Evidence map›Paper›PMID 40883735›Full record

ArticleBMC medicine2025

Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors.

Steven Wambua, Francesca L Crowe, Shakila Thangaratinam, Dermot O'Reilly, Colin McCowan, Sinead Brophy, Christopher Yau, Krishnarajah Nirantharakumar, Richard D Riley, Kym I E Snell and 1 more

Abstract readValidation Study
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Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Steven WambuaDepartment of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
Francesca L CroweDepartment of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
Shakila ThangaratinamInstitute of Metabolism and Systems Research, WHO Collaborating Centre for Global Women's Health, University of Birmingham, Birmingham, UK.
Dermot O'ReillyCentre for Public Health, Queen's University Belfast, Belfast, UK.
Colin McCowanSchool of Medicine, University of St Andrews, St Andrews, UK.
Sinead BrophyData Science, Medical School, Swansea University, Swansea, UK.
Christopher YauBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
Krishnarajah NirantharakumarDepartment of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
Richard D RileyDepartment of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
Kym I E SnellDepartment of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK. k.snell@bham.ac.uk.
MuM-PreDiCT Group

Funding

British Heart Foundation (BHF) Data Science Centre and HDR-UK-Turing Wellcome PhD Programme SP/19/3/34678Medical Research Council the National Institute for Health Research in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council. MR/W014432/1Wellcome Trust
6 · The paper itself

Abstract

backgroundEach year, over 700,000 pregnancies occur in the UK, with up to 10% affected by complications such as hypertensive disorders of pregnancy and gestational diabetes mellitus. Pregnancy-related complications and reproductive factors are associated with an increased risk of cardiovascular disease (CVD) later in life. Our aim was to determine whether adding pregnancy factors to a prediction model with established CVD risk factors improves 10-year risk prediction of CVD in postpartum women, using QRISK®-3 as a benchmark model.

methodsWe used a population-based retrospective cohort of women aged 15 to 49 who had been pregnant from the Clinical Practice Research Datalink (CPRD) primary care database. Women who were CVD-free were followed from 6 months postpartum. We evaluated the performance of QRISK®-3 and updated the risk prediction model using established risk factors for CVD from QRISK®-3 and additional risk factors specific to pregnancy. Models were developed using Cox-proportional hazards regression for CVD within 10 years. Models were evaluated and compared using measures of overall model fit, calibration, discrimination and clinical utility.

resultsAmong 567,667 eligible women, 2175 (0.38%) experienced a CVD event within 10 years. The median follow-up was 4 years. Of the additional pregnancy factors, gestational hypertension, preeclampsia, miscarriage, stillbirth, postnatal depression, gravidity, endometriosis and polycystic ovary syndrome remained associated with CVD after adjusting for other established risk factors of CVD. Adding pregnancy factors to those from QRISK®-3 led to marginal improvements in model performance (QRISK®-3 C-statistic: 0.703 (95% CI 0.687 to 0.718), updated model C-statistic: 0.726 (95% CI 0.711 to 0.740) Although calibration did not improve overall, subgroup analysis showed better calibration in women with a history of pre-eclampsia, postnatal depression and preterm birth using the updated model. The clinical utility was improved for updated models.

conclusionsThe updated risk prediction models resulted in marginal improvement in discrimination and calibration compared to QRISK®-3 in postpartum women. This could be due to the known association of pregnancy-related complications with established risk factors of CVD. Although the overall predictive performance and calibration of the updated models was similar, the updated model resulted in better clinical utility.

Indexed as

Cardiovascular DiseasesPostpartum PeriodAdolescentAdultFemaleHumansMiddle AgedPregnancyRetrospective StudiesRisk AssessmentRisk FactorsUnited KingdomYoung AdultCardiovascular diseasePregnancy complicationsQRISK®-3Risk prediction

Identifiers

PMID40883735
PMCPMC12398157

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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.