Atrial fibrillation burden is underestimated by non-invasive monitoring: the CIRCA-DOSE trial
Bibliographic record
Abstract
How to quantify success after atrial fibrillation (AF) ablation remains debated. The traditional time-to-event outcome may not fully reflect patient experience, prompting interest in AF burden, defined as the proportion of time in AF. Implantable cardiac monitors (ICMs) offer the most accurate estimates of AF burden, but their use is limited by cost and invasiveness. Hence, many studies report AF burden from intermittent non-invasive monitors as surrogates for ICM-derived burden. However, the validity of non-invasively derived AF burden remains uncertain. We investigated the relationship between AF burden derived from ICMs and simulated non-invasive monitoring strategies. We analysed 346 patients (mean age 59 ± 10 years; 67% men) undergoing first catheter ablation for symptomatic paroxysmal AF in the CIRCA-DOSE trial (NCT01913522).1 All patients underwent insertion of an ICM (Reveal LINQ, Medtronic, Minneapolis) before ablation. The ICM-derived AF burden was calculated as per cent time in AF from Day 90 to 365. To simulate non-invasive strategies, we analysed ICM data corresponding to periods when these monitors would have been used: three 24 h, 48 h, 7-day, or 14-day ambulatory ECGs (AECGs) at 3, 6, and 12 months, and weekly 1 min transtelephonic monitoring (TTM). Non-invasive AF burden was calculated as the per cent time in AF observed during these intervals or, for TTM, the proportion of positive tracings. Burdens were compared using a non-parametric paired Wilcoxon test; bias was defined as the mean non-invasive minus ICM-derived burden. The institutional ethics committee approved the study. The data are available upon request.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".