sFlt-1 Is an Independent Predictor of Adverse Maternal Outcomes in Women With SARS-CoV-2 Infection and Hypertensive Disorders of Pregnancy

Preeclampsia (PE) and COVID-19 share a common vascular-endothelial physiopathological pathway that may aggravate or worsen women's outcomes when both coexist. This study aims to evaluate the association of sFlt-1 levels and adverse maternal outcomes among positive SARS-CoV-2 pregnant women with...

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Veröffentlicht in:Frontiers in medicine 2022-05, Vol.9, p.894633-894633
Hauptverfasser: Hernandez-Pacheco, Jose Antonio, Torres-Torres, Johnatan, Martinez-Portilla, Raigam Jafet, Solis-Paredes, Juan Mario, Estrada-Gutierrez, Guadalupe, Mateu-Rogell, Paloma, Nares-Torices, Miguel Angel, Lopez-Marenco, Mario Enmanuel, Escobedo-Segura, Keren Rachel, Posadas-Nava, Alejandro, Villafan-Bernal, Jose Rafael, Rojas-Zepeda, Lourdes, Becerra-Navarro, Norma Patricia, Casillas-Barrera, Manuel, Pichardo-Cuevas, Mauricio, Muñoz-Manrique, Cinthya, Cortes-Ramirez, Ivan Alonso, Espino-Y-Sosa, Salvador
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Sprache:eng
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Zusammenfassung:Preeclampsia (PE) and COVID-19 share a common vascular-endothelial physiopathological pathway that may aggravate or worsen women's outcomes when both coexist. This study aims to evaluate the association of sFlt-1 levels and adverse maternal outcomes among positive SARS-CoV-2 pregnant women with and without hypertensive disorders of pregnancy (HDP). We performed a multicenter retrospective cohort study of pregnant women with confirmed SARS-CoV-2 infection that required hospital admission. The exposed cohort comprised women with a diagnosis of an HDP. The primary outcome was a composite definition of adverse maternal outcome. The association between predictors and the main and secondary outcomes was assessed using an elastic-net regression which comprised a Lasso and Ridge regression method for automatic variable selection and penalization of non-statistically significant coefficients using a 10-fold cross-validation where the best model if automatically chosen by the lowest Akaike information criterion (AIC) and Bayesian information criteria (BIC). Among 148 pregnant women with COVID-19, the best predictive model comprised sFlt-1 MoMs [odds ratio (OR): 5.13; 95% CI: 2.19-12.05], and HDP (OR: 32.76; 95% CI: 5.24-205). sFlt-1 MoMs were independently associated with an increased probability of an adverse maternal outcome despite adjusting for HDP. Our study shows that sFlt-1 is an independent predictor of adverse outcomes in women with SARS-CoV-2 despite hypertension status.
ISSN:2296-858X
2296-858X
DOI:10.3389/fmed.2022.894633