Impact of Comorbidities on Health Status Measured Using the Kansas City Cardiomyopathy Questionnaire in Patients with Heart Failure with Reduced and Preserved Ejection Fraction
Bibliographic record
Abstract
AIM: Patients with heart failure (HF) often suffer from a range of comorbidities, which may affect their health status. The aim of this study was to assess the impact of different comorbidities on health status in patients with HF and reduced (HFrEF) and preserved ejection fraction (HFpEF). METHODS AND RESULTS: Using individual patient data from HFrEF (ATMOSPHERE, PARADIGM-HF, DAPA-HF) and HFpEF (TOPCAT, PARAGON-HF) trials, we examined the Kansas City Cardiomyopathy Questionnaire (KCCQ) domain scores and overall summary score (KCCQ-OSS) across a range of cardiorespiratory (angina, atrial fibrillation [AF], stroke, chronic obstructive pulmonary disease [COPD]) and other comorbidities (obesity, diabetes, chronic kidney disease [CKD], anaemia). Of patients with HFrEF (n = 20 159), 36.2% had AF, 33.9% CKD, 33.9% diabetes, 31.4% obesity, 25.5% angina, 12.2% COPD, 8.4% stroke, and 4.4% anaemia; the corresponding proportions in HFpEF (n = 6563) were: 54.0% AF, 48.7% CKD, 43.4% diabetes, 53.3% obesity, 28.6% angina, 14.7% COPD, 10.2% stroke, and 6.5% anaemia. HFpEF patients had lower KCCQ domain scores and KCCQ-OSS (67.8 vs. 71.3) than HFrEF patients. Physical limitations, social limitations and quality of life domains were reduced more than symptom frequency and symptom burden domains. In both HFrEF and HFpEF, COPD, angina, anaemia, and obesity were associated with the lowest scores. An increasing number of comorbidities was associated with decreasing scores (e.g. KCCQ-OSS 0 vs. ≥4 comorbidities: HFrEF 76.8 vs. 66.4; HFpEF 73.7 vs. 65.2). CONCLUSIONS: Cardiac and non-cardiac comorbidities are common in both HFrEF and HFpEF patients and most are associated with reductions in health status although the impact varied among comorbidities, by the number of comorbidities, and by HF phenotype. Treating/correcting comorbidity is a therapeutic approach that may improve the health status of patients with HF.
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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 | 0.003 |
| 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 | 0.000 | 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".