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

Journal article

Funding: 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.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® 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.Peer reviewe

Authors: Wambua, S; Crowe, F; Thangaratinam, S; O’Reilly, D; McCowan, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: Diagnostic and Prognostic Research
• Volume: 6
• Issue: 1
• Pages: 23-23
• Article number: 23
• Publication date: 2022-12-19
• DOI: 10.1186/s41512-022-00137-7
• EISSN: 2397-7523
• ISSN: 2397-7523
• Language: English
• Keywords: Medicine; Risk assessment; Predictive modelling; Machine learning; Internal medicine; Framingham Risk Score; Population; Environmental health; Computer science; Pregnancy; Disease