Impact of Multimorbidity on Mortality in Heart Failure with Reduced Ejection Fraction: Which Comorbidities Matter Most? An Analysis of PARADIGM-HF and ATMOSPHERE
Bibliographic record
Abstract
AIMS: Multimorbidity, the coexistence of two or more chronic conditions, is synonymous with heart failure (HF). How risk related to comorbidities compares at individual and population levels is unknown. The aim of this study is to examine the risk related to comorbidities, alone and in combination, both at individual and population levels. METHODS AND RESULTS: Using two clinical trials in HF - the Prospective comparison of ARNI (Angiotensin Receptor-Neprilysin Inhibitor) with ACEI (Angiotensin-Converting Enzyme Inhibitor) to Determine Impact on Global Mortality and morbidity in HF trial (PARADIGM-HF) and the Aliskiren Trial to Minimize Outcomes in Patients with Heart Failure trials (ATMOSPHERE) - we identified the 10 most common comorbidities and examined 45 possible pairs. We calculated population attributable fractions (PAF) for all-cause death and relative excess risk due to interaction with Cox proportional hazard models. Of 15 066 patients in the study, 14 133 (93.7%) had at least one and 11 867 (78.8%) had at least two of the 10 most prevalent comorbidities. The greatest individual risk among pairs was associated with peripheral artery disease (PAD) in combination with stroke (hazard ratio [HR] 1.73; 95% confidence interval [CI] 1.28-2.33) and anaemia (HR 1.71; 95% CI 1.39-2.11). The combination of chronic kidney disease (CKD) and hypertension had the highest PAF (5.65%; 95% CI 3.66-7.61). Two pairs demonstrated significant synergistic interaction (atrial fibrillation with CKD and coronary artery disease, respectively) and one an antagonistic interaction (anaemia and obesity). CONCLUSIONS: In HF, the impact of multimorbidity differed at the individual patient and population level, depending on the prevalence of and the risk related to each comorbidity, and the interaction between individual comorbidities. Patients with coexistent PAD and stroke were at greatest individual risk whereas, from a population perspective, coexistent CKD and hypertension mattered most.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".