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IRIS
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. Copyright (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study
Dagliati, Arianna;Strasser, Zachary H.;Hossein Abad, Zahra Shakeri;Klann, Jeffrey G.;Wagholikar, Kavishwar B.;Mesa, Rebecca;Visweswaran, Shyam;Morris, Michele;Luo, Yuan;Henderson, Darren W.;Samayamuthu, Malarkodi Jebathilagam;Tan, Bryce W. Q.;Verdy, Guillame;Omenn, Gilbert S.;Xia, Zongqi;Bellazzi, Riccardo;Murphy, Shawn N.;Holmes, John H.;Estiri, Hossein;Aaron, James R.;Agapito, Giuseppe;Albayrak, Adem;Albi, Giuseppe;Alessiani, Mario;Alloni, Anna;Amendola, Danilo F.;François Angoulvant, null;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
2023-01-01
Abstract
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. Copyright (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1491980
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.