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Prediction models for preeclampsia: A systematic review.

Annelien C De Kat1, Jane Hirst1, Mark Woodward2

  • 1The George Institute for Global Health, University of Oxford Le Gros Clark Building, South Parks Road, Oxford OX1 3QX, UK; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.

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Summary

Preeclampsia prediction models exist but vary widely. Further validation and calibration are needed before widespread clinical use for improved maternal and fetal health outcomes.

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Area of Science:

  • Obstetrics and Gynecology
  • Perinatal Medicine
  • Epidemiology

Background:

  • Preeclampsia poses significant risks to maternal and fetal well-being.
  • Early identification of high-risk pregnancies is crucial for prevention and treatment.
  • Despite numerous models, individualized preeclampsia prediction is not standard clinical practice.

Purpose of the Study:

  • To systematically review existing studies on preeclampsia prediction models.
  • To provide an overview of the current landscape of preeclampsia prediction research.

Main Methods:

  • A comprehensive MEDLINE search was conducted up to January 2017.
  • Included prognostic studies focused on predicting preeclampsia or related disorders.
  • Study quality was assessed using the Quality in Prognostic Studies (QUIPS) tool.

Main Results:

  • 70 studies encompassing 425,125 participants and 68 prediction models were reviewed.
  • Common predictors included medical history, BMI, blood pressure, parity, uterine artery pulsatility index, and maternal age.
  • Limited internal (4%) and external (6%) validation was reported, with no clear association between predictor type and model performance.

Conclusions:

  • Numerous preeclampsia prediction models have been developed, showing varied effectiveness.
  • High heterogeneity among studies hinders the selection of optimal models.
  • Further validation and calibration of promising models are essential for clinical implementation.