Weighted Cumulative Exposure Modelling to Assess the Association Between Reproductive Factors and Future Cardiovascular Disease in Women
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".