Nationwide Study of Sex Differences in Incident Heart Failure in Newly Diagnosed Nonvalvular Atrial Fibrillation
Bibliographic record
Abstract
Background Heart failure (HF) is a leading complication of nonvalvular atrial fibrillation (NVAF), and the presence of both conditions worsens prognosis. Sex-specific associations between NVAF and outcomes focus on stroke; less is known about HF. We evaluated sex differences in incident HF in NVAF. Methods We identified adults age ≥ 65 years hospitalized for incident NVAF without prior HF from April 2010 to March 2018 in Canada. The primary outcome was incident HF hospitalization, with a secondary composite outcome of incident HF hospitalization or all-cause mortality at 1 year. Cox proportional hazard regression models were constructed for the association between sex and outcomes, adjusting for age, comorbidities, socioeconomic status, cardioversion, and medications. Results Of 68,909 NVAF patients, 53.8% were women. Women had a higher rate of the primary outcome (30.0% vs 25.6%, P < 0.001) and the composite outcome (39.5% vs 36.6%, P < 0.001) than men. In multivariable analysis without adjusting for medications, there was an 8% increase risk of HF (95% confidence interval [CI] 1.05-1.11, P < 0.001) for women, which was attenuated when accounting for medication (hazard ratio [HR] 1.01, 95% CI 0.98-1.04). After full adjustment, women age ≥ 75 years were at higher risk of the primary outcome (HR 1.10, 95% CI 1.06-1.13, P < 0.001) and the composite outcome (HR 1.04, 95% CI 1.01-1.07, P < 0.001), compared with men, whereas there was a significantly lower risk for those age 65-75 years. Conclusions In this nationwide study of incident NVAF without HF, women age ≥ 75 years were more likely to develop HF or die than men. Strategies to prevent HF in older women with NVAF 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".