Myocardial infarction in pregnancy and postpartum in the UK
Bibliographic record
Abstract
AIM: Cardiac disease is a leading cause of maternal death in the developed world, responsible for one-fifth of all maternal deaths in the UK. The aim of this study was to estimate the incidence of myocardial infarction (MI) in pregnancy and up to one week postpartum in the UK and describe risk factors, management and outcomes. METHODS: A prospective population-based study with nested case control analysis used the UK Obstetric Surveillance System to identify all women in the UK with MI in pregnancy (in the years 2005-2010). A control group of 1360 women was used for comparison. Multivariable unconditional logistic regression was conducted to identify potential risk factors for MI in pregnancy and calculate adjusted odds ratios with 95% confidence intervals. RESULTS: Twenty-five cases of MI in pregnancy were reported, giving an estimated incidence of 0.7 per 100,000 maternities (95%CI 0.5-1.1). Maternal age, smoking, hypertension, twin pregnancy and pre-eclampsia were independently associated with MI in pregnancy. Fifteen (60%) women underwent coronary angiography; nine (60%) had coronary atherosclerosis, three (21%) had coronary artery dissection, one (7%) had a coronary thrombus and two (13%) had normal coronary arteries. Nine women had angioplasty +/- stenting and two were thrombolysed. No women died. CONCLUSIONS: Many risk factors are both recognisable and modifiable. Management of MI in pregnancy was highly variable indicating a clear need for further information regarding the safety and outcomes of different interventions. The addition of pregnancy status as a compulsory field in cardiac audit databases would enable routine collection of this information.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".