Migraine and pregnancy-related headaches as a risk factor for cardiovascular and cerebrovascular events in pregnancy: a systematic review and meta-analysis of over 94 million pregnancies
Bibliographic record
Abstract
BACKGROUND: Migraine is prevalent among women of childbearing age and is associated with increased long-term cardiovascular and cerebrovascular risk. Pregnancy, a hypercoagulable state, may potentiate these risks. This study aimed to quantify the association between migraine or pregnancy-related headaches and cerebrovascular and cardiovascular events during pregnancy and the postpartum period. METHODS: We conducted a PRISMA-compliant systematic review and meta-analysis of observational studies comparing pregnant women with and without migraine or pregnancy-related headaches. PubMed, Scopus, and Web of Science were searched through May 8, 2025. Adjusted odds ratios (ORs) were pooled using random-effects models. RESULTS: Twelve studies encompassing 94,195,776 pregnancies met the inclusion criteria. Migraine was associated with markedly increased odds of all strokes and transient ischemic attacks (OR 10.45; 95% CI 4.27-25.57) and ischemic stroke (OR 7.14; 95% CI 2.51-20.31). Hemorrhagic stroke risk was elevated but not statistically significant overall (OR 2.25; 95% CI 0.99-5.18), while subarachnoid hemorrhage showed a 69% increased odds. Regarding cardiovascular events: myocardial infarction risk increased by 96%, peripartum cardiomyopathy odds were 2.68-fold (95% CI 1.73-4.14), and spontaneous coronary artery dissection odds were 9.21-fold higher (95% CI 3.72-22.82). All included studies were rated as "good" quality by the Newcastle-Ottawa Scale. CONCLUSIONS: Migraine and pregnancy-related headaches are independent risk factors for a broad spectrum of cerebrovascular and cardiovascular events during pregnancy and the puerperium. These findings highlight the need for heightened clinical surveillance, targeted cardiovascular risk counselling, and multidisciplinary management strategies for this population.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".