Is there a role for anticoagulation with dabigatran in S. aureus bacteremia? Protocol for the adjunctive treatment domain of the <i>Staphylococcus aureus</i> Network Adaptive Platform (SNAP) randomised controlled trial
Bibliographic record
Abstract
Introduction Many patients receive oral anticoagulation for reduced stroke risk in atrial fibrillation or as treatment or prevention of venous thromboembolism. Oral factor Xa inhibitors (oral FXaI, eg, apixaban, edoxaban or rivaroxaban) are commonly prescribed for this indication. Dabigatran, an oral direct thrombin inhibitor, is similarly approved. In vitro and animal model evidence suggests that dabigatran also has direct effects on Staphylococcus aureus virulence and infection. Observational data have shown that dabigatran users are less likely to develop S. aureus bacteremia (SAB), and a small randomised controlled trial showed that dabigatran has anti- S. aureus effects when compared with low molecular weight heparins during bloodstream infection. We seek to answer whether dabigatran is superior to the oral FXaIs in achieving better SAB outcomes among patients who independently require oral anticoagulation. We report the intervention-specific protocol, embedded in an adaptive platform trial. Methods and analysis The S. aureus Network Adaptive Platform (SNAP) trial [ NCT05137119 ] is a pragmatic, randomised, multicentre adaptive platform trial that compares different SAB therapies for 90-day mortality rates. For this intervention (‘Dabi-SNAP’), patients receiving therapy with an oral FXaI will be randomised to continue as usual or to change to dabigatran as of the next scheduled dose. All subjects will receive standard of care antibiotics and/or antibiotics allocated through other active domains in the platform. As the choice of anticoagulant may not demonstrate large differences in mortality, a ranked composite of death and adverse outcomes (Desirability of Outcome Ranking, or DOOR) was chosen as the primary outcome. Ethics and dissemination The study is conditionally approved by the research ethics board of the McGill University Health Centre: identifier 2025-10900. Trial results will be published open access in a peer-reviewed journal and presented at a global infectious disease conference. The trial is registered at clinicaltrials.gov with the identifier NCT06650501 . Trial registration number NCT0665050 .
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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 | 0.036 | 0.050 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.011 |
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".