The effect of body mass index on clinical outcomes in patients with newly diagnosed atrial fibrillation in the GARFIELD-AF registry
Bibliographic record
Abstract
Abstract Introduction Higher body mass index (BMI) is associated with a higher risk of atrial fibrillation (AF). However, previous evidence has suggested an inverse association between BMI and risk of AF outcomes. Purpose To explore the association between BMI and outcomes in those with newly diagnosed AF in the GARFIELD-AF registry. Methods GARFIELD-AF is an international registry of consecutively recruited patients aged ≥18 years with newly diagnosed AF and ≥1 stroke risk factor. Data were collected prospectively on 52,080 patients. Participants with missing or extreme BMI values and those without two-year follow-up were excluded. Cox proportional hazard models were used to estimate the effect of BMI on the risk of outcomes. Models were adjusted for age, sex, ethnicity, smoking, alcohol, and ≥moderate chronic kidney disease. Where appropriate participants were divided into groups based on BMI. Restricted cubic splines were used to assess non-linear relationships. Results BMI and outcome data were available for 40,495 patients. Those with higher BMI were generally younger, and more likely to have pre-existing hypertension, diabetes, or vascular disease (Table). Underweight patients received anticoagulation less often than those in other groups (60.3% vs 67.9%, respectively). During follow-up, 2,801 participants (6.9%) died and 603 (1.5%) had new/worsening heart failure. Following adjustment for potential confounders, a U-shaped relationship was seen between BMI and all-cause mortality and new/worsening heart failure (Figure). For all-cause mortality, the lowest risk was at 30kg/m2. Below this level, there was an 8% higher risk of mortality (95% confidence interval (CI) 6 to 9%) per 1kg/m2 lower BMI. Above 30kg/m2, there was a 5% higher risk of mortality per 1kg/m2 higher BMI (95% CI 4 to 7%). For new/worsening heart failure, the lowest risk was at 25kg/m2. Above this level, 1kg/m2 higher BMI was associated with an 5% higher risk (95% CI 13 to 6%). Conclusions BMI was an important risk factor for both all-cause mortality and new/worsening heart failure in AF. Those at both extremes of BMI are at higher risk. BMI and selected outcomes Funding Acknowledgement Type of funding source: Private company. Main funding source(s): The GARFIELD-AF registry is funded by an unrestricted research grant from Bayer AG.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".