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Federated learning for describing COVID-19 patients and hospital outcomes: An unCoVer analysis: José L Peñalvo

BACKGROUND: Since the onset of the pandemic, the unCoVer network has been identifying real-world data from EMR of hospitalised patients with COVID-19 across countries. These heterogeneous data are integrated into a multi-user data repository operated through Opal/DataSHIELD, an interoperable open-so...

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Autores principales: Peñalvo, JL, Mertens, E, Cottam, J, Berrozpe-Maldonado, V, Fernández-Lobón, D, Solarte-Pabón, O, Menasalvas, E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9594618/
http://dx.doi.org/10.1093/eurpub/ckac131.254
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author Peñalvo, JL
Mertens, E
Cottam, J
Berrozpe-Maldonado, V
Fernández-Lobón, D
Solarte-Pabón, O
Menasalvas, E
author_facet Peñalvo, JL
Mertens, E
Cottam, J
Berrozpe-Maldonado, V
Fernández-Lobón, D
Solarte-Pabón, O
Menasalvas, E
author_sort Peñalvo, JL
collection PubMed
description BACKGROUND: Since the onset of the pandemic, the unCoVer network has been identifying real-world data from EMR of hospitalised patients with COVID-19 across countries. These heterogeneous data are integrated into a multi-user data repository operated through Opal/DataSHIELD, an interoperable open-source server application, providing privacy-preserving access to individual-level information for federated data analyses. METHODS: unCoVer’s federated data platform provided access to EMR collected between 02/2020 - 04/2022 from 6 hospitals in Bosnia and Herzegovina (1), Romania (2), Spain (2), and Turkey (1) for a total of 14,236 patients. Demographics, and co-morbidities at admission, length of hospital stay and intensive care (ICU) needs, are presented according to the patients’ status at discharge. RESULTS: A total of 11,248 (79.0%) of all patients reviewed recovered from COVID-19 after an average 11.5 (SD 10.8) days hospitalised, with only 4.09% of patients needing ICU. A smaller proportion of patients were transferred (5.93%), and 2143 (15.1%) were considered in-hospital deaths after an average 11.6 (SD 10.5) days in the hospital where most (81.2%) needed ICU. Recovered patients had a mean age of 57.7 (SD 16.3) years old, and gender neutral (51.2% men), in contrast to deceased patients that were 74.2 (SD 12.4) years old (59.7% men). Current smoking was infrequent for both recovered or deceased patients (3.27%, and 2.83%, respectively). Cardiometabolic conditions were less commonly reported among later recovered patients in comparison with deceased patients: obesity (10.7% vs 12.1%), diabetes (15.9% vs 27.4%), hypertension (23.2% vs 42.7%), and CVD (9.33% vs 44.9%). Chronic pulmonary disease was also more frequent among deceased patients (10.3% vs 18.1%). CONCLUSIONS: Characteristics of hospitalised COVID-19 patients differ according to outcomes at discharge with more in-hospital death reported among older, chronic patients across 6 hospitals in 4 countries. KEY MESSAGES: • Federated analyses provide unique opportunities for robust results by privacy-preserving accessing individual-level data from heterogeneous data sources. • The unCoVer network aims to demonstrate the usability of the infrastructure to address research questions related to the COVID-19 while extending the concept to other clinical areas.
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spelling pubmed-95946182022-11-04 Federated learning for describing COVID-19 patients and hospital outcomes: An unCoVer analysis: José L Peñalvo Peñalvo, JL Mertens, E Cottam, J Berrozpe-Maldonado, V Fernández-Lobón, D Solarte-Pabón, O Menasalvas, E Eur J Public Health Poster Displays BACKGROUND: Since the onset of the pandemic, the unCoVer network has been identifying real-world data from EMR of hospitalised patients with COVID-19 across countries. These heterogeneous data are integrated into a multi-user data repository operated through Opal/DataSHIELD, an interoperable open-source server application, providing privacy-preserving access to individual-level information for federated data analyses. METHODS: unCoVer’s federated data platform provided access to EMR collected between 02/2020 - 04/2022 from 6 hospitals in Bosnia and Herzegovina (1), Romania (2), Spain (2), and Turkey (1) for a total of 14,236 patients. Demographics, and co-morbidities at admission, length of hospital stay and intensive care (ICU) needs, are presented according to the patients’ status at discharge. RESULTS: A total of 11,248 (79.0%) of all patients reviewed recovered from COVID-19 after an average 11.5 (SD 10.8) days hospitalised, with only 4.09% of patients needing ICU. A smaller proportion of patients were transferred (5.93%), and 2143 (15.1%) were considered in-hospital deaths after an average 11.6 (SD 10.5) days in the hospital where most (81.2%) needed ICU. Recovered patients had a mean age of 57.7 (SD 16.3) years old, and gender neutral (51.2% men), in contrast to deceased patients that were 74.2 (SD 12.4) years old (59.7% men). Current smoking was infrequent for both recovered or deceased patients (3.27%, and 2.83%, respectively). Cardiometabolic conditions were less commonly reported among later recovered patients in comparison with deceased patients: obesity (10.7% vs 12.1%), diabetes (15.9% vs 27.4%), hypertension (23.2% vs 42.7%), and CVD (9.33% vs 44.9%). Chronic pulmonary disease was also more frequent among deceased patients (10.3% vs 18.1%). CONCLUSIONS: Characteristics of hospitalised COVID-19 patients differ according to outcomes at discharge with more in-hospital death reported among older, chronic patients across 6 hospitals in 4 countries. KEY MESSAGES: • Federated analyses provide unique opportunities for robust results by privacy-preserving accessing individual-level data from heterogeneous data sources. • The unCoVer network aims to demonstrate the usability of the infrastructure to address research questions related to the COVID-19 while extending the concept to other clinical areas. Oxford University Press 2022-10-25 /pmc/articles/PMC9594618/ http://dx.doi.org/10.1093/eurpub/ckac131.254 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the European Public Health Association. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Poster Displays
Peñalvo, JL
Mertens, E
Cottam, J
Berrozpe-Maldonado, V
Fernández-Lobón, D
Solarte-Pabón, O
Menasalvas, E
Federated learning for describing COVID-19 patients and hospital outcomes: An unCoVer analysis: José L Peñalvo
title Federated learning for describing COVID-19 patients and hospital outcomes: An unCoVer analysis: José L Peñalvo
title_full Federated learning for describing COVID-19 patients and hospital outcomes: An unCoVer analysis: José L Peñalvo
title_fullStr Federated learning for describing COVID-19 patients and hospital outcomes: An unCoVer analysis: José L Peñalvo
title_full_unstemmed Federated learning for describing COVID-19 patients and hospital outcomes: An unCoVer analysis: José L Peñalvo
title_short Federated learning for describing COVID-19 patients and hospital outcomes: An unCoVer analysis: José L Peñalvo
title_sort federated learning for describing covid-19 patients and hospital outcomes: an uncover analysis: josé l peñalvo
topic Poster Displays
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9594618/
http://dx.doi.org/10.1093/eurpub/ckac131.254
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