Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2021
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
_version_ 1783662166069477376
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
work_keys_str_mv AT prietoalhambradaniel unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT kostkakristin unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT duartesallestalita unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT pratsuribealbert unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT senaanthony unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT pistilloandrea unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT khalidsara unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT lailana unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT golozarasieh unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT alshammarithamirm unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT dawouddalia unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT nybergfredrik unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT wilcoxadam unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT andrycalan unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT williamsandrew unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT ostropoletsanna unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT areiacarlos unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT jungchiyoung unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT harlechristopher unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT reichchristian unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT blacketerclair unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT moralesdaniel unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT dorrdavida unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT burnedward unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT roelelena unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT tanenghooi unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT mintyevan unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT defalcofrank unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT demaeztugabriel unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT liporigigi unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT alghoulheba unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT zhuhong unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT thomasjason unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT bianjiang unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT parkjimyung unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT roldanjordimartinez unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT posadajose unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT bandajuanm unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT horcajadajuanp unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT kohlerjulianna unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT shahkarishma unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT natarajankarthik unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT lynchkristine unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT liuli unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT schillinglisa unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT recaldemartina unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT spotnitzmatthew unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT gongmengchun unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT mathenymichael unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT valvenyneus unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT weiskopfnicole unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT shahnigam unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT alserosaid unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT casajustpaula unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT parkraewoong unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT schuffrobert unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT seagersarah unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT duvallscott unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT yousengchan unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT songseokyoung unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT fernandezbertolinsergio unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT fortinstephen unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT magoctanja unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT falconerthomas unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT subbianvignesh unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT huservojtech unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT ahmedwaheedulrahman unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT carterwilliam unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT guanyin unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT galvanyankuic unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT hexing unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT rijnbeekpeter unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT hripcsakgeorge unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT ryanpatrick unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis
AT suchardmarc unravelingcovid19alargescalecharacterizationof45millioncovid19casesusingcharybdis