D-dimer and factor VIIa in atrial fibrillation – prognostic values for cardiovascular events and effects of anticoagulation therapy
Bibliographic record
Abstract
Summary Coagulation markers may improve monitoring the risk of stroke and bleeding in patients with atrial fibrillation (AF) during anticoagulant treatment. We examined baseline levels of D-dimer and their association with stroke, cardiovascular death and major bleeding in 6,202 AF patients randomised to dabigatran or warfarin in the RE-LY trial. The effects of treatment on serial levels of D-dimer and coagulation factor (F) VIIa in 2,567 patients were also analysed. Baseline D-dimer levels were related to the rate of stroke/systemic embolism (SEE) with 0.64 % in the lowest quartile (Q1, as reference) (D-dimer < 298 μg/l), 1.38 % Q2 (D-dimer 298–473 μg/l), 1.71 % Q3 (D-dimer 474–822 μg/l) and 2.00 % in Q4 (D-dimer > 822 μg/l) (p=0.0007). Similar associations were shown for cardiovascular death and major bleeding. Addition of baseline D-dimer to established clinical risk factors improved prediction of stroke/SEE, cardiovascular death and major bleeding (C-index increased from 0.66 to 0.68, 0.71 to 0.73 and 0.66 to 0.67, respectively). Dabigatran provided a greater reduction of D-dimer levels than warfarin regardless of baseline anticoagulant treatment. Ontreatment levels of FVIIa were markedly reduced by warfarin (median 12.1–13.8 mU/ml) but significantly higher with dabigatran (median 39.4–49.0 mU/ml) at all-time points. Dabigatran is associated with greater reduction in D-dimer without the pronounced reduction of FVIIa seen with warfarin. These different effects on the coagulation system might explain the better efficacy and less intracranial bleeding observed with dabigatran compared with warfarin. Clinical Trial Registration: NCT00262600 (www.clinicaltrials.gov). Supplementary Material to this article is available online at www.thrombosis-online.com.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".