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Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol
INTRODUCTION: unCoVer—Unravelling data for rapid evidence-based response to COVID-19—is a Horizon 2020-funded network of 29 partners from 18 countries capable of collecting and using real-world data (RWD) derived from the response and provision of care to patients with COVID-19 by health systems acr...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8602928/ https://www.ncbi.nlm.nih.gov/pubmed/34794999 http://dx.doi.org/10.1136/bmjopen-2021-055630 |
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author | Peñalvo, José L. Mertens, Elly Ademović, Enisa Akgun, Seval Baltazar, Ana Lúcia Buonfrate, Dora Čoklo, Miran Devleesschauwer, Brecht Diaz Valencia, Paula Andrea Fernandes, João C. Gómez, Enrique Javier Hynds, Paul Kabir, Zubair Klein, Jörn Kostoulas, Polychronis Llanos Jiménez, Lucía Lotrean, Lucia Maria Majdan, Marek Menasalvas, Ernestina Nguewa, Paul Oh, In-Hwan O’Sullivan, Georgie Pereira, David M. Reina Ortiz, Miguel Riva, Silvia Soriano, Gloria Soriano, Joan B. Spilki, Fernando Tamang, Mary Elizabeth Trofor, Antigona Carmen Vaillant, Michel Van Ierssel, Sabrina Vuković, Jakov Castellano, José M. |
author_facet | Peñalvo, José L. Mertens, Elly Ademović, Enisa Akgun, Seval Baltazar, Ana Lúcia Buonfrate, Dora Čoklo, Miran Devleesschauwer, Brecht Diaz Valencia, Paula Andrea Fernandes, João C. Gómez, Enrique Javier Hynds, Paul Kabir, Zubair Klein, Jörn Kostoulas, Polychronis Llanos Jiménez, Lucía Lotrean, Lucia Maria Majdan, Marek Menasalvas, Ernestina Nguewa, Paul Oh, In-Hwan O’Sullivan, Georgie Pereira, David M. Reina Ortiz, Miguel Riva, Silvia Soriano, Gloria Soriano, Joan B. Spilki, Fernando Tamang, Mary Elizabeth Trofor, Antigona Carmen Vaillant, Michel Van Ierssel, Sabrina Vuković, Jakov Castellano, José M. |
author_sort | Peñalvo, José L. |
collection | PubMed |
description | INTRODUCTION: unCoVer—Unravelling data for rapid evidence-based response to COVID-19—is a Horizon 2020-funded network of 29 partners from 18 countries capable of collecting and using real-world data (RWD) derived from the response and provision of care to patients with COVID-19 by health systems across Europe and elsewhere. unCoVer aims to exploit the full potential of this information to rapidly address clinical and epidemiological research questions arising from the evolving pandemic. METHODS AND ANALYSIS: From the onset of the COVID-19 pandemic, partners are gathering RWD from electronic health records currently including information from over 22 000 hospitalised patients with COVID-19, and national surveillance and screening data, and registries with over 1 900 000 COVID-19 cases across Europe, with continuous updates. These heterogeneous datasets will be described, harmonised and integrated into a multi-user data repository operated through Opal-DataSHIELD, an interoperable open-source server application. Federated data analyses, without sharing or disclosing any individual-level data, will be performed with the objective to reveal patients’ baseline characteristics, biomarkers, determinants of COVID-19 prognosis, safety and effectiveness of treatments, and potential strategies against COVID-19, as well as epidemiological patterns. These analyses will complement evidence from efficacy/safety clinical trials, where vulnerable, more complex/heterogeneous populations and those most at risk of severe COVID-19 are often excluded. ETHICS AND DISSEMINATION: After strict ethical considerations, databases will be available through a federated data analysis platform that allows processing of available COVID-19 RWD without disclosing identification information to analysts and limiting output to data aggregates. Dissemination of unCoVer’s activities will be related to the access and use of dissimilar RWD, as well as the results generated by the pooled analyses. Dissemination will include training and educational activities, scientific publications and conference communications. |
format | Online Article Text |
id | pubmed-8602928 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-86029282021-11-19 Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol Peñalvo, José L. Mertens, Elly Ademović, Enisa Akgun, Seval Baltazar, Ana Lúcia Buonfrate, Dora Čoklo, Miran Devleesschauwer, Brecht Diaz Valencia, Paula Andrea Fernandes, João C. Gómez, Enrique Javier Hynds, Paul Kabir, Zubair Klein, Jörn Kostoulas, Polychronis Llanos Jiménez, Lucía Lotrean, Lucia Maria Majdan, Marek Menasalvas, Ernestina Nguewa, Paul Oh, In-Hwan O’Sullivan, Georgie Pereira, David M. Reina Ortiz, Miguel Riva, Silvia Soriano, Gloria Soriano, Joan B. Spilki, Fernando Tamang, Mary Elizabeth Trofor, Antigona Carmen Vaillant, Michel Van Ierssel, Sabrina Vuković, Jakov Castellano, José M. BMJ Open Research Methods INTRODUCTION: unCoVer—Unravelling data for rapid evidence-based response to COVID-19—is a Horizon 2020-funded network of 29 partners from 18 countries capable of collecting and using real-world data (RWD) derived from the response and provision of care to patients with COVID-19 by health systems across Europe and elsewhere. unCoVer aims to exploit the full potential of this information to rapidly address clinical and epidemiological research questions arising from the evolving pandemic. METHODS AND ANALYSIS: From the onset of the COVID-19 pandemic, partners are gathering RWD from electronic health records currently including information from over 22 000 hospitalised patients with COVID-19, and national surveillance and screening data, and registries with over 1 900 000 COVID-19 cases across Europe, with continuous updates. These heterogeneous datasets will be described, harmonised and integrated into a multi-user data repository operated through Opal-DataSHIELD, an interoperable open-source server application. Federated data analyses, without sharing or disclosing any individual-level data, will be performed with the objective to reveal patients’ baseline characteristics, biomarkers, determinants of COVID-19 prognosis, safety and effectiveness of treatments, and potential strategies against COVID-19, as well as epidemiological patterns. These analyses will complement evidence from efficacy/safety clinical trials, where vulnerable, more complex/heterogeneous populations and those most at risk of severe COVID-19 are often excluded. ETHICS AND DISSEMINATION: After strict ethical considerations, databases will be available through a federated data analysis platform that allows processing of available COVID-19 RWD without disclosing identification information to analysts and limiting output to data aggregates. Dissemination of unCoVer’s activities will be related to the access and use of dissimilar RWD, as well as the results generated by the pooled analyses. Dissemination will include training and educational activities, scientific publications and conference communications. BMJ Publishing Group 2021-11-18 /pmc/articles/PMC8602928/ /pubmed/34794999 http://dx.doi.org/10.1136/bmjopen-2021-055630 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Research Methods Peñalvo, José L. Mertens, Elly Ademović, Enisa Akgun, Seval Baltazar, Ana Lúcia Buonfrate, Dora Čoklo, Miran Devleesschauwer, Brecht Diaz Valencia, Paula Andrea Fernandes, João C. Gómez, Enrique Javier Hynds, Paul Kabir, Zubair Klein, Jörn Kostoulas, Polychronis Llanos Jiménez, Lucía Lotrean, Lucia Maria Majdan, Marek Menasalvas, Ernestina Nguewa, Paul Oh, In-Hwan O’Sullivan, Georgie Pereira, David M. Reina Ortiz, Miguel Riva, Silvia Soriano, Gloria Soriano, Joan B. Spilki, Fernando Tamang, Mary Elizabeth Trofor, Antigona Carmen Vaillant, Michel Van Ierssel, Sabrina Vuković, Jakov Castellano, José M. Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol |
title | Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol |
title_full | Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol |
title_fullStr | Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol |
title_full_unstemmed | Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol |
title_short | Unravelling data for rapid evidence-based response to COVID-19: a summary of the unCoVer protocol |
title_sort | unravelling data for rapid evidence-based response to covid-19: a summary of the uncover protocol |
topic | Research Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8602928/ https://www.ncbi.nlm.nih.gov/pubmed/34794999 http://dx.doi.org/10.1136/bmjopen-2021-055630 |
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