Impact of Mitral Regurgitation in Patients with Acute Heart Failure: Insights from the RELAX-AHF-2 Trial
Bibliographic record
Abstract
AIMS: The impact of mitral regurgitation (MR) in patients hospitalized for acute heart failure (AHF) is not well established. We assessed the role of MR in patients enrolled in the Relaxin in Acute Heart Failure 2 (RELAX-AHF-2) trial. METHODS AND RESULTS: Patients enrolled in RELAX-AHF-2 with available data regarding MR status were included in this analysis. Baseline characteristics, in-hospital data, and clinical outcomes through 180-day follow-up were evaluated. The impact of moderate/severe MR was assessed. Among 6420 AHF patients with known MR status, 1810 patients (28.2%) had moderate/severe MR. Compared to patients with no/mild MR, those with moderate/severe MR were more likely to have history of heart failure (HF), prior HF hospitalization, more comorbidities, symptoms/signs of HF, lower left ventricular ejection fraction and higher N-terminal pro-B-type natriuretic peptide levels. Moderate/severe MR was associated with longer length of hospital stay, higher rates of residual dyspnoea, increased jugular venous pressure through the index hospitalization and a higher unadjusted risk of the composite of cardiovascular (CV) death or rehospitalization for HF/renal failure (RF) through 180 days (crude hazard ratio [HR] 1.15, 95% confidence interval [CI] 1.03-1.27, p = 0.01). The association between moderate/severe MR and poorer outcomes was not maintained in a multivariable model including several covariates of interest (adjusted HR 1.03, 95% CI 0.91-1.17, p = 0.65). Similar findings were observed for HF/RF rehospitalization alone. CONCLUSIONS: In patients with AHF, moderate/severe MR was associated with a worse clinical profile but did not have an independent prognostic impact on clinical outcomes.
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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) | 0.002 | 0.002 |
| 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".