Prognostic value of quality of life and functional status in patients with heart failure: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Functional health status is increasingly being recognized as a viable endpoint in heart failure (HF) trials. We sought to assess its prognostic impact and relationship with traditional clinical outcomes in patients with HF. METHODS: MEDLINE and Cochrane central were searched up to January 2021 for post hoc analyses of trials or observational studies that assessed independent association between baseline health/functional status, and mortality and hospitalization in patients with HF across the range of left ventricular ejection fractions to evaluate the prognostic ability of NYHA class [II, III, IV], KCCQ, MLHFQ, and 6MWD. Hazard ratios (HR) with 95% confidence intervals were pooled. RESULTS: Twenty-two studies were included. Relative to NYHA I, NYHA class II (HR 1.54 [1.16-2.04]; p < 0.01), NYHA class III (HR 2.08 [1.57-2.77]; p < 0.01), and NYHA class IV (HR 2.53 [1.25-5.12]; p = 0.01) were independently associated with increased risk of mortality. 6MWD (per 10 m) was associated with decreased mortality (HR 0.98 [0.98-0.99]; p < 0.01). A 5-point increase in KCCQ-OSS (HR 0.94 [0.91-0.96]; p < 0.01) was associated with decreased mortality. A high MLHFQ score (> 45) was significantly associated with increased mortality (HR 1.30 [1.14-1.47]; p < 0.01). NHYA class, 6MWD (per 10 m), KCCQ-OSS, and MLHFQ all significantly associated with all-cause mortality in patients with HF. CONCLUSION: Identifying such patients with poor health status using functional health assessment can offer a complementary assessment of disease burden and trajectory which carries a strong prognostic value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".