Influence of Direct Oral Anticoagulant Levels and Thrombin Generation on Postoperative Bleeding [SONAR]: A Nested Case–Control Study
Bibliographic record
Abstract
Abstract A direct oral anticoagulant (DOAC) concentration threshold above which an impact on surgical hemostasis starts to occur is unknown. Thrombin generation assays (TGAs) provide a measure of the coagulation phenotype. This study aimed to determine whether preoperative TGA parameters are associated with postoperative bleeding, and whether this is partly due to residual DOAC levels. We conducted a nested case–control study using samples from apixaban/rivaroxaban-treated patients with atrial fibrillation from the PAUSE (Perioperative Anticoagulation Use for Surgery Evaluation) perioperative study. Cases were participants with postoperative major or clinically relevant nonmajor bleeding; controls were participants without bleeding. DOAC levels were measured using a chromogenic anti-Xa assay (BIOPHEN DiXaI; rivaroxaban/apixaban calibrators). TGA parameters were measured using calibrated automated thrombography. Generalized linear mixed models and causal mediation analyses were used to evaluate the relationship between DOAC levels, TGA parameters, and bleeding. Forty eight cases were matched to 474 controls. Residual DOAC levels were higher in cases than controls (p = 0.03) and each TGA parameter was correlated with residual DOAC levels (p<0.05). A longer lag time (LT; odds ratio [OR] = 1.319 per minute [95% confidence interval [CI]: 1.077–1.617]) and time-to-peak (TTP; OR = 1.154 per minute [95% CI: 1.028–1.296]) were associated with an increased odds of bleeding; higher peak (OR = 0.994 per nM [95% CI: 0.989–0.998]) and mean velocity rate index (mVRI; OR = 0.986 per nM/min [95% CI: 0.976–0.996]) were associated with a lower odds of bleeding. The effect of apixaban/rivaroxaban levels on bleeding was mediated by altered TGA parameters (LT, TTP, peak, mVRI). These findings support a measurable effect from low residual DOAC levels on thrombin generation and suggest a causal contribution of both toward bleeding.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".