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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".