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<sup>2</sup>, 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 <i>P</i> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".