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Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS
BACKGROUND: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Basel...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941629/ https://www.ncbi.nlm.nih.gov/pubmed/33688639 http://dx.doi.org/10.21203/rs.3.rs-279400/v1 |
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author | Prieto-Alhambra, Daniel Kostka, Kristin Duarte-Salles, Talita Prats-Uribe, Albert Sena, Anthony Pistillo, Andrea Khalid, Sara Lai, Lana Golozar, Asieh Alshammari, Thamir M Dawoud, Dalia Nyberg, Fredrik Wilcox, Adam Andryc, Alan Williams, Andrew Ostropolets, Anna Areia, Carlos Jung, Chi Young Harle, Christopher Reich, Christian Blacketer, Clair Morales, Daniel Dorr, David A. Burn, Edward Roel, Elena Tan, Eng Hooi Minty, Evan DeFalco, Frank de Maeztu, Gabriel Lipori, Gigi Alghoul, Heba Zhu, Hong Thomas, Jason Bian, Jiang Park, Jimyung Roldán, Jordi Martínez Posada, Jose Banda, Juan M Horcajada, Juan P Kohler, Julianna Shah, Karishma Natarajan, Karthik Lynch, Kristine Liu, Li Schilling, Lisa Recalde, Martina Spotnitz, Matthew Gong, Mengchun Matheny, Michael Valveny, Neus Weiskopf, Nicole Shah, Nigam Alser, Osaid Casajust, Paula Park, Rae Woong Schuff, Robert Seager, Sarah DuVall, Scott You, Seng Chan Song, Seokyoung Fernández-Bertolín, Sergio Fortin, Stephen Magoc, Tanja Falconer, Thomas Subbian, Vignesh Huser, Vojtech Ahmed, Waheed-Ul-Rahman Carter, William Guan, Yin Galvan, Yankuic He, Xing Rijnbeek, Peter Hripcsak, George Ryan, Patrick Suchard, Marc |
author_facet | Prieto-Alhambra, Daniel Kostka, Kristin Duarte-Salles, Talita Prats-Uribe, Albert Sena, Anthony Pistillo, Andrea Khalid, Sara Lai, Lana Golozar, Asieh Alshammari, Thamir M Dawoud, Dalia Nyberg, Fredrik Wilcox, Adam Andryc, Alan Williams, Andrew Ostropolets, Anna Areia, Carlos Jung, Chi Young Harle, Christopher Reich, Christian Blacketer, Clair Morales, Daniel Dorr, David A. Burn, Edward Roel, Elena Tan, Eng Hooi Minty, Evan DeFalco, Frank de Maeztu, Gabriel Lipori, Gigi Alghoul, Heba Zhu, Hong Thomas, Jason Bian, Jiang Park, Jimyung Roldán, Jordi Martínez Posada, Jose Banda, Juan M Horcajada, Juan P Kohler, Julianna Shah, Karishma Natarajan, Karthik Lynch, Kristine Liu, Li Schilling, Lisa Recalde, Martina Spotnitz, Matthew Gong, Mengchun Matheny, Michael Valveny, Neus Weiskopf, Nicole Shah, Nigam Alser, Osaid Casajust, Paula Park, Rae Woong Schuff, Robert Seager, Sarah DuVall, Scott You, Seng Chan Song, Seokyoung Fernández-Bertolín, Sergio Fortin, Stephen Magoc, Tanja Falconer, Thomas Subbian, Vignesh Huser, Vojtech Ahmed, Waheed-Ul-Rahman Carter, William Guan, Yin Galvan, Yankuic He, Xing Rijnbeek, Peter Hripcsak, George Ryan, Patrick Suchard, Marc |
author_sort | Prieto-Alhambra, Daniel |
collection | PubMed |
description | BACKGROUND: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. METHODS: We conducted a descriptive cohort study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11(th) June 2020 and are iteratively updated via GitHub [4]. FINDINGS: We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https:/data.ohdsi.org/Covid19CharacterizationCharybdis/. INTERPRETATION: CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice. |
format | Online Article Text |
id | pubmed-7941629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-79416292021-03-10 Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS Prieto-Alhambra, Daniel Kostka, Kristin Duarte-Salles, Talita Prats-Uribe, Albert Sena, Anthony Pistillo, Andrea Khalid, Sara Lai, Lana Golozar, Asieh Alshammari, Thamir M Dawoud, Dalia Nyberg, Fredrik Wilcox, Adam Andryc, Alan Williams, Andrew Ostropolets, Anna Areia, Carlos Jung, Chi Young Harle, Christopher Reich, Christian Blacketer, Clair Morales, Daniel Dorr, David A. Burn, Edward Roel, Elena Tan, Eng Hooi Minty, Evan DeFalco, Frank de Maeztu, Gabriel Lipori, Gigi Alghoul, Heba Zhu, Hong Thomas, Jason Bian, Jiang Park, Jimyung Roldán, Jordi Martínez Posada, Jose Banda, Juan M Horcajada, Juan P Kohler, Julianna Shah, Karishma Natarajan, Karthik Lynch, Kristine Liu, Li Schilling, Lisa Recalde, Martina Spotnitz, Matthew Gong, Mengchun Matheny, Michael Valveny, Neus Weiskopf, Nicole Shah, Nigam Alser, Osaid Casajust, Paula Park, Rae Woong Schuff, Robert Seager, Sarah DuVall, Scott You, Seng Chan Song, Seokyoung Fernández-Bertolín, Sergio Fortin, Stephen Magoc, Tanja Falconer, Thomas Subbian, Vignesh Huser, Vojtech Ahmed, Waheed-Ul-Rahman Carter, William Guan, Yin Galvan, Yankuic He, Xing Rijnbeek, Peter Hripcsak, George Ryan, Patrick Suchard, Marc Res Sq Article BACKGROUND: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. METHODS: We conducted a descriptive cohort study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11(th) June 2020 and are iteratively updated via GitHub [4]. FINDINGS: We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https:/data.ohdsi.org/Covid19CharacterizationCharybdis/. INTERPRETATION: CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice. American Journal Experts 2021-03-01 /pmc/articles/PMC7941629/ /pubmed/33688639 http://dx.doi.org/10.21203/rs.3.rs-279400/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Prieto-Alhambra, Daniel Kostka, Kristin Duarte-Salles, Talita Prats-Uribe, Albert Sena, Anthony Pistillo, Andrea Khalid, Sara Lai, Lana Golozar, Asieh Alshammari, Thamir M Dawoud, Dalia Nyberg, Fredrik Wilcox, Adam Andryc, Alan Williams, Andrew Ostropolets, Anna Areia, Carlos Jung, Chi Young Harle, Christopher Reich, Christian Blacketer, Clair Morales, Daniel Dorr, David A. Burn, Edward Roel, Elena Tan, Eng Hooi Minty, Evan DeFalco, Frank de Maeztu, Gabriel Lipori, Gigi Alghoul, Heba Zhu, Hong Thomas, Jason Bian, Jiang Park, Jimyung Roldán, Jordi Martínez Posada, Jose Banda, Juan M Horcajada, Juan P Kohler, Julianna Shah, Karishma Natarajan, Karthik Lynch, Kristine Liu, Li Schilling, Lisa Recalde, Martina Spotnitz, Matthew Gong, Mengchun Matheny, Michael Valveny, Neus Weiskopf, Nicole Shah, Nigam Alser, Osaid Casajust, Paula Park, Rae Woong Schuff, Robert Seager, Sarah DuVall, Scott You, Seng Chan Song, Seokyoung Fernández-Bertolín, Sergio Fortin, Stephen Magoc, Tanja Falconer, Thomas Subbian, Vignesh Huser, Vojtech Ahmed, Waheed-Ul-Rahman Carter, William Guan, Yin Galvan, Yankuic He, Xing Rijnbeek, Peter Hripcsak, George Ryan, Patrick Suchard, Marc Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS |
title | Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS |
title_full | Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS |
title_fullStr | Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS |
title_full_unstemmed | Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS |
title_short | Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS |
title_sort | unraveling covid-19: a large-scale characterization of 4.5 million covid-19 cases using charybdis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941629/ https://www.ncbi.nlm.nih.gov/pubmed/33688639 http://dx.doi.org/10.21203/rs.3.rs-279400/v1 |
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