Relationship between quality of life and burden of recurrent atrial fibrillation following ablation: CAPCOST multicentre cohort study
Bibliographic record
Abstract
AIMS: Atrial fibrillation (AF) significantly impairs patients' quality of life (QOL). We performed this study to investigate the effect of AF-ablation success and atrial fibrillation burden (AFB) on QOL measures. METHODS AND RESULTS: Overall, 230 patients with paroxysmal AF refractory to antiarrhythmic drugs were enrolled and underwent ablation in a multicentre, prospective cohort. Electrocardiogram, 48-h Holter, Canadian Cardiovascular Society Severity of Atrial Fibrillation (CCS-SAF), short form-12 (SF-12), and Atrial Fibrillation Effect on Quality of life (AFEQT) scales were used to assess patients. Atrial fibrillation burden was defined as total duration of AF during the month prior to each visit (h/month). The change in AFB was calculated as the difference between the month prior to the 12-month post-ablation and the baseline pre-ablation. The Minimal Clinically Important Difference (MCID) was considered as a 19-point change for AFEQT and 3-5-point change for SF-12 scores. There was significant rise in the AFEQT and SF12 and decrease in CCS-SAF score post-AF ablation; however, the magnitude of these changes was greater in patients without AF recurrence (P < 0.05). The QOL score that best differentiated patients with and without recurrence was AFEQT, while, CCS-SAF was the most specific score. Patients with AFB decrease >19 h/month had significantly greater change in QOL scores. Atrial fibrillation burden < 24 h/month at 12-months post-ablation was associated with significant changes in QOL and CCS-SAF when adjusting for baseline scores and other covariates. These changes were consistent with the MCID of these measures. CONCLUSION: Patients experience significant improvements in QOL post-ablation, which correlate with a decrease in AFB despite ongoing brief recurrences of AF. CLINICAL TRIAL REGISTRATION: NCT01562912. https://www.clinicaltrials.gov/ct2/show/NCT01562912? term=capcost&rank=1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".