Global variations in the prevalence, treatment, and impact of atrial fibrillation in a multi-national cohort of 153 152 middle-aged individuals
Bibliographic record
Abstract
AIMS: To compare the prevalence of electrocardiogram (ECG)-documented atrial fibrillation (or flutter) (AF) across eight regions of the world, and to examine antithrombotic use and clinical outcomes. METHODS AND RESULTS: Baseline ECGs were collected in 153 152 middle-aged participants (ages 35-70 years) to document AF in two community-based studies, spanning 20 countries. Medication use and clinical outcome data (mean follow-up of 7.4 years) were available in one cohort. Cross-sectional analyses were performed to document the prevalence of AF and medication use, and associations between AF and clinical events were examined prospectively. Mean age of participants was 52.1 years, and 57.7% were female. Age and sex-standardized prevalence of AF varied 12-fold between regions; with the highest in North America, Europe, China, and Southeast Asia (270-360 cases per 100 000 persons); and lowest in the Middle East, Africa, and South Asia (30-60 cases per 100 000 persons) (P < 0.001). Compared with low-income countries (LICs), AF prevalence was 7-fold higher in middle-income countries (MICs) and 11-fold higher in high-income countries (HICs) (P < 0.001). Differences in AF prevalence remained significant after adjusting for traditional AF risk factors. In LICs/MICs, 24% of participants with AF and a CHADS2 score ≥1 received antithrombotic therapy, compared with 85% in HICs. AF was associated with an increased risk of stroke [hazard ratio (HR) 2.29; 95% confidence interval (CI) 1.49-3.52] and death (HR 2.97; 95% CI 2.25-3.93); with similar rates in different countries grouped by income level. CONCLUSIONS: Large variations in AF prevalence occur in different regions and countries grouped by income level, but this is only partially explained by traditional AF risk factors. Antithrombotic therapy is infrequently used in poorer countries despite the high risk of stroke associated with AF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".