Factors associated with health-related quality of life in heart failure in 23,000 patients from 40 countries: results of the global congestive heart failure research program
Bibliographic record
Abstract
Abstract Background Poor health-related quality of life (HRQL) is common in heart failure (HF) and strongly predicts death and HF hospitalization in all regions of the world. Understanding facors associated with HRQL could therefore lead to improved prognosis in HF patients. Despite that the majority of HF occurs in low- and middle-income countries, there are limited data characterizing self-perceived health HRQL and its correlates in these settings. Purpose To examine clinical and social correlates of HRQL in patients with HF from high- (HIC), upper middle- (UMIC), lower middle-(LMIC) and low-income (LIC) countries. Methods Between 2017 and 2020, we enrolled 23,292 patients with HF (32% inpatients, 61% men) from 40 countries in the Global Congestive Heart Failure Study. We recorded HRQL at baseline using 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 (estimates expressed as β-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. We used partial R2-estimates to understand the contribution to the variability in KCCQ-12-SS of 4 different groups of covariates. (sociodemographic, comorbidities, treatments and signs and symptoms of congestion). Results Mean age was 63 years and 40% were in NYHA class III–IV. Average HRQL was 55± SD 0.5. It was 62.5 (95% 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 (β-coefficient [95% CI]) of KCCQ-12-SS were NYHA class III vs class I/II (−12.1 [−12.8 to −11.4] and class IV vs. class I/II (−16.5 [−17.7 to −15.3]), effort dyspnea (−9.5 [−10.2 to −8.8]) and living in LIC vs. HIC (−5.8 [−7.1 to −4.4]). Symptoms explained most of the KCCQ-12-SS variability (partial R2=0.32 of total adjusted R2=0.51), followed by sociodemographic factors (R2=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. Improved symptom control may have a big impact on HRQL, especially in LICs. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Bayer AG
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".