Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/152741 |
Resumo: | Purpose: COVID-19 presents complex pathophysiology, and evidence collected points towards an intricate interaction between viral-dependent and individual immunological mechanisms. Identifying phenotypes through clinical and biological markers may provide a better understanding of the subjacent mechanisms and an early patient-tailored characterization of illness severity. Methods: A multicenter prospective cohort study was performed in 5 hospitals in Portugal and Brazil for one year between 2020–2021. All adult patients with an Intensive Care Unit admission with SARS-CoV-2 pneumonia were eligible. COVID-19 was diagnosed using clinical and radiologic criteria with a SARS-CoV-2 positive RT-PCR test. A two-step hierarchical cluster analysis was made using several class-defining variables. Results: 814 patients were included. The cluster analysis revealed a three-class model, allowing for the definition of three distinct COVID-19 phenotypes: 407 patients in phenotype A, 244 patients in phenotype B, and 163 patients in phenotype C. Patients included in phenotype A were significantly older, with higher baseline inflammatory biomarkers profile, and a significantly higher requirement of organ support and mortality rate. Phenotypes B and C demonstrated some overlapping clinical characteristics but different outcomes. Phenotype C patients presented a lower mortality rate, with consistently lower C-reactive protein, but higher procalcitonin and interleukin-6 serum levels, describing an immunological profile significantly different from phenotype B. Conclusions: Severe COVID-19 patients exhibit three different clinical phenotypes with distinct profiles and outcomes. Their identification could have an impact on patients’ care, justifying different therapy responses and inconsistencies identified across different randomized control trial results. |
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Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 PatientsResults from a Cohort Observational Studycluster analysisCOVID-19critical caremortality ratephenotypesMedicine(all)Purpose: COVID-19 presents complex pathophysiology, and evidence collected points towards an intricate interaction between viral-dependent and individual immunological mechanisms. Identifying phenotypes through clinical and biological markers may provide a better understanding of the subjacent mechanisms and an early patient-tailored characterization of illness severity. Methods: A multicenter prospective cohort study was performed in 5 hospitals in Portugal and Brazil for one year between 2020–2021. All adult patients with an Intensive Care Unit admission with SARS-CoV-2 pneumonia were eligible. COVID-19 was diagnosed using clinical and radiologic criteria with a SARS-CoV-2 positive RT-PCR test. A two-step hierarchical cluster analysis was made using several class-defining variables. Results: 814 patients were included. The cluster analysis revealed a three-class model, allowing for the definition of three distinct COVID-19 phenotypes: 407 patients in phenotype A, 244 patients in phenotype B, and 163 patients in phenotype C. Patients included in phenotype A were significantly older, with higher baseline inflammatory biomarkers profile, and a significantly higher requirement of organ support and mortality rate. Phenotypes B and C demonstrated some overlapping clinical characteristics but different outcomes. Phenotype C patients presented a lower mortality rate, with consistently lower C-reactive protein, but higher procalcitonin and interleukin-6 serum levels, describing an immunological profile significantly different from phenotype B. Conclusions: Severe COVID-19 patients exhibit three different clinical phenotypes with distinct profiles and outcomes. Their identification could have an impact on patients’ care, justifying different therapy responses and inconsistencies identified across different randomized control trial results.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)Comprehensive Health Research Centre (CHRC) - pólo NMSRUNCidade, José Pedrode Souza Dantas, Vicente Césde Figueiredo Thompson, Alessandrade Miranda, Renata Carnevale Carneiro ChermontMamfrim, RafaelaCaroli, HenriqueEscudini, GabrielaOliveira, NataliaCastro, TaizaPovoa, Pedro2023-05-12T22:12:08Z2023-042023-04-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/152741eng2077-0383PURE: 60176323https://doi.org/10.3390/jcm12083035info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T18:11:27Zoai:run.unl.pt:10362/152741Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T18:11:27Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients Results from a Cohort Observational Study |
title |
Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients |
spellingShingle |
Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients Cidade, José Pedro cluster analysis COVID-19 critical care mortality rate phenotypes Medicine(all) |
title_short |
Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients |
title_full |
Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients |
title_fullStr |
Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients |
title_full_unstemmed |
Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients |
title_sort |
Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients |
author |
Cidade, José Pedro |
author_facet |
Cidade, José Pedro de Souza Dantas, Vicente Cés de Figueiredo Thompson, Alessandra de Miranda, Renata Carnevale Carneiro Chermont Mamfrim, Rafaela Caroli, Henrique Escudini, Gabriela Oliveira, Natalia Castro, Taiza Povoa, Pedro |
author_role |
author |
author2 |
de Souza Dantas, Vicente Cés de Figueiredo Thompson, Alessandra de Miranda, Renata Carnevale Carneiro Chermont Mamfrim, Rafaela Caroli, Henrique Escudini, Gabriela Oliveira, Natalia Castro, Taiza Povoa, Pedro |
author2_role |
author author author author author author author author author |
dc.contributor.none.fl_str_mv |
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) Comprehensive Health Research Centre (CHRC) - pólo NMS RUN |
dc.contributor.author.fl_str_mv |
Cidade, José Pedro de Souza Dantas, Vicente Cés de Figueiredo Thompson, Alessandra de Miranda, Renata Carnevale Carneiro Chermont Mamfrim, Rafaela Caroli, Henrique Escudini, Gabriela Oliveira, Natalia Castro, Taiza Povoa, Pedro |
dc.subject.por.fl_str_mv |
cluster analysis COVID-19 critical care mortality rate phenotypes Medicine(all) |
topic |
cluster analysis COVID-19 critical care mortality rate phenotypes Medicine(all) |
description |
Purpose: COVID-19 presents complex pathophysiology, and evidence collected points towards an intricate interaction between viral-dependent and individual immunological mechanisms. Identifying phenotypes through clinical and biological markers may provide a better understanding of the subjacent mechanisms and an early patient-tailored characterization of illness severity. Methods: A multicenter prospective cohort study was performed in 5 hospitals in Portugal and Brazil for one year between 2020–2021. All adult patients with an Intensive Care Unit admission with SARS-CoV-2 pneumonia were eligible. COVID-19 was diagnosed using clinical and radiologic criteria with a SARS-CoV-2 positive RT-PCR test. A two-step hierarchical cluster analysis was made using several class-defining variables. Results: 814 patients were included. The cluster analysis revealed a three-class model, allowing for the definition of three distinct COVID-19 phenotypes: 407 patients in phenotype A, 244 patients in phenotype B, and 163 patients in phenotype C. Patients included in phenotype A were significantly older, with higher baseline inflammatory biomarkers profile, and a significantly higher requirement of organ support and mortality rate. Phenotypes B and C demonstrated some overlapping clinical characteristics but different outcomes. Phenotype C patients presented a lower mortality rate, with consistently lower C-reactive protein, but higher procalcitonin and interleukin-6 serum levels, describing an immunological profile significantly different from phenotype B. Conclusions: Severe COVID-19 patients exhibit three different clinical phenotypes with distinct profiles and outcomes. Their identification could have an impact on patients’ care, justifying different therapy responses and inconsistencies identified across different randomized control trial results. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-12T22:12:08Z 2023-04 2023-04-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/152741 |
url |
http://hdl.handle.net/10362/152741 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2077-0383 PURE: 60176323 https://doi.org/10.3390/jcm12083035 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817545933640957952 |