Abstract 11875: Risk of Malignant Cancer Among Women With New-onset Atrial Fibrillation
Bibliographic record
Abstract
Introduction: A substantial proportion of patients with atrial fibrillation (AF) die of non-cardiovascular causes, and recent studies suggest a link between AF and cancer. However, this association has not been evaluated in long-term prospective studies. Methods: A total of 34691 women ≥45 years and free of AF, cardiovascular disease and cancer at baseline were prospectively followed for incident AF and malignant cancer within the Women’s Health Study. All incident AF and cancer events were validated by medical record review. Cox proportional-hazards models using time-updated covariates were constructed to assess the relationship of new-onset AF with incident cancer and to adjust for potential confounders. We then assessed the risk of incident AF among women with cancer using a similar modelling approach. Results: Mean age at baseline was 55±7 years. During 19.1 years of follow-up, we observed 1467 (4.2%) AF and 5130 (14.8%) cancer events. AF was a significant risk factor for incident cancer in age-adjusted (hazard ratio (HR) 1.58, 95% confidence interval (CI), 1.34, 1.87, p<0.0001) and multivariable adjusted (HR 1.49, 95% CI, 1.26, 1.77, p<0.0001) models, and was increased among women with paroxysmal (HR 1.35, 95% CI 1.09, 1.67, p=0.005) and non-paroxysmal AF (HR 1.61, 95% CI 1.23, 2.09, p=0.0004). The risk of cancer was highest in the first 3 months after new-onset AF (HR 3.53, 95% CI 2.05, 6.08, p<0.0001) but remained significant beyond 1 year (adjusted HR 1.44, 95% CI 1.19, 1.73, p=0.0001). New-onset AF was also associated with an increased risk of cancer mortality (adjusted HR 1.37, 95% CI 1.01, 1.85, p=0.04). In contrast, women with new-onset cancer had an increased risk of incident AF within 3 months (HR 4.61, 95% CI 2.81, 7.54, p<0.0001) but not beyond 1 year (HR 1.17, 95% CI 0.97, 1.41, p=0.11). Conclusions: In this large cohort of initially healthy women, new-onset AF was a significant risk factor for the short and long term diagnosis of incident cancer. In contrast, cancer was not associated with an increased AF risk over the long term. Our results may suggest that AF could be an early sign of occult cancer or an underlying systemic process conferring an increased cancer risk.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".