Surveillance for Outcomes Selected as Atrial Fibrillation Quality Indicators in Canada: 10-Year Trends in Stroke, Major Bleeding, and Heart Failure
Bibliographic record
Abstract
Background Whether advances in identification and management of atrial fibrillation and atrial flutter (collectively, AF) have led to improved outcomes is unclear. We sought to study trends in clinical outcomes selected as quality indicators for nonvalvular AF in Canada. Methods We identified hospitalized patients with a first diagnosis of nonvalvular AF between April 2006 and March 2015, in all of Canada except Quebec. We assessed trends in 1-year incidence of stroke/systemic embolism (SSE), major bleeding, and initial heart failure (HF) hospitalization. Results The cohort included 466,476 patients. The median age was 77 years (interquartile range, 68-84 years), 46% were female, and 68% had a C ongestive Heart Failure, H ypertension, A ge (≥75 years), D iabetes, S troke/Transient Ischemic Attack, V ascular Disease, A ge (65-74 years), S ex (Female) (CHA 2 DS 2 -VASc) score > 3. Within 1 year of discharge, 3.5% were hospitalized for stroke or SSE, 1.6% for major bleeding, and 8.6% for new HF. Over the study period, the crude rate of SSE declined from 3.6% to 3.3% ( P = 0.002), whereas the rates of hospitalization for new HF and for major bleeding did not significantly change. After adjustment for CHA 2 DS 2 -VASc score, the yearly rates of incident SSE (risk ratio, 0.99; 95% confidence interval [CI], 0.98-0.99; P = 0.002) and HF (risk ratio, 0.99; 95% CI, 0.99-1.00; P = 0.001) declined ≤ 1% absolute, whereas major bleeding remained unchanged (risk ratio, 1.00; 95% CI, 0.99-1.00; P = 0.28). Conclusions Among hospitalized patients with nonvalvular AF in Canada, the rate of SSE and new HF decreased modestly over a 10-year period, with no significant change in major bleeding. Efforts to study process-based quality indicators, with increased focus on HF prevention, are needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".