The Risk of Stroke and Stroke Type in Patients With Atrial Fibrillation and Chronic Kidney Disease
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) and chronic kidney disease (CKD) are known to increase the risk of stroke. Objectives: We set out to examine the risk of stroke by kidney function and albuminuria in patients with and without AF. Design: Retrospective cohort study. Settings: Ontario, Canada. Participants: A total of 736 666 individuals (>40 years) from 2002 to 2015. Measurements: New-onset AF, albumin-to-creatinine ratio (ACR), and an estimated glomerular filtration rate (eGFR). Methods: A total of 39 120 matched patients were examined for the risk of ischemic, hemorrhagic, or any stroke event, accounting for the competing risk of all-cause mortality. Interaction terms for combinations of ACR/eGFR and the outcome of stroke with and without AF were examined. Results: In a total of 4086 (5.2%) strokes (86% ischemic), the presence of AF was associated with a 2-fold higher risk for any stroke event and its subtypes of ischemic and hemorrhagic stroke. Across eGFR levels, the risk of stroke was 2-fold higher with the presence of AF except for low levels of eGFR (eGFR < 30 mL/min/1.73 m 2 , hazard ratio [HR]: 1.38, 95% confidence interval [CI]: 0.99-1.92). Similarly across ACR levels, the risk of stroke was 2-fold higher except for high levels of albuminuria (ACR > 30 mg/g, HR: 1.61, 95% CI: 1.31-1.99). The adjusted risk of stroke with AF differed by combinations of ACR and eGFR categories (interaction P value = .04) compared with those without AF. Both stroke types were more common in patients with AF, and ischemic stroke rates differed significantly by eGFR and ACR categories. Limitations: Medication information was not included. Conclusions: Patients with CKD and AF are at a high risk of total, ischemic, and hemorrhagic strokes; the risk is highest with lower eGFR and higher ACR and differs based on eGFR and the degree of ACR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".