The Burden of Non-Cardiac Comorbidities and Association with Clinical Outcomes in an Acute Heart Failure Trial – Insights from ASCEND-HF
Bibliographic record
Abstract
AIMS: Non-cardiac comorbidities are highly prevalent in patients with heart failure (HF). Our objective was to define the association between non-cardiac comorbidity burden and clinical outcomes, costs of care, and length of stay within a large randomized trial of acute HF patients. METHODS AND RESULTS: Patients with complete medical history for the following comorbidities were included: diabetes mellitus, chronic obstructive pulmonary disease, chronic liver disease, history of cancer within the last 5 years, chronic renal disease (baseline serum creatinine >3.0 mg/mL), current smoking, alcohol abuse, depression, anaemia, peripheral arterial disease, and cerebrovascular disease. Patients were classified by overall burden of non-cardiac comorbidities (0, 1, 2, 3, and 4+). Hierarchical generalized linear models were used to assess associations between comorbidity burden and 30-day all-cause death or HF hospitalization and 180-day all-cause death in addition to costs of care and length of stay. A total of 6945 patients were included in the final analysis. Mean comorbidity number was 2.2 (± 1.34). Patients with 4+ comorbidities had higher rates of 30-day all-cause death/HF hospitalization as compared with patients with no comorbidities [odds ratio (OR) 3.32, 95% confidence interval (CI) 1.61-6.84; P < 0.01]. Similar results were seen with respect to 180-day death (OR 2.13, 95% CI 1.33-3.43; P < 0.01). Higher comorbidity burden was associated with higher 180-day costs of care and length of stay. CONCLUSIONS: Higher comorbidity burden is associated with poor clinical outcomes, higher costs of care, and extended length of stay. Further studies are needed to define the impact of comorbidity management programmes on outcomes for HF patients.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".