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.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".