Thromboembolic events around the time of cardioversion for atrial fibrillation in patients receiving antiplatelet treatment in the ACTIVE trials
Bibliographic record
Abstract
AIMS: It is unknown whether cardioversion of atrial fibrillation causes thromboembolic events or is a risk marker. To assess causality, we examined the temporal pattern of thromboembolism in patients having cardioversion. METHODS AND RESULTS: We studied patients randomized to aspirin or aspirin plus clopidogrel in the ACTIVE trials, comparing the thromboembolic rate in the peri-cardioversion period (30 days before until 30 days after) to the rate during follow-up, remote from cardioversion. Among 962 patients, the 30-day thromboembolic rate remote from cardioversion was 0.16%; while it was 0.73% in the peri-cardioversion period [hazard ratio (HR) 4.1, 95% confidence interval (CI) 2.1-7.9]. The 30-day thromboembolic rates in the periods immediately before and after cardioversion were 0.47% and 0.96%, respectively (HR 2.2, 95% CI 0.7-7.1). Heart failure (HF) hospitalization increased in the peri-cardioversion period (HR 11.5, 95% CI 6.8-19.4). Compared to baseline, the thromboembolic rate in the 30 days following cardioversion was increased both in patients who received oral anticoagulation or a transoesophageal echocardiogram prior to cardioversion (HR 7.9, 95% CI 2.8-22.4) and in those who did not (HR 4.8, 95% CI 1.6-14.9) (interaction P = 0.2); the risk was also increased with successful (HR 4.5; 95% CI 2.0-10.5) and unsuccessful (HR 10.2; 95% CI 2.3-44.9) cardioversion. CONCLUSIONS: Thromboembolic risk increased in the 30 days before cardioversion and persisted until 30 days post-cardioversion, in a pattern similar to HF hospitalization. These data suggest that the increased thromboembolic risk around the time of cardioversion may not be entirely causal, but confounded by the overall clinical deterioration of patients requiring cardioversion.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".