Abstract P252: Discharge Antithrombotic Therapy for Ischemic Stroke Patients With Prior Aspirin Failure
Bibliographic record
Abstract
Introduction: Aspirin is one of the most commonly used medications for cardiovascular disease and stroke prevention. Many older patients who present with a first or recurrent stroke are already on aspirin monotherapy, yet little evidence is available to guide antithrombotic strategies for these patients. Method: Using data from the American Heart Association Get With The Guidelines-Stroke Registry, we described discharge antithrombotic treatment pattern among Medicare beneficiaries without atrial fibrillation who were discharged alive for acute ischemic stroke from 1734 hospitals in the United States between October 2012 and December 2017. Results: Of 261,634 ischemic stroke survivors, 100,016 (38.2%) were on prior aspirin monotherapy (median age 78 years; 53% women; 79.4% initial stroke and 20.6% recurrent stroke). The most common discharge antithrombotics (Figure) were 81 mg aspirin monotherapy (20.9%), 325 mg aspirin monotherapy (18.2%), clopidogrel monotherapy (17.8%), and dual antiplatelet therapy (DAPT) of 81 mg aspirin and clopidogrel (17.1%). Combined, aspirin monotherapy, clopidogrel monotherapy, and DAPT accounted for 86.8% of discharge antithrombotics. The rest of 13.2% were discharged on either aspirin/dipyridamole, warfarin or non-vitamin K antagonist oral anticoagulants with or without antiplatelet, or no antithrombotics at all. Among patients with documented stroke etiology (TOAST criteria), 81 mg aspirin monotherapy (21.2-24.0%) was the most commonly prescribed antithrombotic for secondary stroke prevention. The only exception was those with large-artery atherosclerosis, in which, 25.3% received DAPT of 81 mg aspirin and clopidogrel at discharge. Conclusion: Substantial variations exist in discharge antithrombotic therapy for secondary stroke prevention in ischemic stroke with prior aspirin failure. Future research is needed to identify best management strategies to care for this complex but common clinical scenario.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".