Abstract 179: Ischemic Stroke in Atrial Fibrillation: 30-day Outcomes and Factors Associated with Severity
Bibliographic record
Abstract
Background and Hypothesis: Ischemic stroke (IS) in atrial fibrillation (AF) is associated with high mortality. Inflammation, endothelial dysfunction, and hypercoagulability, in addition to blood stasis in the left atrium, play a critical role in thrombogenesis in AF. Hyperglycemia and chronic kidney disease (CKD) are potent triggers for inflammation, oxidative stress, and thrombogenesis. Statins have been shown to possess anti-inflammatory, anti-oxidant, and anti-thrombotic properties. Accordingly, we assessed the hypothesis that statin use may modulate stroke severity in AF. Methods: Consecutive IS admissions were identified from 2006-2010. All events were subject to CT or MRI and assessed for functional independence at discharge using modified Rankin scale (mRS). AF was confirmed by ECG at presentation or within the prior 6 months in all cases. Covariates were abstracted from the medical record. To account for confounding by treatment, we used multivariable logistic regression analysis adjusted using inverse probability weighting. Results: We identified 1,030 AF-related IS; mean age was 77, 56% were female, mean CHA 2 DS 2 VASC score was 4.8 designating high baseline stroke risk. IS resulted in severe neurological deficit or death (mRS ≥ 4) for 69%; 21% died within 30-days. Severe stroke was associated with older age, diabetes, dementia, prior ischemic stroke, prior venous thromboembolism, and CKD (Table). Baseline statin use was associated with a 33% reduced risk of sustaining a severe stroke. Conclusion: Strokes in AF are associated with high morbidity and mortality. Clinical markers of thrombophilia, including prior IS, DVT, and PE, were significantly associated with severe strokes. Diabetes and CKD independently increased this risk. Statin use resulted in less severe outcomes. Advancing our basic understanding of these interrelated thrombogenic pathways will inform clinical interventions to reduce these devastating outcomes.
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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 | 0.002 |
| 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) | 0.004 | 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".