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. 2020 Apr 21;15(4):e0230955.
doi: 10.1371/journal.pone.0230955. eCollection 2020.

Systematic review of prediction models for gestational hypertension and preeclampsia

Edward Antwi  1   2 , Mary Amoakoh-Coleman  1   3 , Dorice L Vieira  4 , Shreya Madhavaram  4 , Kwadwo A Koram  3 , Diederick E Grobbee  1 , Irene A Agyepong  2 , Kerstin Klipstein-Grobusch  1   5
Affiliations

Systematic review of prediction models for gestational hypertension and preeclampsia

Edward Antwi et al. PLoS One. .

Abstract

Introduction: Prediction models for gestational hypertension and preeclampsia have been developed with data and assumptions from developed countries. Their suitability and application for low resource settings have not been tested. This review aimed to identify and assess the methodological quality of prediction models for gestational hypertension and pre-eclampsia with reference to their application in low resource settings.

Methods: Using combinations of keywords for gestational hypertension, preeclampsia and prediction models seven databases were searched to identify prediction models developed with maternal data obtained before 20 weeks of pregnancy and including at least three predictors (Prospero registration CRD 42017078786). Prediction model characteristics and performance measures were extracted using the CHARMS, STROBE and TRIPOD checklists. The National Institute of Health quality assessment tools for observational cohort and cross-sectional studies were used for study quality appraisal.

Results: We retrieved 8,309 articles out of which 40 articles were eligible for review. Seventy-seven percent of all the prediction models combined biomarkers with maternal clinical characteristics. Biomarkers used as predictors in most models were pregnancy associated plasma protein-A (PAPP-A) and placental growth factor (PlGF). Only five studies were conducted in a low-and middle income country.

Conclusions: Most of the studies evaluated did not completely follow the CHARMS, TRIPOD and STROBE guidelines in prediction model development and reporting. Adherence to these guidelines will improve prediction modelling studies and subsequent application of prediction models in clinical practice. Prediction models using maternal characteristics, with good discrimination and calibration, should be externally validated for use in low and middle income countries where biomarker assays are not routinely available.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flow diagram for inclusion and exclusion of relevant articles.
Fig 2
Fig 2. Frequency of predictor variables in the prediction models.
Fig 3
Fig 3. Risk of bias assessment of the prediction studies.
Fig 4
Fig 4. Forest plot of prediction models for preeclampsia.
See this image and copyright information in PMC

References

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