Factors associated with health‐related quality of life in heart failure in 23 000 patients from 40 countries: results of the <scp>G‐CHF</scp> research programme
Bibliographic record
Abstract
Abstract Aims To examine clinical and social correlates of health‐related quality of life (HRQL) in patients with heart failure (HF) from high‐ (HIC), upper middle‐ (UMIC), lower middle‐ (LMIC) and low‐income (LIC) countries. Methods and results Between 2017 and 2020, 23 292 patients with HF (32% inpatients, 61% men) from 40 countries were enrolled in the Global Congestive Heart Failure study. HRQL was recorded at baseline using the Kansas City Cardiomyopathy Questionnaire (KCCQ)‐12. In a cross‐sectional analysis, we compared age‐ and sex‐adjusted mean KCCQ‐12 summary scores (SS: 0–100, higher = better) between patients from different country income levels. We used multivariable linear regression examining correlations (estimated coefficients) of KCCQ‐12‐SS with sociodemographic, comorbidity, treatment and symptom covariates. The adjusted model (37 covariates) was informed by univariable findings, clinical importance and backward selection. Mean age was 63 years and 40% of patients were in New York Heart Association (NYHA) class III–IV. Average HRQL was 55 SD 27. It was 62.5 (95% confidence interval [CI] 62.0–63.1) in HIC, 56.8 (56.1–57.4) in UMIC, 48.6 (48.0–49.3) in LMIC, and 38.5 (37.3–39.7) in LICs ( p < 0.0001). Strong correlates (estimated coefficient [95% CI]) of KCCQ‐12‐SS were NYHA class III versus class I/II (−12.1 [−12.8 to −11.4] and class IV versus class I/II (−16.5 [−17.7 to −15.3]), effort dyspnoea (−9.5 [−10.2 to −8.8]) and living in LIC versus HIC (−5.8 [−7.1 to −4.4]). Symptoms explained most of the KCCQ‐12‐SS variability (partial R 2 = 0.32 of total adjusted R 2 = 0.51), followed by sociodemographic factors (R 2 = 0.12). Results were consistent in populations across income levels. Conclusion The most important correlates of HRQL in HF patients relate to HF symptom severity, irrespective of country income level.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".