Prior stroke and transient ischemic attack as risk factors for subsequent stroke in atrial fibrillation patients: A report from the GARFIELD-AF registry
Bibliographic record
Abstract
Background It is not always possible to verify whether a patient complaining of symptoms consistent with transient ischemic attack has had an actual cerebrovascular event. Research question To characterize the risk of cardiovascular events associated with a history of stroke/transient ischemic attack in patients with atrial fibrillation. Study design and methods This study investigated the clinical characteristics and outcomes of patients with a history of stroke/transient ischemic attack among 52,014 patients enrolled prospectively in GARFIELD-AF registry. The diagnosis of stroke or transient ischemic attack was not protocol defined but based on physicians’ assessment. Patients’ one-year risk of death, stroke/systemic embolism, and major bleeding was assessed by multivariable Cox regression. Results At enrollment, 5617 (10.9%) patients were reported to have a history of stroke or transient ischemic attack. Patients with stroke or transient ischemic attack were older and had a greater burden of diabetes, moderate-to-severe kidney disease, and atherothrombosis and higher median CHA 2 DS 2 -VASc and HAS-BLED scores than those without history of stroke or transient ischemic attack. After adjustment, prior stroke/transient ischemic attack was associated with significantly higher risk for all-cause mortality (hazard ratio (HR), 1.26; 95% confidence interval (CI), 1.12–1.42), cardiovascular death (HR, 1.22; 95% CI, 1.01–1.48), non-cardiovascular death (HR, 1.39; 95% CI, 1.15–1.68), and stroke/systemic embolism (HR, 2.17; 95% CI, 1.80–2.63) than patients without history of stroke/transient ischemic attack. In patients with a prior stroke alone higher risk was observed for all-cause mortality (HR, 1.29; 95% CI, 1.11–1.50), non-cardiovascular death (HR, 1.39; 95% CI, 1.10–1.77), and stroke/systemic embolism (HR, 2.29; 95% CI, 1.83–2.86). No significantly elevated risk of adverse events was seen for patients with history of transient ischemic attack alone. Interpretation A history of prior stroke or transient ischemic attack is a strong independent risk factor for mortality and stroke/systemic embolism. This excess risk is mainly attributed to a history of stroke (with or without transient ischemic attack), whereas history of transient ischemic attack is a weaker predictor. Clinical trial registration: NCT01090362.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".