Change of Health‐Related Quality of Life Over Time and Its Association With Patient Outcomes in Patients With Heart Failure
Bibliographic record
Abstract
Background Improving health‐related quality of life is an important goal in the management of patients with heart failure (HF). Defining health‐related quality of life changes over time in patients with HF with preserved (HFpEF) or reduced ejection fraction and showing their association with other important clinical events could support the use of health‐related quality of life as a measure of quantifying HF care. Methods and Results In the Alberta HEART (Heart Failure Aetiology and Analysis Team) cohort (n=621), patients were categorized into 4 subgroups: healthy controls (n=98), at risk (n=163), HFpEF (n=191), and HF with reduced ejection fraction (n=169). The change of the Kansas City Cardiomyopathy Questionnaire (KCCQ), EuroQOL 5 dimensions, and Functional Assessment of Cancer Therapy—Anemia over 12 months, and its association with a composite of death or rehospitalization within 3 years were assessed. At baseline, the KCCQ overall summary score was 73 (interquartile range, 53–86) in HFpEF and 78 (interquartile range, 56–90) in HF with reduced ejection fraction ( P =0.22). Overall, 30.5% of patients with HF experienced ≥5‐point improvements and 32.4% had ≥5‐point worsening in KCCQ overall summary score at 12 months, which did not differ between HFpEF and HF with reduced ejection fraction ( P =0.23). Clinical events were higher in patients with HF who had a decline in KCCQ over 12 months as compared with those with stable KCCQ scores (70.2% versus 52.0%, P =0.012). The results were similar for the Functional Assessment of Cancer Therapy—Anemia and EuroQOL 5 dimensions. Conclusions In patients with HF, the KCCQ quantified clinically meaningful changes over time, which were associated with important clinical outcomes in patients with HFpEF. Given the observed variability and prognostication in different patient trajectories, health‐related quality of life measures could be valuable for quantifying the quality of care in healthcare systems.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".