Factors influencing the quality of life in patients with heart failure and its impact on mortality: insights from HEROES registry
Bibliographic record
Abstract
Abstract Background Novel therapies for heart failure (HF) are introduced not only to prevent clinical events but also to improve patients' quality of life (QoL). HF patients often present with multiple non-cardiac comorbidities, which may impact their prognosis and patient-reported outcomes. Despite the widespread use of QoL assessments in this group — both in clinical practice and clinical trials — the exact role of patient-related factors as determinants of QoL remains not fully elucidated. Purpose This study aims to assess the impact of comorbidities and other patient-related factors on QoL in HF patients, as well as the association between QoL and mortality in this group. Methods This study is part of a prospective, observational, national registry - HEart failuRe ObsErvational Study of the Polish Cardiac Society (HEROES). Patients with HF were enrolled in 41 Polish centers and the only exclusion criterion was age < 18 years old. QoL was assessed at baseline with a KCCQ-12 overall score. Follow-up data on all-cause mortality was obtained from the Polish Ministry of Digital Affairs. Adjusted linear regression with backward elimination method was used to identify factors associated with KCCQ-12. Results The study included 1397 patients with available KCCQ-12 overall score [median = 47.92 (28.65 – 72.92)]. Patients hospitalized for HF had similar values of KCCQ-12 overall score compared to non-cardiac hospitalizations [36 (21-57) vs 39 (10-60), p=0.942] and higher KCCQ-12 scores than patients hospitalized for other cardiac results [53 (38-78), p<0.001]. Patients with higher NYHA class had consistently significantly lower values of all KCCQ-12 domains and KCCQ-12 overall score [85 (69-93) vs 65 (46-79) vs 35 (23-49) vs 21 (10-38), all p<0.001]. Adjusted linear regression revealed that visit type, hepatic dysfunction, body mass index, gender, chronic kidney disease, implantable cardioverter-defibrillator device, atrial fibrillation, stroke, asthma, anemia and age were independently associated with KCCQ-12 (Figure 1). KCCQ-12 was also significantly associated with all-cause mortality (HR per 1 point increase =0.976, 95%CI:0.970-0.981, p<0.001) (Figure 2). The AUC for KCCQ-12 overall score to predict mortality at the follow-up period was 0.680 (95%CI:0.642-0.717, p<0.001). Conclusions Both cardiac and non-cardiac comorbidities, as well as visit type, age and body mass index, are independently associated with QoL in patients with HF. Future studies incorporating QoL assessment in HF patients should be interpreted with particular consideration of these factors.Adjusted linear regression for KCCQ-12 Kaplan-Meier curves for mortality
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".