Association of left ventricular ejection fraction with worsening renal function in patients with acute heart failure: insights from the <scp>RELAX‐AHF</scp> ‐2 study
Bibliographic record
Abstract
AIMS: Whether risk of worsening renal function (WRF) during acute heart failure (AHF) hospitalization or the association between in-hospital WRF and post-discharge outcomes vary according to left ventricular ejection fraction (LVEF) is uncertain. We assessed incidence of WRF, factors related to its development and impact of WRF on post-discharge outcomes across the spectrum of LVEF in patients enrolled in RELAX-AHF-2. METHODS AND RESULTS: A total of 6112 patients who had LVEF measured on admission and renal function determined prospectively during hospitalization were included. WRF, defined as a rise in serum creatinine ≥0.3 mg/dL from baseline through day 5, occurred in 1722 patients (28.2%). Incidence increased progressively from lowest to highest LVEF quartile (P < 0.001). After baseline adjustment, WRF risk in Q4 (LVEF >50%) remained significantly greater than in Q1 (LVEF ≤29%; hazard ratio 1.2, 95% confidence interval 1-1.43; P = 0.050). Age and comorbidity burden including chronic kidney disease increased as LVEF increased. Neither admission haemodynamic abnormalities, extent of diuresis during hospitalization nor residual congestion explained the increased incidence of WRF in patients with higher LVEF. Serelaxin treatment and diuretic responsiveness were associated with reduced risk of WRF in all LVEF quartiles. WRF in patients in the upper three LVEF quartiles increased risk of post-discharge events. CONCLUSIONS: Worsening renal function incidence during AHF hospitalization increases progressively with LVEF. Greater susceptibility of patients with higher LVEF to WRF appears more related to their advanced age and worse underlying kidney function rather than haemodynamic or treatment effects. WRF is associated with increased risk of post-discharge events except in patients in the lowest LVEF quartile.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".