Impact of Multimorbidity on Mortality in Heart Failure With Mildly Reduced and Preserved Ejection Fraction
Bibliographic record
Abstract
BACKGROUND: How different combinations of comorbidities influence risk at the patient level and population level in patients with heart failure with mildly reduced ejection fraction/heart failure with preserved ejection fraction is unknown. We aimed to investigate the prevalence of different combinations of cardiovascular and noncardiovascular comorbidities (ie, multimorbidity) and associated risk of death at the patient level and population level. METHODS: Using patient-level data from the TOPCAT trial (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) and PARAGON-HF trial (Prospective Comparison of ARNI With ARB Global Outcomes in HF With Preserved Ejection Fraction), we investigated the 5 most common cardiovascular and noncardiovascular comorbidities and the resultant 45 comorbidity pairs. Cox proportional hazard models were used to calculate the population-attributable fractions for all-cause mortality and the relative excess risk due to interaction for each comorbidity pair. RESULTS: Among 6504 participants, 95.2% had at least 2 of the 10 most prevalent comorbidities. The comorbidity pair with the greatest patient-level risk was stroke and peripheral artery disease (adjusted hazard ratio, 1.88 [95% CI, 1.27-2.79]), followed by peripheral artery disease and chronic obstructive pulmonary disease (1.81 [95% CI, 1.31-2.51]), and coronary artery disease and stroke (1.67 [95% CI, 1.33-2.11]). The pair with the highest population-level risk was hypertension and chronic kidney disease (CKD; adjusted population-attributable fraction, 14.8% [95% CI, 9.2%-19.9%]), followed by diabetes and CKD (13.3% [95% CI, 10.6%-16.0%]), and hypertension and diabetes (11.9% [95% CI, 7.1%-16.5%). A synergistic interaction (more than additive risk) was found for the comorbidity pairs of stroke and coronary artery disease (relative excess risk due to interaction, 0.61 [95% CI, 0.13-1.09]), diabetes and CKD (relative excess risk due to interaction, 0.46 [95% CI, -0.15 to 0.77]), and obesity and CKD (relative excess risk due to interaction, 0.24 [95% CI, 0.01-0.46]). CONCLUSIONS: The risk associated with comorbidity pairs differs at the patient and population levels in heart failure with mildly reduced ejection fraction/heart failure with preserved ejection fraction. At the population level, hypertension, CKD, and diabetes account for the greatest risk, whereas at the patient level, polyvascular disease and chronic obstructive pulmonary disease are the most important.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".