Hypertensive Disorders in Pregnancy and the Risk of Subsequent Cardiovascular Disease
Bibliographic record
Abstract
BACKGROUND: Hypertensive disorders in pregnancy (HDP) have been shown to predict later risk of cardiovascular disease (CVD). However, previous studies have not accounted for subsequent pregnancies and their complications, which are potential confounders and intermediates of this association. METHODS: A cohort of 146 748 women with a first pregnancy was constructed using the Clinical Practice Research Datalink. HDP was defined using diagnostic codes, elevated blood pressure readings, or new use of an anti-hypertensive drug between 18 weeks' gestation and 6 weeks post-partum. The study outcomes were incident CVD and hypertension. Marginal structural Cox models (MSM) were used to account for time-varying confounders and intermediates. Time-fixed exposure defined at the first pregnancy was used in secondary analyses. RESULTS: A total of 997 women were diagnosed with incident CVD, and 6812 women were diagnosed with hypertension or received a new anti-hypertensive medication during the follow-up period. Compared with women without HDP, those with HDP had a substantially higher rate of CVD (hazard ratio (HR) 2.2, 95% confidence interval (CI) 1.7, 2.7). In women with HDP, the rate of hypertension was five times that of women without a HDP (HR 5.6, 95% CI 5.1, 6.3). With overlapping 95% CIs, the time-fixed analysis and the MSM produced consistent results for both outcomes. CONCLUSIONS: Women with HDP are at increased risk of developing subsequent CVD and hypertension. Similar estimates obtained with the MSM and the time-fixed analysis suggests that subsequent pregnancies do not confound a first episode of HDP and later CVD.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".