Risk of venous thromboembolism in pregnant patients with active malignancy: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Cancer currently occurs in about 1 in 1000 pregnancies. Both active malignancy and pregnancy are individual risk factors for venous thromboembolism (VTE). The purpose of this systematic review/meta-analysis was to evaluate the rate of VTE in pregnant patients with active malignancy compared with pregnant patients without malignancy. MATERIAL AND METHODS: Embase, Medline/PubMed, Cochrane Database, and clinicaltrial.gov were search by a trained librarian from inception until June 2021, and limited to English and French language human studies using keywords related to pregnancy, neoplasm, and thrombosis. This study was prospectively registered with PROSPERO (CRD42021245886). Title, abstract, and full-text review was performed using the Covidence data management system. Two authors reviewed the studies independently. Of the 3821 articles screened, seven cohort studies were included that reported VTE rate in patients with active malignancy in pregnancy. RESULTS: A total of 5928 individuals had active malignancy and pregnancy. Active malignancy in pregnancy significantly increased the odds of a VTE (odds ratio [OR] 6.8, 95% confidence interval [CI] 3.8-12.1). Specifically, patients with thyroid (OR 2.7, 95% CI 1.3-6.3), cervix (OR 6.6, 95% CI 2.4-18.0), or other gynecological (OR 10.6, 95% CI 4.4-25.8) cancers; Hodgkin's lymphoma (OR 8.7, 95% CI 3.3-23.4); or acute leukemia (OR 17.1, 95% CI 10.9-26.8) all had increased odds, whereas those with brain cancer (OR 6.1, 95% CI 0.4-98.2), breast cancer (OR 2.5, 95% CI 0.3-17.4), malignant melanoma (OR 5.5, 95% CI 0.3-88.1), or non-Hodgkin's lymphoma (OR 3.2, 95% CI 0.8-12.9) malignancies did not have statistically significant increased odds for VTE. No studies reported whether prophylactic anticoagulation was used during pregnancy in this population; nor did they report timing in pregnancy of the VTE. The absolute risk for VTE in those with active malignancy was 0.9% compared with 0.2% in those without active malignancy in pregnancy. CONCLUSIONS: Pregnancy with active malignancy confers a significant increased risk for VTE compared with pregnancy alone. Given this finding, prophylactic anticoagulation during pregnancy and postpartum could be considered in this patient population. Data are underpowered to make firm recommendations per cancer type.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".