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

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

<h4>Background</h4>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 regres...

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Main Authors: Guiyou Yang, Tünde Montgomery-Csobán, Wessel Ganzevoort, Sanne J Gordijn, Kimberley Kavanagh, Paul Murray, Laura A Magee, Henk Groen, Peter von Dadelszen
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-02-01
Series:PLoS Medicine
Online Access:https://doi.org/10.1371/journal.pmed.1004509
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https://doi.org/10.1371/journal.pmed.1004509

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