Outcomes of paroxysmal atrial fibrillation ablation studies are affected more by study design and patient mix than ablation technique
Bibliographic record
Abstract
OBJECTIVE: We tested whether ablation methodology and study design can explain the varying outcomes in terms of atrial fibrillation (AF)-free survival at 1 year. BACKGROUND: There have been numerous paroxysmal AF ablation trials, which are heterogeneous in their use of different ablation techniques and study design. A useful approach to understanding how these factors influence outcome is to dismantle the trials into individual arms and reconstitute them as a large meta-regression. METHODS: Data were collected from 66 studies (6941 patients). With freedom from AF as the dependent variable, we performed meta-regression using the individual study arm as the unit. RESULTS: Success rates did not change regardless of the technique used to produce pulmonary vein isolation (PVI). Neither was adjunctive lesion sets associated with any improvement in outcome. Studies that included more males and fewer hypertensive patients were found more likely to report better outcomes. The electrocardiography method selected to assess outcome also plays an important role. Outcomes were worse in studies that used regular telemonitoring (by 23%; P < 0.001) or in patients who had implantable loop recorders (by 21%; P = 0.006), rather than those with the less thorough periodic Holter monitoring. CONCLUSIONS: Outcomes of AF ablation studies involving PVI are not affected by the technologies used to produce PVI. Neither do adjunctive lesion sets change the outcome. Achieving high success rates in these studies appears to be dependent more on patient mix and on the thoroughness of AF detection protocols. These should be carefully considered when quoting the success rates of AF ablation procedures that are derived from such studies.
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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 | 0.241 | 0.329 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.022 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".