Relationship Between Left Ventricular Ejection Fraction and Cardiovascular Outcomes Following Hospitalization for Heart Failure: Insights from the RELAX-AHF-2 Trial
Bibliographic record
Abstract
AIMS: Although left ventricular ejection fraction (LVEF) is routinely used to categorize patients with heart failure (HF), whether it predicts outcomes after hospitalization for acute heart failure (AHF) is uncertain. Consequently, we assessed the relationship between LVEF and cardiovascular (CV) outcomes in a large, well characterized cohort of patients hospitalized for AHF. METHODS AND RESULTS: The 6128 patients from the RELAX-AHF-2 trial who had LVEF measured during AHF hospitalization were separated into LVEF quartiles and the relationship between LVEF and a composite of CV mortality and rehospitalization for HF or renal failure through 180 days was assessed. We found progressively lower risk for this composite outcome as LVEF increased (hazard ratio 0.95, 95% confidence interval 0.93-0.98 per 5% LVEF increase, P < 0.001) that was driven predominantly by decreased risk for rehospitalization. The smoothed spline curve depicting risk remained stable as LVEF decreased until reaching approximately 40%, at which point risk increased progressively with further reductions in LVEF. Significant differences between LVEF quartiles for post-discharge CV risk were seen in patients with an ischaemic aetiology or with a history of HF preceding index hospitalization, but were less robust in patients with non-ischaemic aetiology and absent in those with de novo HF. CONCLUSION: In patients hospitalized with AHF, CV events over 180 days were more frequent in patients with lower LVEF. This was due predominantly to a significant increase in risk for HF/renal failure rehospitalization but not in either CV or all-cause mortality. LVEF had greater prognostic value in patients with ischaemic aetiology or pre-existing HF.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".