Protocol for development and validation of postpartum cardiovascular disease (CVD) risk prediction model incorporating reproductive and pregnancy-related candidate predictors

Diagnostic and Prognostic Research

Protocol

Christopher Yau¹  Dermot O’Reilly²  Sinead Brophy³  Steven Wambua⁴  Krishnarajah Nirantharakumar⁴  Richard Riley⁴  Francesca Crowe⁴  Colin McCowan⁵  Shakila Thangaratinam⁶ 

[1] Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, OX3 7LF, Oxford, UK;Nuffield Department of Women’s & Reproductive Health, University of Oxford, Level 3 Women’s Centre, John Radcliffe Hospital, OX3 9DU, Oxford, UK;Health Data Research, London, UK;Centre for Public Health, Queen’s University Belfast, Belfast, UK;Data Science, Medical School, Swansea University, Swansea, UK;Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK;School of Medicine, University of St Andrews, St Andrews, 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;

关键词: Prediction modeling;    Cardiovascular disease;    Pregnant women;    Prognosis;    Pregnancy complications;   

DOI  :  10.1186/s41512-022-00137-7

 received in 2022-08-02, accepted in 2022-12-07,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundCardiovascular 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.MethodsUsing 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.DiscussionThe 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.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

Fig. 1