Investigators developed and internally validated a cardiovascular disease risk prediction model for reproductive-aged women that incorporated pregnancy-related and female-specific risk factors alongside traditional cardiovascular risk factors, showing that the expanded model may improve postpartum risk stratification despite modest discriminatory performance.
Using linked data from the UK Clinical Practice Research Datalink, Hospital Episode Statistics, and national mortality records, the investigators developed a prognostic model for women aged 15 to 45 years who had a live birth or stillbirth. The primary cohort included 262,891 women with one randomly selected delivery between 2000 and 2017 who had no history of cardiovascular disease and entered follow-up 42 days postdelivery. A secondary cohort of 109,858 women with first deliveries was used to evaluate model performance. The investigators compared a base model containing traditional cardiovascular risk factors with a full model incorporating pregnancy-related and sex-specific predictors, with internal validation performed using bootstrap resampling.
The outcome was incident cardiovascular disease, defined as a composite of coronary artery disease, coronary revascularization, myocardial infarction, cerebrovascular disease, peripheral vascular disease, unstable angina, and cardiovascular-related mortality.
During a median follow-up of 3.8 years, 943 participants experienced a cardiovascular event, corresponding to an incidence rate of 0.81 per 1,000 person-years. The final model retained traditional cardiovascular risk factors together with social deprivation, polycystic ovary syndrome, oral contraceptive use, depression, thyroid disorders, hypertensive disorders of pregnancy, gestational diabetes, preterm birth, small-for-gestational-age birthweight, parity, and a history of pregnancy complications.
The full model outperformed the model based on traditional cardiovascular risk factors alone. Following internal validation, the optimism-corrected C-statistic was 0.637 compared with 0.613 for the base model, while the calibration slope was 0.919. Similar improvements were observed in the first-delivery cohort.
Pregnancy-related factors contributed substantially to model performance. Hypertensive disorders of pregnancy, gestational diabetes, and preterm birth were among the strongest pregnancy-specific predictors retained in the model, highlighting the importance of pregnancy history when assessing cardiovascular risk in younger women. In the first-delivery cohort, gestational diabetes, hypertensive disorders of pregnancy, preterm birth, and thyroid disease were the principal female-specific predictors beyond traditional cardiovascular risk factors.
Risk classification analyses showed greater discrimination among women with first deliveries compared with in the random-delivery cohort. At the predefined high-risk threshold, the likelihood ratio was 6.01 in the first-delivery cohort compared with 3.83 in the random-delivery cohort, suggesting better identification of higher-risk women experiencing their first pregnancy.
The investigators noted several limitations. Adverse cardiovascular events were relatively uncommon, which may have limited the model's discriminatory performance. Follow-up was not long enough to estimate lifetime cardiovascular risk, several predictors required multiple imputation because of missing data, and the model underwent internal rather than external validation. The investigators said external validation in more ethnically diverse populations with longer follow-up is needed before the model can be considered for broader clinical use.
Incorporating pregnancy-related and female-specific factors into postpartum cardiovascular risk assessment may improve identification of reproductive-aged women at increased risk of future cardiovascular disease, although additional refinement and external validation are needed.
"Pregnancy-related and sex-specific risk factors provide additional prognostic information beyond established [cardiovascular disease] risk factors for the identification of reproductive-aged women at risk of [cardiovascular disease]. Despite modest discrimination, our models are a first step toward risk stratification in this patient population," wrote lead study author Sonia M. Grandi, of the Child Health Evaluative Sciences Program at The Hospital for Sick Children in Canada, and colleagues.
The study was funded by the Canadian Institutes of Health Research. Full disclosures of the study authors can be found in the study.
Source: JACC: Advances
