Effect of Comorbidities on Outcomes and Angiotensin Converting Enzyme Inhibitor Effects in Patients with Predominantly Left Ventricular Dysfunction and Heart Failure
Bibliographic record
Abstract
AIMS: Comorbidities are frequent in heart failure and impact outcomes. It is not known whether comorbidities are associated with outcomes in asymptomatic left ventricular dysfunction compared to clinical heart failure and whether comorbidities interfere with treatment effects. Our objective was to assess comorbidities and their effects on outcomes in predominantly asymptomatic populations without previous heart failure treatment of the SOLVD prevention trial, compared to symptomatic heart failure patients of SOLVD treatment and to evaluate associations to the effect of enalapril. METHODS AND RESULTS: This post hoc analysis from the SOLVD prevention and SOLVD treatment trials includes 4228 patients with left ventricular dysfunction and 2569 patients with heart failure. The preexisting comorbidities hypertension, diabetes mellitus, pulmonary disease, angina pectoris, renal impairment, and anaemia were similar in SOLVD treatment and SOLVD prevention, with a higher prevalence in SOLVD treatment. Comorbidities are significantly associated with the primary composite of SOLVD time to death or heart failure hospitalization (SOLVD prevention: HR 4.8, CI: 3.2-7.18, P < 0.0001; SOLVD treatment: HR 2.9, CI: 2.12-3.95, P < 0.0001 for more than four comorbidities vs. no comorbidities), and to death, heart failure hospitalization, and cardiovascular death where the effect of the number of coexisting comorbidities was additive. There was no significant interaction of comorbidities with treatment effects of enalapril. CONCLUSION: Comorbidities increased events in asymptomatic left ventricular dysfunction and in symptomatic heart failure, but did not interfere with the effects of enalapril. Comorbidities need to be adequately addressed in clinical trials, which should also involve non-cardiac treatments in order to improve outcome for heart failure 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".