Frontiers in Medicine (May 2022)

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

  • Jose Antonio Hernandez-Pacheco,
  • Jose Antonio Hernandez-Pacheco,
  • Johnatan Torres-Torres,
  • Johnatan Torres-Torres,
  • Johnatan Torres-Torres,
  • Raigam Jafet Martinez-Portilla,
  • Raigam Jafet Martinez-Portilla,
  • Juan Mario Solis-Paredes,
  • Guadalupe Estrada-Gutierrez,
  • Paloma Mateu-Rogell,
  • Paloma Mateu-Rogell,
  • Paloma Mateu-Rogell,
  • Miguel Angel Nares-Torices,
  • Mario Enmanuel Lopez-Marenco,
  • Keren Rachel Escobedo-Segura,
  • Alejandro Posadas-Nava,
  • Jose Rafael Villafan-Bernal,
  • Jose Rafael Villafan-Bernal,
  • Lourdes Rojas-Zepeda,
  • Lourdes Rojas-Zepeda,
  • Norma Patricia Becerra-Navarro,
  • Manuel Casillas-Barrera,
  • Mauricio Pichardo-Cuevas,
  • Cinthya Muñoz-Manrique,
  • Cinthya Muñoz-Manrique,
  • Ivan Alonso Cortes-Ramirez,
  • Salvador Espino-y-Sosa,
  • Salvador Espino-y-Sosa

DOI
https://doi.org/10.3389/fmed.2022.894633
Journal volume & issue
Vol. 9

Abstract

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BackgroundPreeclampsia (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).MethodsWe 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).ResultsAmong 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.ConclusionsOur study shows that sFlt-1 is an independent predictor of adverse outcomes in women with SARS-CoV-2 despite hypertension status.

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