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Could Pregnancy Complications Predict CVD Risk?
Investigators found that adding pregnancy-related and female-specific factors modestly improved postpartum cardiovascular risk prediction compared with traditional risk factors alone.
To develop and validate a cardiovascular disease risk prediction model for reproductive-aged women incorporating pregnancy-related and female-specific risk factors alongside traditional cardiovascular risk factors.
Approach:
Cohorts: The primary cohort included 262,891 women aged 15 to 45 years with a live birth or stillbirth, while a secondary cohort of 109,858 women with first deliveries was used to evaluate model performance.
Model Comparison: A base model with traditional cardiovascular risk factors was compared to a full model that included pregnancy-related and sex-specific predictors.
Outcome Definition: Incident cardiovascular disease was defined as a composite of various cardiovascular events and mortality.
Follow-Up: Participants were followed for a median of 3.8 years.
Key Findings:
943 participants experienced a cardiovascular event, with an incidence rate of 0.81 per 1,000 person-years, including coronary artery disease, myocardial infarction, and cardiovascular-related mortality.
The full model showed improved performance with an optimism-corrected C-statistic of 0.637 compared to 0.613 for the base model.
Pregnancy-related factors, particularly hypertensive disorders of pregnancy, gestational diabetes, and preterm birth, were significant predictors.
Interpretation:
Incorporating pregnancy-related and female-specific factors may enhance postpartum cardiovascular risk assessment.
Limitations:
Adverse cardiovascular events were relatively uncommon, potentially limiting the model's discriminatory performance.
Follow-up duration was insufficient to estimate lifetime cardiovascular risk.
Some predictors required multiple imputation due to missing data.
The model underwent internal validation only, lacking external validation.
Conclusion:
Further refinement and external validation are necessary before the model can be used clinically.
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