Clusters of Pregnant Women with Severe Acute Respiratory Syndrome Due to Covid-19

An Unsupervised Learning Approach

Bibliographic Data

ID15514672
AuthorsIsadora Celine Rodrigues Carneiro (0000-0002-9108-3213, Fundação Carlos Chagas), Sofia Galvão Feronato (0000-0002-4833-526X, Fundação Carlos Chagas), Guilherme Ferreira Silveira (0000-0002-1866-0563, Fundação Carlos Chagas), Alexandre D P Chiavegatto Filho (0000-0003-3251-9600, Universidade de São Paulo), Hellen G G Santos (0000-0002-6446-8660, Fundação Carlos Chagas, corresponding author)
Year2022
Volume19
Issue20
Pages13522-13522
Publication date2022-10-19
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph192013522
PMID36294103
OpenAlexW4306836130
LanguageEN
References cited32

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

Cluster (spacecraft · Comorbidity · Disease · Environmental health · Population · COVID-19 and Mental Health · COVID-19 Impact on Reproduction · Maternal Mental Health During Pregnancy and Postpartum · Medicine · Epidemiology · Internal Medicine · Pediatrics

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