Development and Internal Validation of a Model Predicting Premature Cardiovascular Disease Among Women With Hypertensive Disorders of Pregnancy: A Population‐Based Study in Quebec, Canada
Bibliographic record
Abstract
Background Hypertensive disorders of pregnancy (HDP) are associated with an increased risk of premature cardiovascular disease (CVD), but existing cardiovascular prediction models do not adequately capture risks in young women. We developed a model to predict the 10-year risk of premature CVD and mortality among women who have HDP. Methods and Results Using a population-based cohort of women with HDP who delivered between April 1989 and March 2017 in Quebec, Canada, we developed a 10-year CVD risk model using Cox proportional hazards regression. Women aged 18 to 45 years were followed from their first HDP-complicated delivery until March 2018. We assessed performance of the model based on discrimination, calibration, and risk stratification ability. Internal validity was assessed using the bootstrap method. The cohort included 95 537 women who contributed 1 401 084 person-years follow-up. In total, 4024 (4.2%) of women were hospitalized for CVD, of which 1585 events (1.6%) occurred within 10 years of follow-up. The final model had modest discriminatory performance (area under the receiver operating characteristic curve, 0.66; 95% CI, 0.65-0.67) and good calibration with slope of 0.95 and intercept of -0.19. There was moderate classification accuracy (likelihood ratio+: 5.90; 95% CI, 5.01-6.95) in the highest-risk group upon risk stratification. Conclusions Overall, our model had modest performance in predicting the 10-year risk of premature CVD for women with HDP. We recommend the addition of clinical variables, and external validation, before consideration for clinical use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".