Abstract 15319: Impact of Atherosclerotic Cardiovascular Disease Risk Factors on Pregnancy Outcomes in Women With Heart Disease
Bibliographic record
Abstract
Introduction: The impact of atherosclerotic cardiovascular disease (ASCVD) risk factors on pregnancy outcomes in women with pre-existing heart disease (HD) has not been examined. Aim: To determine the risk of major adverse cardiovascular events (MACE), preeclampsia and fetal events in women with HD stratified according to the presence of ASCVD risk factors. Methods: We studied a consecutive cohort of pregnant women with heart disease. ASCVD risk factors included any of the following: obesity, hypertension, dyslipidemia, diabetes or smoking. Primary outcomes were MACE (heart failure, cardiac arrest, CV death, stroke and myocardial infarction), preeclampsia and adverse fetal events (pre-term birth, small for gestational age, intraventricular hemorrhage, neonatal death and respiratory distress syndrome). Univariate logistic regression was used to determine the odds of adverse outcomes. Results: In total, 1656 pregnancies (congenital heart disease n=1041, acquired heart disease n= 420, isolated arrhythmia n=195) were included. At least one ASCVD risk factor was present in 24% of pregnancies. Overall, MACE occurred in 7.1%, preeclampsia in 4.3% and adverse fetal events in 30.1% of the pregnancies. Compared to pregnancies in women without ASCVD risk factors, those with ASCVD risk factors were more likely to have pregnancies complicated by MACE (9.7% vs 6.3%, p= 0.025), preeclampsia (8.3% vs 3.4%, p < 0.001), and fetal events (38.5% vs 27.5%, p <0.001). There were differences in maternal and fetal outcomes with or without ASCVD risk factors when stratified by diagnosis (acquired heart disease, congenital heart disease, isolated arrhythmias) (Figure 1). Conclusions: The presence of ASCVD risk factors in women with heart disease is associated with worse maternal and fetal outcomes. Modification of ASCVD risk factors may improve pregnancy outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".