| アブストラクト | BACKGROUND: 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(R) 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(R)-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(R)-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. |
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| 投稿者 | Wambua, Steven; Crowe, Francesca; Thangaratinam, Shakila; O'Reilly, Dermot; McCowan, Colin; Brophy, Sinead; Yau, Christopher; Nirantharakumar, Krishnarajah; Riley, Richard |
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| 組織名 | Institute of Applied Health Research, College of Medical and Dental Sciences,;University of Birmingham, Edgbaston, Birmingham, UK. ssw107@student.bham.ac.uk.;University of Birmingham, Edgbaston, Birmingham, UK.;WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and;Systems Research, University of Birmingham, Birmingham, UK.;Department of Obstetrics and Gynaecology, Birmingham Women's and Children's NHS;Foundation Trust, Birmingham, UK.;Centre for Public Health, Queen's University Belfast, Belfast, UK.;School of Medicine, University of St Andrews, St Andrews, UK.;Data Science, Medical School, Swansea University, Swansea, UK.;Big Data Institute, University of Oxford, Li Ka Shing Centre for Health;Information and Discovery, Old Road Campus, Oxford, OX3 7LF, UK.;Nuffield Department of Women's & Reproductive Health, University of Oxford, Level;3 Women's Centre, John Radcliffe Hospital, Oxford, OX3 9DU, UK.;Health Data Research, London, UK. |
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