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External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact
BACKGROUND: Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116925/ https://www.ncbi.nlm.nih.gov/pubmed/37052252 http://dx.doi.org/10.1080/07853890.2023.2195204 |
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author | Giacobbe, Daniele Roberto Di Maria, Emilio Tagliafico, Alberto Stefano Bavastro, Martina Trombetta, Carlo Simone Marelli, Cristina Di Meco, Gabriele Cattardico, Greta Mora, Sara Signori, Alessio Vena, Antonio Mikulska, Malgorzata Dentone, Chiara Bruzzone, Bianca Bignotti, Bianca Orsi, Andrea Robba, Chiara Ball, Lorenzo Brunetti, Iole Battaglini, Denise Di Biagio, Antonio Sormani, Maria Pia Pelosi, Paolo Giacomini, Mauro Icardi, Giancarlo Bassetti, Matteo |
author_facet | Giacobbe, Daniele Roberto Di Maria, Emilio Tagliafico, Alberto Stefano Bavastro, Martina Trombetta, Carlo Simone Marelli, Cristina Di Meco, Gabriele Cattardico, Greta Mora, Sara Signori, Alessio Vena, Antonio Mikulska, Malgorzata Dentone, Chiara Bruzzone, Bianca Bignotti, Bianca Orsi, Andrea Robba, Chiara Ball, Lorenzo Brunetti, Iole Battaglini, Denise Di Biagio, Antonio Sormani, Maria Pia Pelosi, Paolo Giacomini, Mauro Icardi, Giancarlo Bassetti, Matteo |
author_sort | Giacobbe, Daniele Roberto |
collection | PubMed |
description | BACKGROUND: Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis. METHODS: Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method. RESULTS: Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81–5.30, p < 0.001) and for phenotype C vs. B (HR 2.20, 95% CI 1.50–3.23, p < 0.001). A non-statistically significant trend towards higher mortality was also observed for phenotype B vs. A (HR 1.41; 95% CI 0.92–2.15, p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes. CONCLUSIONS: The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study. |
format | Online Article Text |
id | pubmed-10116925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-101169252023-04-21 External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact Giacobbe, Daniele Roberto Di Maria, Emilio Tagliafico, Alberto Stefano Bavastro, Martina Trombetta, Carlo Simone Marelli, Cristina Di Meco, Gabriele Cattardico, Greta Mora, Sara Signori, Alessio Vena, Antonio Mikulska, Malgorzata Dentone, Chiara Bruzzone, Bianca Bignotti, Bianca Orsi, Andrea Robba, Chiara Ball, Lorenzo Brunetti, Iole Battaglini, Denise Di Biagio, Antonio Sormani, Maria Pia Pelosi, Paolo Giacomini, Mauro Icardi, Giancarlo Bassetti, Matteo Ann Med Infectious Diseases BACKGROUND: Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis. METHODS: Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method. RESULTS: Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81–5.30, p < 0.001) and for phenotype C vs. B (HR 2.20, 95% CI 1.50–3.23, p < 0.001). A non-statistically significant trend towards higher mortality was also observed for phenotype B vs. A (HR 1.41; 95% CI 0.92–2.15, p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes. CONCLUSIONS: The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study. Taylor & Francis 2023-04-13 /pmc/articles/PMC10116925/ /pubmed/37052252 http://dx.doi.org/10.1080/07853890.2023.2195204 Text en © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
spellingShingle | Infectious Diseases Giacobbe, Daniele Roberto Di Maria, Emilio Tagliafico, Alberto Stefano Bavastro, Martina Trombetta, Carlo Simone Marelli, Cristina Di Meco, Gabriele Cattardico, Greta Mora, Sara Signori, Alessio Vena, Antonio Mikulska, Malgorzata Dentone, Chiara Bruzzone, Bianca Bignotti, Bianca Orsi, Andrea Robba, Chiara Ball, Lorenzo Brunetti, Iole Battaglini, Denise Di Biagio, Antonio Sormani, Maria Pia Pelosi, Paolo Giacomini, Mauro Icardi, Giancarlo Bassetti, Matteo External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact |
title | External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact |
title_full | External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact |
title_fullStr | External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact |
title_full_unstemmed | External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact |
title_short | External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact |
title_sort | external validation of unsupervised covid-19 clinical phenotypes and their prognostic impact |
topic | Infectious Diseases |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116925/ https://www.ncbi.nlm.nih.gov/pubmed/37052252 http://dx.doi.org/10.1080/07853890.2023.2195204 |
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