Abstract 10: Pre-stroke Antithrombotic Therapy and Stroke Severity in Acute Ischemic Stroke Patients with Atrial Fibrillation
Bibliographic record
Abstract
Background: Antithrombotic therapies are known to prevent thromboembolic events and stroke for patients with atrial fibrillation (AF). Despite this, it is unclear in contemporary practice what percentage of AF patients presenting with acute ischemic stroke (AIS) receive antithrombotic therapy prior to stroke and how pre-stroke antithrombotic therapy affects stroke severity, particularly in the era of non-vitamin K antagonist oral anticoagulants (NOACs). Methods: We analyzed data from 1625 Get With The Guidelines-Stroke hospitals between October 2012 and March 2015. Multivariable logistic regression was performed to evaluate the association between antithrombotic therapy and stroke severity according to NIHSS. Results: Of 103,327AIS patients with pre-stroke AF, 28,583 (28%) were not on any home antithrombotics prior to their stroke, and another 30,149 (29%) were receiving aspirin alone; followed by warfarin alone (18,532, 18%) with only a quarter (4705/18,532) in the therapeutic range, aspirin and warfarin (7130, 7%), and NOACs alone (5803, 6%) ( Figure ). Even among patients with a CHA2DS2-VASc score≥2, only one third of them (34,145/99,807) were receiving some form of oral anticoagulant. Stroke severity was associated with home antithrombotic use ( Figure ). Compared with those receiving aspirin alone, warfarin with INR≥2 (adjusted odds ratio [aOR], 0.72, 95% CI 0.66-0.79), aspirin and warfarin (aOR 0.83, 95% CI 0.77-0.90), NOACs alone (aOR 0.83, 95% CI 0.77-0.91), NOACs with aspirin or clopidogrel (aOR 0.69, 95% 0.60-0.79), aspirin and clopidogrel (aOR 0.90, 95% CI 0.83-0.99), or triple antithrombotic therapy (aOR 0.64, 95% 0.49-0.84) were associated with a lower likelihood of severe stroke (NIHSS≥16). In contrast, patients subtherapeutic (INR<2 or INR missing) on warfarin (aOR 1.07, 95% 1.01-1.13) and those not receiving any antithrombotic treatment (aOR 1.10, 95% 1.05-1.15) were more likely to present with more severe stroke. Conclusions: A majority of AF patients presenting with AIS are not on guideline recommended anticoagulation or are not therapeutic on their anticoagulation. Even when strokes occurred on therapeutic warfarin or a NOAC, the strokes were less severe. These findings highlight the huge opportunities to further improve proper use of oral anticoagulants in eligible AF patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".