Longitudinal Changes in Health‐Related Quality of Life in Patients With Atrial Fibrillation
Bibliographic record
Abstract
Background Optimizing health-related quality of life (HRQoL) is an important aim of atrial fibrillation (AF) treatment. Little is known about patients' long-term HRQoL trajectories and the impact of patient and disease characteristics. The aim of this study was to describe HRQoL trajectories in an observational AF study population and in clusters of patients with similar patient and disease characteristics. Methods and Results We used 5-year follow-up data from the Swiss-Atrial Fibrillation prospective cohort, which enrolled 2415 patients with prevalent AF from 2014 to 2017. HRQoL data, collected yearly, comprised EuroQoL-5 dimension utilities and EuroQoL visual analog scale scores. Patient clusters with similar characteristics at enrollment were identified using hierarchical clustering. HRQoL trajectories were analyzed descriptively and with inverse probability-weighted regressions. Effects of postbaseline clinical events were additionally assessed using time-shifted event variables. Among 2412 (99.9%) patients with available baseline HRQoL, 3 clusters of patients with AF were identified, which we characterized as follows: "cardiovascular dominated," "isolated symptomatic," and "severely morbid without cardiovascular disease." Utilities and EuroQoL visual analog scale scores remained stable over time for the full population and the clusters; isolated symptomatic patients showed higher levels of HRQoL. Utilities were reduced after occurrences of stroke, hospitalization for heart failure, and bleeding, by -0.12 (95% CI, -0.18 to -0.06), -0.10 (95% CI, -0.13 to -0.08), and -0.06 (95% CI, -0.08 to -0.04), respectively, on a 0 to 1 utility scale. Utility of surviving patients returned to preevent levels 4 years after heart failure hospitalization; 3 years after bleeding; and 1 year after stroke. Conclusions In patients with prevalent AF, HRQoL was stable over time, irrespective of baseline patient characteristics. Clinical events of hospitalization for heart failure, stroke, and bleeding had only a temporary effect on HRQoL.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".