Hepatorenal Dysfunction Identifies High-Risk Patients with Acute Heart Failure: Insights from the RELAX-AHF Trial
Bibliographic record
Abstract
AIMS: Episodes of acute heart failure (AHF) may lead to end-organ dysfunction. In this post hoc analysis of the Relaxin in Acute Heart Failure trial, we used the MELD-XI (Model of End-Stage Liver Dysfunction) score to examine hepatorenal dysfunction in patients with AHF. METHODS AND RESULTS: On admission, the MELD-XI score was elevated (abnormal) in 918 (82%) patients, with 638 (57%) having isolated renal dysfunction (creatinine > 1 mg/dL), 73 (6.5%) isolated liver dysfunction (bilirubin > 1 mg/dL), and 207 (18.5%) coexisting dysfunction of the kidneys and the liver (both creatinine and bilirubin > 1 mg/dL). The percentage of patients with elevated MELD-XI score remained constant through a 60 day follow-up, as we observed a gradual decrease of liver dysfunction prevalence, counterbalanced by an increase in renal dysfunction. Serelaxin treatment was associated with a lower MELD-XI score on Day 2 and Day 5 (both P < 0.05), but this difference vs. placebo disappeared during longer follow-up. In the multivariable model, an elevated MELD-XI score on admission was associated with higher 180 day mortality: hazard ratios (95% confidence interval) for cardiovascular death were 3.10 (1.22-7.87), and for all-cause death 2.47 (1.19-5.15); both P < 0.05. The addition of the MELD-XI score to a prespecified prognostic model increased the discrimination of the model for all-cause death, but the increment in the C-index was only modest: 0.013 (P = 0.02). CONCLUSIONS: In patients with AHF, hepatorenal dysfunction is prevalent and related to poor outcome. The MELD-XI score is a useful prognosticator in AHF.
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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 | 0.004 | 0.004 |
| 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".