Characterization of COVID-19 infected pregnant women with ICU admission and the risk of preterm: A cluster analysis

Abstract

Background

The unique physiological changes during pregnancy present challenges in understanding the full scope and effects of COVID-19 on pregnant women, adding complexity to their medical management. Given the significant changes in the immune, circulatory, respiratory, and hormonal systems during the progression of the pregnancy, and the specific factors with higher risk of COVID-19, like metabolic, vascular, and endothelial factors, typically also associated with maternal and neonatal unfavorable outcomes, the full understanding of how COVID-19 affects pregnant women is not clarified yet.

Methods

In this study, anonymous data from medical records of pregnant women with lab-confirmed COVID‐19 in Astana, Kazakhstan from May 1, 2021, to July 14, 2021, were collected retrospectively. A multivariate regression model was built to identify factors associated with the risk of ICU admission. Cluster analysis was performed to identify distinct groups among women admitted to the ICU based on their blood parameters, coagulation profiles, and oxygenation saturation levels.

Results

10.7 % of pregnant women were admitted to ICU. Among them, 4.36 % were in the 2nd trimester and 13.58 % in the 3rd trimester. No women in the 1st trimester were admitted to ICU. A multivariate regression model demonstrates that gestational diabetes, leukocytes, CRP, and saturation were the factors significantly associated with a higher risk of ICU admission. Three clusters of pregnant women were segmented, and preterm birth was frequent in clusters 1 (serious systemic conditions affecting multiple organs) and 3 (women with hypertension and preeclampsia), whereas cluster 2 represents women who can also be characterized as suffering from infections with a possible autoimmune component. Neutrophil to lymphocyte ratio was frequent in clusters 1 and 3.

Conclusion

In this study, multivariable analysis identified factors with a risk of ICU admission, and clustering analysis helped to identify groups of COVID-19-infected pregnant women admitted to ICU with similar risk profiles. Differences in clusters can help to explain discrepancies in COVID-19 outcomes and suggest biochemical and molecular mechanisms involved in COVID-19 and outline a more personalized approach to understanding, diagnosing, and treating women.