Weighted Cumulative Exposure Modelling to Assess the Association Between Reproductive Factors and Future Cardiovascular Disease in Women.

Background: The occurrence of reproductive or pregnancy events, such as severe maternal morbidity (SMM), may reveal a predisposition to chronic disease and premature mortality. However, most studies have examined these exposures without considering their timing, severity, or recurrence.

Objectives: We propose using a weighted cumulative exposure (WCE) modelling approach to flexibly describe the relationship between reproductive events and longer-term health outcomes in a longitudinal cohort of pregnant women.

Methods: Application of the WCE modelling approach is accomplished in three steps. First, relative weights are estimated from a multivariable Cox proportional hazards model corresponding to the association of each reproductive risk factor with a given health outcome. Then, a longitudinal dataset is constructed in which all reproductive predictors are recorded at regular intervals (every 3 months), beginning 42 days after each woman's first birth in the cohort and ending at an outcome or censoring event. A new multivariable Cox model applied to this longitudinal dataset, incorporating time-varying WCE-derived reproductive risk scores along with simple time-varying reproductive and non-reproductive predictors, is estimated. Finally, adjusted WCE-based hazard ratios (HR) associated with different reproductive event exposure histories are calculated.

Results: In the cohort of 1,992,972 births in Canada (excluding Quebec), 2008-2021, with mean (SD) follow-up time in the longitudinal dataset of 7.3 ± 3.8 years, we propose to use the WCE approach to predict outcomes such as premature cardiovascular disease (16,846 cardiovascular hospitalisations observed, or 1.19 per 1000 person-years).

Conclusions: Use of flexible WCE modelling to quantify risks of pregnancy events such as SMM, adjusted for reproductive and non-reproductive CVD risk factors, will account for variation in timing and severity of these events and will capture their cumulative effects across a woman's reproductive trajectory. This approach can refine estimates of etiologic associations and inform novel clinical prediction models with the potential to predict postpartum long-term health outcomes for a given woman based on her unique reproductive history.