Hypertensive disorders of pregnant women with heart disease: the ESC EORP ROPAC Registry
Bibliographic record
Abstract
AIMS: Hypertensive disorders of pregnancy (HDP) occur in 10% of pregnancies in the general population, pre-eclampsia specifically in 3-5%. Hypertensive disorders of pregnancy may have a high prevalence in, and be poorly tolerated by, women with heart disease. METHODS AND RESULTS: The prevalence and outcomes of HDP (chronic hypertension, gestational hypertension or pre-eclampsia) were assessed in the ESC EORP ROPAC (n = 5739), a worldwide prospective registry of pregnancies in women with heart disease.The overall prevalence of HDP was 10.3%, made up of chronic hypertension (5.9%), gestational hypertension (1.3%), and pre-eclampsia (3%), with significant differences between the types of underlying heart disease (P < 0.05). Pre-eclampsia rates were highest in women with pulmonary arterial hypertension (PAH) (11.1%), cardiomyopathy (CMP) (7.1%), and ischaemic heart disease (IHD) (6.3%). Maternal mortality was 1.4 and 0.6% in women with vs. without HDP (P = 0.04), and even 3.5% in those with pre-eclampsia. All pre-eclampsia-related deaths were post-partum and 50% were due to heart failure. Heart failure occurred in 18.5 vs. 10.6% of women with vs. without HDP (P < 0.001) and in 29.1% of those with pre-eclampsia. Perinatal mortality was 3.1 vs. 1.7% in women with vs. without HDP (P = 0.019) and 4.7% in those with pre-eclampsia. CONCLUSION: Hypertensive disorders of pregnancy and pre-eclampsia rates were higher in women with CMP, IHD, and PAH than in the general population. Adverse outcomes were increased in women with HDP, and maternal mortality was strikingly high in women with pre-eclampsia. The combination of HDP and heart disease should prompt close surveillance in a multidisciplinary context and the diagnosis of pre-eclampsia requires hospital admission and continued monitoring during the post-partum period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".