Incidence of myocardial infarction in pregnancy: a systematic review and meta-analysis of population-based studies
Bibliographic record
Abstract
Aims: Cardiac disease is one of the leading causes of indirect maternal death, and myocardial infarction (MI) is one of its most common aetiologies. The objectives of this systematic review and meta-analysis were to characterize the incidence of pregnancy-associated MI (PAMI), as well as the maternal mortality and the case-fatality rates due to PAMI. Methods and results: Articles were obtained by searching electronic databases, bibliographies and conference proceedings with no language or date restrictions. Two reviewers independently selected population-based cohort and case-control studies reporting on incidence, mortality and case-fatality rates for pregnancy-associated MI. Meta-analysis was performed to estimate pooled maternal incidence, mortality and case-fatality rates. Meta-regression was performed to explore heterogeneity. Based on 17 included studies, the pooled incidence of PAMI and maternal mortality from PAMI were 3.34 (2.09-4.58) and 0.20 (0.10-0.29) per 100 000 pregnancies, respectively. The case-fatality rate was 5.03% (3.78-6.27%). Country/region (meta-regression P = 0.006) and years of study (meta-regression P = 0.04) were potential explanations for the observed heterogeneity in the pooled incidence estimates of maternal MI and its associated mortality, with more recent studies and those conducted in the USA revealing the highest rates. Conclusion: This article provides a global estimate of the incidence, mortality rate, and case fatality rate of pregnancy-associated MI. We identified higher rates of PAMI in the USA (relative to Canada and European countries) and rising rates over time. Further research regarding this population is needed, especially given rising maternal age and the increasing prevalence of cardiovascular risk factors.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".