unplanned second HCVs was 2.81 [95% CI: 1.20–6.58]. Conclusion An index PED visit is often associated with further HCVs in patients with RBD. The IRR of unplanned second HCVs was high, highlighting the major burden of HCVs for patients with chronic and severe disease.

Published
2020
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41. Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS)

Author

Ivan Lerner, Arnaud Serret-Larmande, Bastien Rance, Nicolas Garcelon, Anita Burgun, Laurent Chouchana, and Antoine Neuraz

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundPatients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. ObjectiveWith this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. MethodsWe developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. ResultsA total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.01) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively. ConclusionsOur innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.

Published
2022
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42. Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis

Author

Xiaoyi Chen, Carole Faviez, Marc Vincent, Luis Briseño-Roa, Hassan Faour, Jean-Philippe Annereau, Stanislas Lyonnet, Mohamad Zaidan, Sophie Saunier, Nicolas Garcelon, and Anita Burgun

Subjects
patient similarity, electronic health records, medical concept embedding, rare disease diagnosis, screening, ciliopathy, Therapeutics. Pharmacology, RM1-950
Abstract

A timely diagnosis is a key challenge for many rare diseases. As an expanding group of rare and severe monogenic disorders with a broad spectrum of clinical manifestations, ciliopathies, notably renal ciliopathies, suffer from important underdiagnosis issues. Our objective is to develop an approach for screening large-scale clinical data warehouses and detecting patients with similar clinical manifestations to those from diagnosed ciliopathy patients. We expect that the top-ranked similar patients will benefit from genetic testing for an early diagnosis. The dependence and relatedness between phenotypes were taken into account in our similarity model through medical concept embedding. The relevance of each phenotype to each patient was also considered by adjusted aggregation of phenotype similarity into patient similarity. A ranking model based on the best-subtype-average similarity was proposed to address the phenotypic overlapping and heterogeneity of ciliopathies. Our results showed that using less than one-tenth of learning sources, our language and center specific embedding provided comparable or better performances than other existing medical concept embeddings. Combined with the best-subtype-average ranking model, our patient-patient similarity-based screening approach was demonstrated effective in two large scale unbalanced datasets containing approximately 10,000 and 60,000 controls with kidney manifestations in the clinical data warehouse (about 2 and 0.4% of prevalence, respectively). Our approach will offer the opportunity to identify candidate patients who could go through genetic testing for ciliopathy. Earlier diagnosis, before irreversible end-stage kidney disease, will enable these patients to benefit from appropriate follow-up and novel treatments that could alleviate kidney dysfunction.

Published
2022
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46. A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

Author

Tsopra, Rosy, Fernandez, Xose, Luchinat, Claudio, Alberghina, Lilia, Lehrach, Hans, Vanoni, Marco, Dreher, Felix, Sezerman, O.Ugur, Cuggia, Marc, de Tayrac, Marie, Miklasevics, Edvins, Itu, Lucian Mihai, Geanta, Marius, Ogilvie, Lesley, Godey, Florence, Boldisor, Cristian Nicolae, Campillo-Gimenez, Boris, Cioroboiu, Cosmina, Ciusdel, Costin Florian, Coman, Simona, Hijano Cubelos, Oliver, Itu, Alina, Lange, Bodo, Le Gallo, Matthieu, Lespagnol, Alexandra, Mauri, Giancarlo, Soykam, H.Okan, Rance, Bastien, Turano, Paola, Tenori, Leonardo, Vignoli, Alessia, Wierling, Christoph, Benhabiles, Nora, and Burgun, Anita

Published
2021
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