Switching From Aspirin Monotherapy After Noncardioembolic Stroke: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Patients who experience an ischemic stroke while on aspirin therapy present a clinical dilemma about optimal long-term secondary prevention. While switching to an alternative antithrombotic agent is often considered, the effectiveness of switching remains uncertain. METHODS: We conducted a systematic review and network meta-analysis of randomized controlled trials reporting outcomes among patients with ischemic stroke while on aspirin who were either continued on aspirin or switched to an alternative antithrombotic therapy. Alternative antithrombotics included 2 trials of vitamin K antagonists (n=478), 3 trials of dual antiplatelet therapy (n=2229), 3 trials of direct oral anticoagulant (n=2660) monotherapy, and 1 trial of low-dose direct oral anticoagulant added onto aspirin (n=92). We excluded trials of patients with only short-term outcomes of 90 days or fewer, or those with cardioembolic sources of stroke requiring anticoagulation. Our primary outcome was recurrent ischemic stroke; the secondary outcome was a composite of ischemic stroke, myocardial infarction, and vascular death (or all-cause mortality). Outcomes reflect recurrent events measured over a median of ≈19 months (range 11-42 months). In the network portion of this meta-analysis, surface under the cumulative ranking curve rankings and pairwise meta-analyses were used to evaluate and compare the relative efficacy of alternative antithrombotic medications. RESULTS: ²=0). For the composite secondary outcome, 6 studies contributed data, yielding a pooled relative risk of 0.89 (95% CI, 0.72-1.10). In the network meta-analysis, dabigatran, apixaban, and aspirin+low-dose rivaroxaban ranked the highest among antithrombotic alternatives to aspirin, though none were significantly better than continuing aspirin. Rankings were similar when based on posterior estimates from the clinical trials and when using predictive distributions that incorporate between-study variance (ie, expected performance in future settings). CONCLUSIONS: Among patients experiencing ischemic stroke while taking aspirin, switching to an alternative antithrombotic therapy was not conclusively associated with a reduction in recurrent stroke and composite cardiovascular events. Trials are needed to determine whether specific antithrombotic strategies meaningfully improve outcomes in this high-risk population.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".