Rhythm-Monitoring Strategy and Arrhythmia Recurrence in Atrial Fibrillation Ablation Trials: A Systematic Review
Bibliographic record
Abstract
Background : The rhythm-monitoring strategy after catheter ablation (CA) for atrial fibrillation (AF) impacts the detection of atrial arrhythmia recurrence and is not well characterized. We performed a systematic review and meta-regression analysis to determine whether the duration and mode of rhythm monitoring after CA affects detection of atrial arrhythmia recurrence. Methods Databases were systematically searched for randomized controlled trials of adult patients undergoing first CA for AF from 2007 to 2021. Duration and strategy of rhythm monitoring were extracted. Meta-regression was used to identify any association between duration of monitoring and detection of atrial arrhythmia recurrence. The primary measure of outcome was single-procedure recurrence of atrial arrhythmia. Results The search strategy yielded 57 trial arms from 56 randomized controlled trials comprising 5322 patients: 36 arms of patients with paroxysmal AF (PAF), and 21 arms of patients with persistent AF (PeAF) or both PAF/PeAF. Intermittent monitoring was associated with detection of significantly less atrial arrhythmia recurrence than continuous monitoring in PAF arms (31.2% vs 46.9%, P = 0.001), but not in PeAF/PAF-PeAF combined arms (43.3% vs 63.6%, P = 0.12). No significant relationship was seen between the duration of intermittent rhythm monitoring and atrial arrhythmia recurrence detection in either the PAF ( P = 0.93) or PeAF/PAF-PeAF combined arms ( P = 0.20). Conclusions Continuous rhythm monitoring detected higher atrial arrhythmia recurrence rates, compared to intermittent rhythm monitoring, in patients with PAF. The duration of intermittent monitoring did not show a statistically significant relationship to the yield of arrhythmia detection, in near identical cohorts of trial subjects undergoing similar interventions, with clinical and research implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".