Development and Validation of a Predictive Model for Maternal Cardiovascular Morbidity Events in Patients With Hypertensive Disorders of Pregnancy.

Journal: Anesthesia and analgesia
Published Date: Nov 6, 2024

Abstract

BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a major contributor to maternal morbidity, mortality, and accelerated cardiovascular (CV) disease. Comorbid conditions are likely important predictors of CV risk in pregnant people. Currently, there is no way to predict which people with HDP are at risk of acute CV complications. We developed and validated a predictive model for all CV events and for heart failure, renal failure, and cerebrovascular events specifically after HDP.

Authors

  • Marie-Louise Meng
    From the Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina.
  • Yuqi Li
    Department of Urology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China; Public Center of Experimental Technology, Southwest Medical University, Luzhou, Sichuan 646000, China.
  • Matthew Fuller
    From the Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina.
  • Quinn Lanners
    Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina.
  • Ashraf S Habib
    From the Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina.
  • Jerome J Federspiel
    Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Duke University School of Medicine, Durham, North Carolina.
  • Johanna Quist-Nelson
    Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of North Carolina, Chapel Hill, North Carolina.
  • Svati H Shah
    Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina.
  • Michael Pencina
    Duke Clinical Research Institute, Durham, NC, USA.
  • Kim Boggess
    Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of North Carolina, Chapel Hill, North Carolina.
  • Vijay Krishnamoorthy
    From the Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina.
  • Matthew Engelhard
    Department of Biostatistics and Bioinformatics, Duke University School of Medicine, United States of America; Duke AI Health, United States of America. Electronic address: m.engelhard@duke.edu.

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