Pregnancy-Associated Myocardial Infarction in Alberta
Bibliographic record
Abstract
Background: Cardiac disease is the leading cause of maternal mortality in developed countries, and myocardial infarction (MI) is an important cause of pregnancy-associated morbidity and mortality. These infrequent, but very serious, events are not optimally described in the medical literature. Objectives: This study describes a 15-year consecutive, retrospective cohort of confirmed pregnancy-associated MIs (PAMIs) identified in Alberta, Canada (2003-2017). Methods: Utilizing a provincial administrative database, a cohort of women with PAMI were identified using a validated algorithm. Additional cases were identified by reviewing provincial maternal mortality records. Medical record review was conducted on each case with further details obtained via linkage with a provincial coronary heart disease registry. Available angiographic images were also reviewed. Results: Forty-three cases of PAMI were identified in Alberta between 2003 and 2017, providing a crude incidence of ∼5.64/100,000 births. Rates of PAMI increased over the study period. Of the identified MIs, 16.3% occurred antepartum (mean gestational age of 18 weeks), while 30.2% were peripartum and 53.4% occurred within 6 months postpartum (at a mean of 7.8 weeks after delivery). The most common mechanism of PAMI was spontaneous coronary artery dissection (44.2%) and this mechanism predominated postpartum. Coronary artery disease was a frequent antepartum cause of MI, whereas demand ischemia was the leading cause of peripartum MI. Maternal mortality was approximately 9%. Conclusions: PAMI is an increasing cause of maternal morbidity and mortality in Alberta. Clinicians should have a high index of suspicion for PAMI and ensure optimal management of this dangerous complication of pregnancy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".