Increasing incidence of non-valvular atrial fibrillation in the UK from 2001 to 2013
Bibliographic record
Abstract
OBJECTIVE: To determine whether the incidence of atrial fibrillation (AF) is static or rising in the UK. DESIGN: Among the cohort of all individuals aged ≥45 years in the UK Clinical Practice Research Datalink (CPRD) (linked to hospital discharges) we identified incident non-valvular AF cases between 2001 and 2013. Overall and annual AF incidence rates were calculated and standardised to the UK population. RESULTS: The cohort of 2.23 million individuals included 91,707 patients with incident AF. The overall standardised AF incidence rate was 6.7 (95% CI 6.7 to 6.8) per 1000 person-years, increasing exponentially with age and higher in men of all ages. There was a small increase in the standardised incidence of AF in the last decade from 5.9 (5.8 to 6.1)/1000 person-years in 2001 to 6.9 (6.8 to 7.1)/1000 person-years in 2013, mostly attributable to subjects aged >80 years with a non-primary hospital discharge diagnosis of AF. Standardised incidence rates of AF among white patients was 8.1 (8.1 to 8.2)/1000 person-years, compared with 5.4 (4.6 to 6.3) for Asians and 4.6 (4.0 to 5.3) for black patients. AF diagnosis was first made in general practice in 39% of incident AF. CONCLUSIONS: The incidence of AF in the UK has increased gradually in the last decade, with more than 200 000 first-ever non-valvular AF cases expected in 2015. This increase is only partly due to population ageing, though the principal increase has been in the elderly hospitalised for a reason other than 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".