The Role of Multimorbidity in Patients with Heart Failure Across the Left Ventricular Ejection Fraction Spectrum: Data from the Swedish Heart Failure Registry
Bibliographic record
Abstract
AIMS: The aim of this analysis was to provide data on the overall comorbidity burden, both cardiovascular (CV) and non-CV, in a large real-world heart failure (HF) population across the ejection fraction (EF). METHODS AND RESULTS: Patients with HF from the Swedish HF Registry between 2000 and 2021 were included. Of 91 463 patients (median age 76 years [interquartile range 67-82]), 98% had at least one among the 17 explored comorbidities (94% at least one CV and 85% at least one non-CV comorbidity). All comorbidities, except for coronary artery disease (CAD), were more frequent in HF with preserved EF (HFpEF). Patients with multiple comorbidities were older, more likely female, inpatients, with HFpEF, worse New York Heart Association class and higher N-terminal pro-B-type natriuretic peptide levels. In a multivariable Cox model, 12 comorbidities were independently associated with a higher risk of death from any cause. The highest risk was associated with dementia (hazard ratio [HR] 1.55, 95% confidence interval [CI] 1.45-1.65), chronic kidney disease (HR 1.37, 95% CI 1.34-1.41), chronic obstructive pulmonary disease (HR 1.32, 95% CI 1.28-1.35). Obesity was associated with a lower risk of all-cause death (HR 0.81, 95% CI 0.79-0.84). CAD and valvular heart disease were associated with a higher risk of all-cause and CV mortality, but not non-CV mortality, whereas cancer and musculo-skeletal disease increased the risk of non-CV mortality. A significant interaction with EF was observed for several comorbidities. Occurrence of CV and non-CV outcomes was related to the number of CV and non-CV comorbidities, respectively. CONCLUSION: The burden of both CV and non-CV comorbidities was high in HF regardless of EF, but overall higher in HFpEF. Multimorbidity was associated with a high risk of death with a different burden on CV or non-CV outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".