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

dc.creatorCarneiro, Isadora Celine Rodrigues
dc.creatorFeronato, Sofia Galvão
dc.creatorSilveira, Guilherme Ferreira
dc.creatorChiavegatto Filho, Alexandre Dias Porto
dc.creatorSantos, Hellen Geremias dos
dc.date2022-12-19T17:43:07Z
dc.date2022-12-19T17:43:07Z
dc.date2022
dc.date.accessioned2023-09-26T23:55:57Z
dc.date.available2023-09-26T23:55:57Z
dc.identifierCARNEIRO, 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.identifier1660-4601
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/56072
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8895730
dc.descriptionCOVID-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.
dc.formatapplication/pdf
dc.languagepor
dc.publisherMDPI
dc.rightsopen access
dc.subjectSevere Acute Respiratory Syndrome
dc.subjectPregnant Women
dc.subjectHospitalization
dc.subjectDelivery of Health Care
dc.subjectHealth Management
dc.subjectSíndrome Respiratorio Agudo Grave
dc.subjectMujeres Embarazadas
dc.subjectHospitalización
dc.subjectAtención a la Salud
dc.subjectGestión en Salud
dc.subjectSyndrome respiratoire aigu sévère
dc.subjectFemmes enceintes
dc.subjectHospitalisation
dc.subjectPrestations des soins de santé
dc.subjectGestion de la Santé
dc.subjectCOVID-19
dc.subjectSíndrome Respiratória Aguda Grave
dc.subjectGestantes
dc.subjectHospitalização
dc.subjectAtenção à Saúde
dc.subjectGestão em Saúde
dc.titleClusters of pregnant women with severe acute respiratory syndrome due to COVID-19: an unsupervised learning approach
dc.typeArticle


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