Impact of body mass index on the outcome of catheter ablation of atrial fibrillation
Bibliographic record
Abstract
Objectives The association between obesity and atrial fibrillation (AF) is well-established. We aimed to evaluate the impact of index body mass index (BMI) on AF recurrence at 12 months following catheter ablation using propensity-weighted analysis. In addition, periprocedural complications and fluoroscopy details were examined to assess overall safety in relationship to increasing BMI ranges. Methods Baseline, periprocedural and follow-up data were collected on consecutive patients scheduled for AF ablation. There were no specific exclusion criteria. Patients were categorised according to baseline BMI in order to assess the outcomes for each category. Results Among 3333 patients, 728 (21.8%) were classified as normal (BMI <25.0 kg/m 2 ), 1537 (46.1%) as overweight (BMI 25.5–29.0 kg/m 2 ) and 1068 (32.0%) as obese (BMI ≥30.0 kg/m 2 ). Procedural duration and radiation dose were higher for overweight and obese patients compared with those with a normal BMI (p=0.002 and p<0.001, respectively). An index BMI ≥30 kg/m 2 led to a 1.2-fold increased likelihood of experiencing recurrent AF at 12-months follow-up as compared with overweight patients (HR 1.223; 95% CI 1.047 to 1.429; p=0.011), while no significant correlation was found between overweight and normal BMI groups (HR 0.954; 95% CI 0.798 to 1.140; p=0.605) and obese versus normal BMI (HR 1.16; 95% CI 0.965 to 1.412; p=0.112). Conclusions Patients with a baseline BMI ≥30 kg/m 2 have a higher recurrence rate of AF following catheter ablation and therefore lifestyle modification to target obesity preprocedure should be considered in these patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".