Factors Associated With Large Improvements in Health-Related Quality of Life in Patients With Atrial Fibrillation
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) adversely impacts health-related quality of life (hrQoL). While some patients demonstrate improvements in hrQoL, the factors associated with large improvements in hrQoL are not well described. Methods: We assessed factors associated with a 1-year increase in the Atrial Fibrillation Effect on Quality-of-Life score of 1 SD (≥18 points; 3× clinically important difference), among outpatients in the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation I registry. Results: Overall, 28% (181/636) of patients had such a hrQoL improvement. Compared with patients not showing large hrQoL improvement, they were of similar age (median 73 versus 74, P =0.3), equally likely to be female (44% versus 48%, P =0.3), but more likely to have newly diagnosed AF at baseline (18% versus 8%; P =0.0004), prior antiarrhythmic drug use (52% versus 40%, P =0.005), baseline antiarrhythmic drug use (34.8% versus 26.8%, P =0.045), and more likely to undergo AF-related procedures during follow-up (AF ablation: 6.6% versus 2.0%, P =0.003; cardioversion: 12.2% versus 5.9%, P =0.008). In multivariable analysis, a history of alcohol abuse (adjusted OR, 2.41; P =0.01) and increased baseline diastolic blood pressure (adjusted OR, 1.23 per 10-point increase and >65 mm Hg; P =0.04) were associated with large improvements in hrQoL at 1 year, whereas patients with prior stroke/transient ischemic attack, chronic obstructive pulmonary disease, and peripheral arterial disease were less likely to improve ( P <0.05 for each). Conclusions: In this national registry of patients with AF, potentially treatable AF risk factors are associated with large hrQoL improvement, whereas less reversible conditions appeared negatively associated with hrQoL improvement. Understanding which patients are most likely to have large hrQoL improvement may facilitate targeting interventions for high-value care that optimizes patient-reported outcomes in AF. Registration: URL: http://www.clinicaltrials.gov . Unique identifier: NCT01165710.
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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 | 0.000 | 0.000 |
| 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".