P283The influence of progression of atrial fibrillation on quality of life: a report from the euro heart survey
Bibliographic record
Abstract
Introduction: The process of progression of atrial fibrillation (AF) from paroxysmal to more persistent forms is an active field of research. However, the influence of AF progression on patient reported quality of life is currently unknown. Purpose: To assess the influence of AF progression on the quality of life (QoL), and whether this relationship is mediated through symptoms and/or concomitant vascular disease and adverse events during follow-up. Methods: In the Euro Heart Survey, 5,333 consecutive patients with AF on an ECG or Holter recording in the previous 12 months were included at several cardiology departments in 182 hospitals in 35 countries. Data were used from a total of 967 patients suffering from either paroxysmal or first detected AF at baseline, who filled out EuroQoL-5D questionnaires at baseline and 1 year follow-up. Results: Progression to persistent or permanent AF occurred in 132 patients (13.6%). These patients experienced more problems on all domains of EuroQoL 5D at baseline, and furthermore developed more problems during 1-year follow-up than patients that did not progress (percentage of patients experiencing more problems at follow-up than at baseline on each of the EuroQoL-5D domains: Mobility 25.7% vs 12.9%; Self-care 14.8% vs 6.6%; Usual activities 30.7% vs 16.1%; Pain / discomfort 25.7% vs 15.7%; and Anxiety / depression 29.4% vs 18.4%; all p < 0.05). This led to a decrease in utility in progressors (baseline 0.744±0.26, follow-up 0.674±0.36; difference 0.07 (95% CI -0.013 - -0.126), p=0.02), whereas utility increased slightly in non-progressors (baseline 0.796±0.23, 1 year 0.814±0.23, p=0.04; difference +0.018 (95% CI 0.008-0.033). Multivariate analysis showed that the effect of progression on utility is mediated by a large effect of adverse events (stroke (B -0.27 (95% CI -0.43 - -0.11); p=0.001), heart failure (B -0.12 (95% CI -0.20 - -0.05); p=0.001), malignancy (B -0.31 (95% CI -0.56 - -0.05); p=0.017) or implantation of an implantable cardiac defibrillator (B -0.12 (95% CI -0.23 - -0.02); p=0.03)), as well as by AF being symptomatic (B -0.04 (95% CI -0.08 – -0.01; p=0.008). Conclusion: AF progression is associated with a decrease in QoL. Although AF related symptoms occur frequently, they only lead to a small decrease in utility, while adverse events – that occur less frequently – lead to a large decrease. Strategies focusing on inhibition of AF progression will thus have the largest effect on QoL if events are prevented. Abstract P283 Figure.
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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 | 0.026 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".