Association of Diabetes Duration and Glycemic Control With Stroke Rate in Patients With Atrial Fibrillation and Diabetes: A Population‐Based Cohort Study
Bibliographic record
Abstract
Background There are limited data on the association of diabetes duration and glycemic control with stroke risk in atrial fibrillation (AF). Our objective was to study the association of diabetes duration and glycated hemoglobin (HbA1c) with the rate of stroke in people with diabetes and newly diagnosed AF. Methods and Results This was a population‐based cohort study using linked administrative data sets. We studied 37 209 individuals aged ≥66 years diagnosed with AF in Ontario between April 2009 and March 2019, who had diabetes diagnosed 1 to 16 years beforehand. The primary outcome was hospitalization for stroke at 1 year. Cause‐specific hazard regression was used to model the association of diabetes duration and glycated hemoglobin (HbA1c) with the rate of stroke. Restricted cubic spline analyses showed increasing hazard ratios (HR) for stroke with longer diabetes duration that plateaued after 10 years and increasing HRs for stroke with HbA1c levels >7%. Relative to patients with <5 years diabetes duration, stroke rates were significantly higher for patients with ≥10 years duration (HR, 1.45; 95% CI, 1.16–1.82; P =0.001), while diabetes duration 5 to <10 years was not significantly different. Relative to glycated hemoglobin 6% to <7%, values ≥8% were associated with higher stroke rates (HR, 1.44; 95% CI, 1.12–1.84; P =0.004), while other HbA1c categories were not significantly different. Conclusions Longer diabetes duration and higher glycated hemoglobin were associated with significantly higher stroke rates in patients with AF and diabetes. Models for stroke risk prediction and preventive care in AF may be improved by considering patients’ diabetes characteristics.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".