Modifiable risk factors predict incident atrial fibrillation and heart failure
Bibliographic record
Abstract
OBJECTIVE: Heart failure (HF) frequently complicates atrial fibrillation (AF) and significantly increases mortality risk. Limited data exist on the modifiable risk factors associated with development of HF in AF patients. METHODS: We examined two large, prospective, population-based cohorts without prior AF or HF at baseline: Malmö Preventive Project (MPP, n=32 625) and Malmö Diet and Cancer Study (MDCS, n=27 695). Using Lunn-McNeil competing risks, multivariable Cox models were constructed to determine hazard ratios (HR) and 95% confidence intervals (CI) of risk factors for incident HF with AF, and AF alone. RESULTS: ), systolic blood pressure (HR 1.20, 95% CI 1.24 to 1.26 vs HR 1.08, 95% CI 1.06 to 1.10 per 10 mm Hg) and current cigarette smoking (HR 1.73, 95% CI 1.54 to 1.95 vs HR 1.23, 95% CI 1.15 to 1.32) had stronger associations with incident AF with HF compared with AF alone (all p for difference <0.0001). Similar results were observed in MDCS (all p for difference <0.009). These three risk factors and diabetes accounted for 51.8% and 54.1% of the population attributable risk (PAR) for AF with HF in MPP and MDCS, respectively, compared with 20.1% and 27.0% for AF alone. CONCLUSIONS: Obesity, hypertension and active smoking preferentially associated with AF with HF, compared with AF alone, and accounted for >50% of the PAR. Randomised trials are needed to assess whether risk factor modification can reduce the incidence of AF with HF and reduce mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".