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. 2022 Oct 19;19(20):13522.
doi: 10.3390/ijerph192013522.

Clusters of Pregnant Women with Severe Acute Respiratory Syndrome Due to COVID-19: An Unsupervised Learning Approach

Isadora Celine Rodrigues Carneiro  1 , Sofia Galvão Feronato  1 , Guilherme Ferreira Silveira  1 , Alexandre Dias Porto Chiavegatto Filho  2 , Hellen Geremias Dos Santos  1
Affiliations

Clusters of Pregnant Women with Severe Acute Respiratory Syndrome Due to COVID-19: An Unsupervised Learning Approach

Isadora Celine Rodrigues Carneiro et al. Int J Environ Res Public Health. .

Abstract

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.

Keywords: COVID-19; healthcare systems and management; hospitalizations; pregnant women; severe acute respiratory syndrome.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Multiple correspondence analysis results for the first and the second principal component (PC). The black dots are “yes” categories, numbered according to the variable labels presented in the methods section. Gray dots correspond to the “non–occurrence” category for symptoms, comorbidities, or hospital characteristics.
Figure 2
Figure 2
Distribution of the pregnant and postpartum women with SARS due to COVID-19 into the three clusters according to the first and the second principal component (PC).
See this image and copyright information in PMC

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