Risk of Ischemic Stroke after Intracranial Hemorrhage in Patients with Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: We aimed to estimate the risk of ischemic stroke after intracranial hemorrhage in patients with atrial fibrillation. MATERIALS AND METHODS: Using discharge data from all nonfederal acute care hospitals and emergency departments in California, Florida, and New York from 2005 to 2012, we identified patients at the time of a first-recorded encounter with a diagnosis of atrial fibrillation. Ischemic stroke and intracranial hemorrhage were identified using validated diagnosis codes. Kaplan-Meier survival statistics and Cox proportional hazard analyses were used to evaluate cumulative rates of ischemic stroke and the relationship between incident intracranial hemorrhage and subsequent stroke. RESULTS: Among 2,084,735 patients with atrial fibrillation, 50,468 (2.4%) developed intracranial hemorrhage and 89,594 (4.3%) developed ischemic stroke during a mean follow-up period of 3.2 years. The 1-year cumulative rate of stroke was 8.1% (95% CI, 7.5-8.7%) after intracerebral hemorrhage, 3.9% (95% CI, 3.5-4.3%) after subdural hemorrhage, and 2.0% (95% CI, 2.0-2.1%) in those without intracranial hemorrhage. After adjustment for the CHA2DS2-VASc score, stroke risk was elevated after both intracerebral hemorrhage (hazard ratio [HR], 2.8; 95% CI, 2.6-2.9) and subdural hemorrhage (HR, 1.6; 95% CI, 1.5-1.7). Cumulative 1-year rates of stroke ranged from 0.9% in those with subdural hemorrhage and a CHA2DS2-VASc score of 0, to 33.3% in those with intracerebral hemorrhage and a CHA2DS2-VASc score of 9. CONCLUSIONS: In a large, heterogeneous cohort, patients with atrial fibrillation faced a substantially heightened risk of ischemic stroke after intracranial hemorrhage. The risk was most marked in those with intracerebral hemorrhage and high CHA2DS2-VASc scores.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".