Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach

Detalhes bibliográficos
Autor(a) principal: Carneiro, Isadora Celine Rodrigues
Data de Publicação: 2022
Outros Autores: Feronato, Sofia Galvão, Silveira, Guilherme Ferreira, Chiavegatto Filho, Alexandre Dias Porto, Santos, Hellen Geremias dos
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/56072
Resumo: Fundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil.
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spelling Carneiro, Isadora Celine RodriguesFeronato, Sofia GalvãoSilveira, Guilherme FerreiraChiavegatto Filho, Alexandre Dias PortoSantos, Hellen Geremias dos2022-12-19T17:43:07Z2022-12-19T17:43:07Z2022CARNEIRO, Isadora Celine Rodrigues et al. Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach. Int. J. Environ. Res. Public Health, v. 19, n. 13522, p. 1–13, 20221660-4601https://www.arca.fiocruz.br/handle/icict/56072porMDPIClusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approachinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil.Universidade de São Paulo. Faculdade de Ciências Farmacêuticas. São Paulo, SP, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil.COVID-19 has been widely explored in relation to its symptoms, outcomes, and risk profiles for the severe form of the disease. Our aim was to identify clusters of pregnant and postpartum women with severe acute respiratory syndrome (SARS) due to COVID-19 by analyzing data available in the Influenza Epidemiological Surveillance Information System of Brazil (SIVEP-Gripe) between March 2020 and August 2021. The study’s population comprised 16,409 women aged between 10 and 49 years old. Multiple correspondence analyses were performed to summarize information from 28 variables related to symptoms, comorbidities, and hospital characteristics into a set of continuous principal components (PCs). The population was segmented into three clusters based on an agglomerative hierarchical cluster analysis applied to the first 10 PCs. Cluster 1 had a higher frequency of younger women without comorbidities and with flu-like symptoms; cluster 2 was represented by women who reported mainly ageusia and anosmia; cluster 3 grouped older women with the highest frequencies of comorbidities and poor outcomes. The defined clusters revealed different levels of disease severity, which can contribute to the initial risk assessment of the patient, assisting the referral of these women to health services with an appropriate level of complexity.Severe Acute Respiratory SyndromePregnant WomenHospitalizationDelivery of Health CareHealth ManagementSíndrome Respiratorio Agudo GraveMujeres EmbarazadasHospitalizaciónAtención a la SaludGestión en SaludSyndrome respiratoire aigu sévèreFemmes enceintesHospitalisationPrestations des soins de santéGestion de la SantéCOVID-19Síndrome Respiratória Aguda GraveGestantesHospitalizaçãoAtenção à SaúdeGestão em Saúdeinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; 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dc.title.en_US.fl_str_mv Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach
title Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach
spellingShingle Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach
Carneiro, Isadora Celine Rodrigues
Severe Acute Respiratory Syndrome
Pregnant Women
Hospitalization
Delivery of Health Care
Health Management
Síndrome Respiratorio Agudo Grave
Mujeres Embarazadas
Hospitalización
Atención a la Salud
Gestión en Salud
Syndrome respiratoire aigu sévère
Femmes enceintes
Hospitalisation
Prestations des soins de santé
Gestion de la Santé
COVID-19
Síndrome Respiratória Aguda Grave
Gestantes
Hospitalização
Atenção à Saúde
Gestão em Saúde
title_short Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach
title_full Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach
title_fullStr Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach
title_full_unstemmed Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach
title_sort Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach
author Carneiro, Isadora Celine Rodrigues
author_facet Carneiro, Isadora Celine Rodrigues
Feronato, Sofia Galvão
Silveira, Guilherme Ferreira
Chiavegatto Filho, Alexandre Dias Porto
Santos, Hellen Geremias dos
author_role author
author2 Feronato, Sofia Galvão
Silveira, Guilherme Ferreira
Chiavegatto Filho, Alexandre Dias Porto
Santos, Hellen Geremias dos
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Carneiro, Isadora Celine Rodrigues
Feronato, Sofia Galvão
Silveira, Guilherme Ferreira
Chiavegatto Filho, Alexandre Dias Porto
Santos, Hellen Geremias dos
dc.subject.en.en_US.fl_str_mv Severe Acute Respiratory Syndrome
Pregnant Women
Hospitalization
Delivery of Health Care
Health Management
topic Severe Acute Respiratory Syndrome
Pregnant Women
Hospitalization
Delivery of Health Care
Health Management
Síndrome Respiratorio Agudo Grave
Mujeres Embarazadas
Hospitalización
Atención a la Salud
Gestión en Salud
Syndrome respiratoire aigu sévère
Femmes enceintes
Hospitalisation
Prestations des soins de santé
Gestion de la Santé
COVID-19
Síndrome Respiratória Aguda Grave
Gestantes
Hospitalização
Atenção à Saúde
Gestão em Saúde
dc.subject.es.en_US.fl_str_mv Síndrome Respiratorio Agudo Grave
Mujeres Embarazadas
Hospitalización
Atención a la Salud
Gestión en Salud
dc.subject.fr.en_US.fl_str_mv Syndrome respiratoire aigu sévère
Femmes enceintes
Hospitalisation
Prestations des soins de santé
Gestion de la Santé
dc.subject.decs.en_US.fl_str_mv COVID-19
Síndrome Respiratória Aguda Grave
Gestantes
Hospitalização
Atenção à Saúde
Gestão em Saúde
description Fundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil.
publishDate 2022
dc.date.accessioned.fl_str_mv 2022-12-19T17:43:07Z
dc.date.available.fl_str_mv 2022-12-19T17:43:07Z
dc.date.issued.fl_str_mv 2022
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.citation.fl_str_mv CARNEIRO, Isadora Celine Rodrigues et al. Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach. Int. J. Environ. Res. Public Health, v. 19, n. 13522, p. 1–13, 2022
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/56072
dc.identifier.issn.en_US.fl_str_mv 1660-4601
identifier_str_mv CARNEIRO, Isadora Celine Rodrigues et al. Clusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach. Int. J. Environ. Res. Public Health, v. 19, n. 13522, p. 1–13, 2022
1660-4601
url https://www.arca.fiocruz.br/handle/icict/56072
dc.language.iso.fl_str_mv por
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dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
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instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Repositório Institucional da FIOCRUZ (ARCA)
collection Repositório Institucional da FIOCRUZ (ARCA)
bitstream.url.fl_str_mv https://www.arca.fiocruz.br/bitstream/icict/56072/1/license.txt
https://www.arca.fiocruz.br/bitstream/icict/56072/2/Clusters_Pregnant_IntJournal_2022ok.pdf
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