Impact of Age on Clinical Outcomes and Response to Serelaxin in Patients with Acute Heart Failure: An Analysis from the RELAX-AHF-2 Trial
Bibliographic record
Abstract
AIMS: Acute heart failure (AHF) is a major cause of hospitalizations and death in the elderly. However, elderly patients are often underrepresented in randomized clinical trials. We analysed the impact of age on clinical outcomes and response to treatment in patients enrolled in Relaxin in Acute Heart Failure (RELAX-AHF-2), a study that included older patients than in previous AHF trials. METHODS AND RESULTS: The RELAX-AHF-2 randomized patients admitted for AHF to infusion of serelaxin or placebo. We examined the association of pre-specified clinical outcomes and treatment effect according to age categories [(years): <65 (n = 1411), 65-74 (n = 1832), 75-79 (n = 1222), 80-84 (n = 1156) and ≥85 (n = 924)]. The mean age of the 6545 patients enrolled in RELAX-AHF-2 was 73.0 ± 11 years. The risk of all-cause and cardiovascular (CV) death (all p < 0.001) as well as the composite endpoint of CV death or heart failure/renal failure rehospitalization through 180 days (p = 0.002) and hospital discharge through day 60 (p = 0.013) were all directly associated with age categories. Age remained independently associated with outcomes after adjustment for clinical confounders and the results were consistent when age was analysed continuously. No clinically significant change in treatment effects of serelaxin was observed across age categories for the pre-specified endpoints (interaction p > 0.05). CONCLUSION: Elderly patients are at higher risk of short- and long-term CV outcomes after a hospitalization for AHF. Further efforts are needed to improve CV outcomes in this population.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".