Characteristics and Clinical Outcomes of Patients with Acute Heart Failure with a Supranormal Left Ventricular Ejection Fraction
Bibliographic record
Abstract
AIM: Recent data suggest that guideline-directed medical therapy of patients with heart failure (HF) with reduced ejection fraction (HFrEF) might improve clinical outcomes in patients with HF up to a left ventricular ejection fraction (LVEF) of 55-65%, whereas patients with higher LVEF do not seem to benefit. Recent data have shown that LVEF may have a U-shaped relation with outcome, with poorer outcome also in patients with supranormal values. This suggests that patients with supranormal LVEF may be a distinctive group of patients. METHODS AND RESULTS: RELAX-AHF-2 was a multicentre, placebo-controlled trial on the effects of serelaxin on 180-day cardiovascular (CV) mortality and worsening HF at day 5 in patients with acute HF. Echocardiograms were performed at hospital admission in 6128 patients: 155 (2.5%) patients were classified as HF with supranormal ejection fraction (HFsnEF; LVEF >65%), 1440 (23.5%) as HF with preserved ejection fraction (HFpEF; LVEF 50-65%), 1353 (22.1%) as HF with mildly reduced ejection fraction (HFmrEF; LVEF 41-49%) and 3180 (51.9%) as HFrEF (LVEF <40%). Patients with HFsnEF compared to HFpEF were more often women, had higher prevalence of non-ischaemic HF, had lower levels of natriuretic peptides, were less likely to be treated with beta-blockers and had higher blood urea nitrogen plasma levels. All-cause mortality was not statistically different between groups, although patients with HFsnEF had the highest numerical rate. A declining trend was seen in the proportion of 180-day deaths due to CV causes from HFrEF (290/359, 80.8%) to HFsnEF (14/24, 58.3%). The reverse was observed with death from non-CV causes. No treatment effect of serelaxin was observed in any of the subgroups. CONCLUSIONS: In this study, only 2.5% of patients were classified as HFsnEF. HFsnEF was primarily characterized by female sex, lower natriuretic peptides and a higher risk of non-CV death.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".