Association of Diabetes With Atrial Fibrillation Phenotype and Cardiac and Neurological Comorbidities: Insights From the Swiss-AF Study.
Bibliographic record
Abstract
Background Diabetes is a major risk factor for atrial fibrillation (AF). However, it remains unclear whether individual AF phenotype and related comorbidities differ between patients who have AF with and without diabetes. This study investigated the association of diabetes with AF phenotype and cardiac and neurological comorbidities in patients with documented AF. Methods and Results Participants in the multicenter Swiss-AF (Swiss Atrial Fibrillation) study with data on diabetes and AF phenotype were eligible. Primary outcomes were parameters of AF phenotype, including AF type, AF symptoms, and quality of life (assessed by the European Quality of Life-5 Dimensions Questionnaire [EQ-5D]). Secondary outcomes were cardiac (ie, history of hypertension, myocardial infarction, and heart failure) and neurological (ie, history of stroke and cognitive impairment) comorbidities. The cross-sectional association of diabetes with these outcomes was assessed using logistic and linear regression, adjusted for age, sex, and cardiovascular risk factors. We included 2411 patients with AF (27.4% women; median age, 73.6 years). Diabetes was not associated with nonparoxysmal AF (odds ratio [OR], 1.01; 95% CI, 0.81-1.27). Patients with diabetes less often perceived AF symptoms (OR, 0.74; 95% CI, 0.59-0.92) but had worse quality of life (β=-4.54; 95% CI, -6.40 to -2.68) than those without diabetes. Patients with diabetes were more likely to have cardiac (hypertension [OR, 3.04; 95% CI, 2.19-4.22], myocardial infarction [OR, 1.55; 95% CI, 1.18-2.03], heart failure [OR, 1.99; 95% CI, 1.57-2.51]) and neurological (stroke [OR, 1.39, 95% CI, 1.03-1.87], cognitive impairment [OR, 1.75, 95% CI, 1.39-2.21]) comorbidities. Conclusions Patients who have AF with diabetes less often perceive AF symptoms but have worse quality of life and more cardiac and neurological comorbidities than those without diabetes. This raises the question of whether patients with diabetes should be systematically screened for silent AF. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT02105844.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".