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Updated: Aug 16, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Protocol for development and validation of postpartum cardiovascular disease (CVD) risk prediction model
Steven Wambua1, Francesca Crowe2, Shakila Thangaratinam3,4
1Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK. ssw107@student.bham.ac.uk.
Insights
A new cardiovascular disease (CVD) risk model will be developed for postpartum women, incorporating pregnancy complications. This aims to improve risk prediction beyond current methods like QRISK®-3 for better preventive care.
Area of Science:
- Cardiovascular health and women's health research.
- Development of predictive algorithms for chronic diseases.
Background:
- Cardiovascular disease (CVD) is a major health concern for women, impacting quality of life and healthcare costs.
- Existing CVD risk scores (e.g., QRISK®-3) do not account for pregnancy complications, a known risk factor for future CVD.
- There is a need for improved CVD risk prediction in postpartum women.
Purpose of the Study:
- To develop and validate a novel 10-year CVD risk prediction model for postpartum women.
- To assess the added value of incorporating pregnancy-related risk factors into CVD risk prediction.
- To enhance preventive strategies for women at high risk of CVD.
Main Methods:
- Utilizing candidate predictors from QRISK®-3 and identified pregnancy risk factors.
- Employing time-to-event Cox proportional hazards models for risk prediction.
- Developing and validating the model using the Clinical Practice Research Datalink (CPRD) and Secure Anonymised Information Linkage (SAIL) databanks.
Main Results:
- A validated 10-year CVD risk prediction model for postpartum women will be produced.
- The study will quantify the predictive value of pregnancy complications for CVD risk.
- The model's performance will be assessed through internal and external validation.
Conclusions:
- The developed model will integrate crucial pregnancy-related factors absent in current CVD risk assessments.
- This research underscores the significance of including pregnancy history in postpartum CVD risk prediction.
- The findings aim to facilitate more accurate risk stratification and targeted interventions for women's cardiovascular health.
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.
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