Development and Validation of the Atrial Fibrillation Effect on QualiTy-of-Life (AFEQT) Questionnaire in Patients With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) has a deleterious impact on health-related quality-of-life (HRQoL), but measuring this outcome is difficult. A comprehensive, validated, disease-specific questionnaire to measure the spectrum of QoL domains affected by AF and its treatment is not available. We developed and validated a 20-item questionnaire, Atrial Fibrillation Effect on QualiTy-of-life (AFEQT), in a 6-center, prospective, observational study. METHODS AND RESULTS: Factor analyses established 4 conceptual domains (Symptoms, Daily Activities, Treatment Concern, and Treatment Satisfaction) from which individual domain and global scores were calculated. Participants from 6 centers completed the AFEQT at baseline, at month 1, and at month 3. Psychometric analyses included internal consistency and known-group validity. Test-retest reliability was assessed by comparing 1-month changes in scores among those with no change in therapy. Effect size was used to assess responsiveness after intervention. Among 219 patients age 62±11.9 years, 94% completed the AFEQT at baseline and 3 months; 66% had paroxysmal, 24% persistent, 5% longstanding persistent, and 5% permanent AF. Internal consistency was >0.88 for all scales. Lower AFEQT scores were observed with increased AF severity, categorized as asymptomatic, mild, moderate and severe, respectively: 71.2±20.6, 71.3±19.2, 57.9±19.0, and 42.0±21.2. Intraclass correlations for Overall, Symptoms, Daily Activities, Treatment Concern, and Satisfaction scores were 0.8, 0.5, 0.8, 0.7, and 0.7, respectively. Changes in 3-month scores were larger after ablation than with pharmacological adjustments, and both were greater than those observed in stable patients. CONCLUSIONS: This initial validation of AFEQT supports its use as an outcome in studies and a means to clinically follow patients with AF.
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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 | 0.007 | 0.011 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".