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dc.contributor.authorWambua, Steven
dc.contributor.authorCrowe, Francesca
dc.contributor.authorThangaratinam, Shakila
dc.contributor.authorO'Reilly, Dermot
dc.contributor.authorMcCowan, Colin
dc.contributor.authorBrophy, Sinead
dc.contributor.authorYau, Christopher
dc.contributor.authorNirantharakumar, Krishnarajah
dc.contributor.authorRiley, Richard
dc.contributor.authorMuM-PreDiCT Group
dc.identifier.citationWambua , S , Crowe , F , Thangaratinam , S , O'Reilly , D , McCowan , C , Brophy , S , Yau , C , Nirantharakumar , K , Riley , R & MuM-PreDiCT Group 2022 , ' Protocol for development and validation of postpartum cardiovascular disease (CVD) risk prediction model incorporating reproductive and pregnancy-related candidate predictors ' , Diagnostic and Prognostic Research , vol. 6 , 23 .
dc.identifier.otherPURE: 282568637
dc.identifier.otherPURE UUID: d0d0e1b7-7a95-4715-8479-0ca3e4b89cfb
dc.identifier.otherPubMedCentral: PMC9761974
dc.identifier.otherORCID: /0000-0002-9466-833X/work/125302968
dc.descriptionFunding: This work is funded by the Strategic Priority Fund “Tackling multimorbidity at scale” programme (grant number MR/W014432/1) delivered by the Medical Research Council and 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. SW PhD studentship is funded by the British Heart Foundation (BHF) Data Science Centre (BHF grant number SP/19/3/34678, awarded to Health Data Research (HDR)). His PhD is also supported through the HDR-UK-Turing Wellcome PhD Programme.en
dc.description.abstractBackground Cardiovascular disease (CVD) is a leading cause of death among women. CVD is associated with reduced quality of life, significant treatment and management costs, and lost productivity. Estimating the risk of CVD would help patients at a higher risk of CVD to initiate preventive measures to reduce risk of disease. The Framingham risk score and the QRISK® score are two risk prediction models used to evaluate future CVD risk in the UK. Although the algorithms perform well in the general population, they do not take into account pregnancy complications, which are well known risk factors for CVD in women and have been highlighted in a recent umbrella review. We plan to develop a robust CVD risk prediction model to assess the additional value of pregnancy risk factors in risk prediction of CVD in women postpartum. Methods Using candidate predictors from QRISK®-3, the umbrella review identified from literature and from discussions with clinical experts and patient research partners, we will use time-to-event Cox proportional hazards models to develop and validate a 10-year risk prediction model for CVD postpartum using Clinical Practice Research Datalink (CPRD) primary care database for development and internal validation of the algorithm and the Secure Anonymised Information Linkage (SAIL) databank for external validation. We will then assess the value of additional candidate predictors to the QRISK®-3 in our internal and external validations. Discussion The developed risk prediction model will incorporate pregnancy-related factors which have been shown to be associated with future risk of CVD but have not been taken into account in current risk prediction models. Our study will therefore highlight the importance of incorporating pregnancy-related risk factors into risk prediction modeling for CVD postpartum.
dc.relation.ispartofDiagnostic and Prognostic Researchen
dc.rightsCopyright © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
dc.subjectPrediction modelingen
dc.subjectCardiovascular diseaseen
dc.subjectPregnant womenen
dc.subjectPregnancy complicationsen
dc.subjectRB Pathologyen
dc.subjectRC Internal medicineen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleProtocol for development and validation of postpartum cardiovascular disease (CVD) risk prediction model incorporating reproductive and pregnancy-related candidate predictorsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.contributor.institutionUniversity of St Andrews. Population and Behavioural Science Divisionen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.description.statusPeer revieweden

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