Evidence map›Paper›PMID 31906891›Full record

ArticleBMC pregnancy and childbirth2020

Prediction of pre-eclampsia in nulliparous women using routinely collected maternal characteristics: a model development and validation study.

Ziad T A Al-Rubaie, H Malcolm Hudson, Gregory Jenkins, Imad Mahmoud, Joel G Ray, Lisa M Askie, Sarah J Lord

Abstract readComparative StudyValidation Study
In one paragraph

Article in BMC pregnancy and childbirth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  13. Artificial intelligence in obstetrics.Obstetrics & gynecology science · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ziad T A Al-RubaieSchool of Medicine, The University of Notre Dame Australia, 160 Oxford Street, Darlinghurst, NSW, 2010, Australia. ziad.alrubaie1@my.nd.edu.au.
H Malcolm HudsonNHMRC Clinical Trials Centre, Sydney Medical School, University of Sydney, Level 6 Medical Foundation Building, 92 Parramatta Road, Locked Bag 77, Camperdown, NSW, 2050, Australia.
Gregory JenkinsDepartment of Obstetrics, Westmead Hospital, Suite 110, 9 Norbrik Drive, Bella Vista, Westmead, NSW, 2153, Australia.
Imad MahmoudDepartment of Obstetrics, Auburn and Mount-Druitt and Blacktown Hospitals, Suite 108, 9 Norbrik Drive, Bella Vista, NSW, 2153, Australia.
Joel G RayDepartments of Medicine, Health Policy Management and Evaluation, and Obstetrics and Gynecology, St. Michael's Hospital, University of Toronto, 30 Bond Street, Toronto, Ontario, M5B 1W8, Canada.
Lisa M AskieNHMRC Clinical Trials Centre, Sydney Medical School, University of Sydney, Level 6 Medical Foundation Building, 92 Parramatta Road, Locked Bag 77, Camperdown, NSW, 2050, Australia.
Sarah J LordSchool of Medicine, The University of Notre Dame Australia, 160 Oxford Street, Darlinghurst, NSW, 2010, Australia.

Funding

Ziad TA Al-Rubaie is supported by an Australian Research Training Program and Collaborative Research Network PhD Scholarship through The University of Notre Dame Australia. The funding source had no role in the design of the study, the collection, analysis Notre Dame HREC approval number: 016071S; and WSLHD HREC approval number: LNR/16/WMEAD/239, LNR SSA/16/WMEAD/256.
6 · The paper itself

Abstract

backgroundGuidelines recommend identifying in early pregnancy women at elevated risk of pre-eclampsia. The aim of this study was to develop and validate a pre-eclampsia risk prediction model for nulliparous women attending routine antenatal care "the Western Sydney (WS) model"; and to compare its performance with the National Institute of Health and Care Excellence (NICE) risk factor-list approach for classifying women as high-risk.

methodsThis retrospective cohort study included all nulliparous women who gave birth in three public hospitals in the Western-Sydney-Local-Health-District, Australia 2011-2014. Using births from 2011 to 2012, multivariable logistic regression incorporated established maternal risk factors to develop and internally validate the WS model. The WS model was then externally validated using births from 2013 to 2014, assessing its discrimination and calibration. We fitted the final WS model for all births from 2011 to 2014, and compared its accuracy in predicting pre-eclampsia with the NICE approach.

resultsAmong 12,395 births to nulliparous women in 2011-2014, there were 293 (2.4%) pre-eclampsia events. The WS model included: maternal age, body mass index, ethnicity, multiple pregnancy, family history of pre-eclampsia, autoimmune disease, chronic hypertension and chronic renal disease. In the validation sample (6201 births), the model c-statistic was 0.70 (95% confidence interval 0.65-0.75). The observed:expected ratio for pre-eclampsia was 0.91, with a Hosmer-Lemeshow goodness-of-fit test p-value of 0.20. In the entire study sample of 12,395 births, 374 (3.0%) women had a WS model-estimated pre-eclampsia risk ≥8%, the pre-specified risk-threshold for considering aspirin prophylaxis. Of these, 54 (14.4%) developed pre-eclampsia (sensitivity 18% (14-23), specificity 97% (97-98)). Using the NICE approach, 1173 (9.5%) women were classified as high-risk, of which 107 (9.1%) developed pre-eclampsia (sensitivity 37% (31-42), specificity 91% (91-92)). The final model showed similar accuracy to the NICE approach when using lower risk-threshold of ≥4% to classify women as high-risk for pre-eclampsia.

conclusionThe WS risk model that combines readily-available maternal characteristics achieved modest performance for prediction of pre-eclampsia in nulliparous women. The model did not outperform the NICE approach, but has the advantage of providing individualised absolute risk estimates, to assist with counselling, inform decisions for further testing, and consideration of aspirin prophylaxis.

Indexed as

Models, StatisticalParityAustraliaFemaleForecastingHumansLogistic ModelsPre-EclampsiaPregnancyRetrospective StudiesRisk FactorsAntenatal careAustraliaMaternal healthNational Institute of health and care excellencePredictionPre-eclampsiaRisk assessmentRisk prediction model

Identifiers

PMID31906891
PMCPMC6945640

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.