Abstract 15518: What Monitoring Duration is Needed to Rule Out High Atrial Fibrillation Burden?
Bibliographic record
Abstract
Introduction: Atrial fibrillation (AF) burden contributes to stroke risk. AF detection rates increase with monitoring duration, but the relationship between time to the first AF episode and AF burden has not been described. We aimed to identify AF free monitoring durations after which a given AF burden can be ruled out. Methods: We included all subjects without permanent AF (n=13,106, median age 65 years (IQR 51-74 years), 40.1% male) monitored for ≥14 days in 2021 in the USA with PocketECG (MEDICALgorithmics), a full disclosure mobile cardiac telemetry device. We graphed 14-day AF burden (% time in AF/total time) by time in days before detection of prespecified AF durations (<30s (micro-AF), ≥30s-6 minutes, ≥6 minutes-1 hour and ≥1 hour in mutually exclusive strata) and calculated the 95th percentile of 14-day AF burden by time to first AF episode of different durations. Confidence intervals were estimated using bootstrapping with 1000 replications. Results: There were 5,605 patients with micro-AF episodes, 1,788 patients with 30s-6min AF episodes, 748 patients with 6 min-1 hour AF episodes and 227 patients with ≥1 hour AF episodes (Fig 1 a-d). The overall AF burden was higher in subjects with longer episodes, but decreased by time to first episode for all episode durations, Figure 1e-h. After 3 days of monitoring the 95th percentile for overall AF burden was 0.03% (95%CI 0.02-0.03%) in patients without micro-AF, 0.1% (95%CI 0.08-0.11% in patients without ≥30s AF, 0.4% (95%CI 0.3-0.4%) without ≥6-minute AF episodes, and 1.5% (95%CI 1.2-1.8%) without ≥1 hour episodes of AF. Conclusions: The probability of a high AF burden decreases as AF-free monitoring time increases, and AF free monitoring time can be used to inform the need for longer monitoring. When no AF episodes lasting ≥1 hour have been detected after 3 days of monitoring the probability of an AF burden >2% can be effectively ruled out.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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; 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".