New-onset atrial fibrillation in chronic coronary syndrome outpatients. Insights from the international CLARIFY registry
Bibliographic record
Abstract
Abstract Background and Aims Data on new-onset atrial fibrillation (NOAF) in patients with chronic coronary syndromes (CCS) are scarce. This study aims to describe the incidence, predictors and impact on cardiovascular outcomes of NOAF in CCS patients. Methods Data from the international (45 countries) CLARIFY registry (prospeCtive observational LongitudinAl RegIstry oF patients with stable coronary arterY disease) were used. Among 29,001 CCS outpatients without previously reported AF at baseline, patients with at least one episode of AF/flutter diagnosed during 5-year follow-up were compared with patients in sinus rhythm throughout the study. Results The incidence rate of NOAF was 1.12 [95% confidence interval (CI) 1.06-1.18] per 100 patients-year (cumulative incidence at five years: 5.0%). Independent predictors of NOAF were increasing age, increasing body mass index, treated hypertension, history of peripheral artery disease, alcohol intake and low left ventricular ejection fraction, while high triglycerides were associated with lower incidence. NOAF was associated with a substantial increase in the risk of adverse outcomes, with adjusted hazard ratios of 2.52 (95%CI 2.11-3.01) for the composite of cardiovascular death, myocardial infarction or stroke, 3.22 (95%CI 2.63-3.94) for cardiovascular death, 1.55 (95%CI 1.08-2.22) for myocardial infarction, 2.80 (95%CI 2.0-3.91) for stroke, 2.64 (95%CI 2.23-3.11) for all cause death, 9.38 (95%CI 8.02-10.97) for hospitalization for heart failure and 4.33 (95%CI 2.94-6.39) for major bleeding. Conclusion Among CCS patients, NOAF is common and is strongly associated with worse outcomes. Whether more intensive preventive measures and more systematic screening for AF would improve prognosis in this population deserves further investigation.FlowchartGraphical Abstract
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".