Oral Anticoagulant Use in Patients with Morbid Obesity: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Obesity is associated with increased risks of atrial fibrillation (AF) and venous thromboembolism (VTE) for which anticoagulation is commonly used. However, data on the efficacy and safety of oral anticoagulants in patients with morbid obesity are limited. METHODS: We conducted a systematic review and meta-analysis to evaluate the efficacy and safety of direct oral anticoagulants (DOACs) or vitamin K antagonists (VKAs) for AF or VTE in patients with morbid obesity. RESULTS: = 77,687). The primary efficacy outcome was stroke/systemic embolism or recurrent VTE, and the primary safety outcome was major bleeding. DOACs were associated with a pooled incidence rate of stroke/systemic embolism of 1.16 per 100 person-years, compared to 1.18 with VKAs. The incidence of recurrent VTE on DOACs was 3.83 per 100 person-years, compared to 6.81 on VKAs. In both VTE and AF populations, DOACs were associated with lower risks of major bleeding compared to VKAs. However, all observational studies had moderate to serious risks of bias. CONCLUSION: Patients with morbid obesity on DOACs had similar risks of stroke/systemic embolism, lower rates of recurrent VTE, and major bleeding events compared to those on VKAs. However, the certainty of evidence was low given that studies were mostly observational with high risk of confounding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".