Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study.

Journal: PLoS medicine
PMID: 39903757

Abstract

BACKGROUND: Preeclampsia is a potentially life-threatening pregnancy complication. Among women whose pregnancies are complicated by preeclampsia, the Preeclampsia Integrated Estimate of RiSk (PIERS) models (i.e., the PIERS Machine Learning [PIERS-ML] model, and the logistic regression-based fullPIERS model) accurately identify individuals at greatest or least risk of adverse maternal outcomes within 48 h following admission. Both models were developed and validated to be used as part of initial assessment. In the United Kingdom, the National Institute for Health and Care Excellence (NICE) recommends repeated use of such static models for ongoing assessment beyond the first 48 h. This study evaluated the models' performance during such consecutive prediction.

Authors

  • Guiyou Yang
    Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
  • Tünde Montgomery-Csobán
    Department of Mathematics and Statistics, University of Strathclyde, Glasgow, United Kingdom.
  • Wessel Ganzevoort
    Department of Obstetrics and Gynecology, Amsterdam University Medical Centers, location AMC, Amsterdam, the Netherlands.
  • Sanne J Gordijn
    Department of Obstetrics and Gynecology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
  • Kimberley Kavanagh
    Department of Mathematics and Statistics, University of Strathclyde, Glasgow, United Kingdom.
  • Paul Murray
    Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, United Kingdom.
  • Laura A Magee
    Institute of Women and Children's Health, School of Life Course and Population Sciences, King's College London, London, United Kingdom.
  • Henk Groen
    Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
  • Peter von Dadelszen
    Institute of Women and Children's Health, School of Life Course and Population Sciences, King's College London, London, United Kingdom.

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