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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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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. |
format | Online Article Text |
id | pubmed-9594618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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