The Impact of Duration of Atrial Fibrillation Recurrences on Measures of Health‐Related Quality of Life and Symptoms
Bibliographic record
Abstract
BACKGROUND: Little is known about the relationship between daily atrial fibrillation (AF) burden and quality of life (QOL). We sought to determine the influence of atrial tachycardia (AT) or AF burden on measures of QOL and symptoms. METHODS AND RESULTS: We retrospectively analyzed patients with dual-chamber pacemakers from the Atrial Septal Pacing Efficacy Clinical Trial (ASPECT), Atrial Therapy Efficacy and Safety Trial (ATTEST), and aTRial arrhythmias dEtected by implaNted Device diagnostics Study (TRENDS) trials. All patients underwent at least one QOL evaluation. We predefined four AF burden groups: no AT/AF (group 1), ≤30 minutes (group 2), 30 minutes-2 hours (group 3), and >2 hours (group 4) per day. We compared QOL measures using the 12-item Short-Form Health Survey (SF-12; standard 4 week recall) and the AF Symptom Checklist (SC) severity and frequency between groups 2-4 to those in group 1. A total of 798 patients were analyzed (age 72 ± 11 years, 447 male [56%]). SC frequency and severity and SF-12 physical and mental scores worsened significantly when patients in group 4 were compared to patients with no AF. There were no statistically significant differences for any of the measures when comparing group 2 or 3 patients to group 1. By linear regression, only the 2-hour-cutoff had a significant impact on QOL as measured by SC frequency (+3.15, P < 0.001), severity (+3.23, P < 0.001), SF-12 physical score (-2.42, P = 0.013), and SF-12 mental score (-2.11, P = 0.021). CONCLUSION: A daily AT/AF burden of more than 2 hours had significant impact on QOL. This might influence the choice of appropriate cut-off points to determine the success of an AF treatment.
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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 | 0.003 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".