Prognostic role of mitral regurgitation in patients with advanced heart failure
Bibliographic record
Abstract
AIM: The impact of mitral regurgitation (MR) in patients with advanced heart failure (HF) is poorly known. We aimed to evaluate the impact of MR on clinical outcomes of a real-world, contemporary, multicentre population with advanced HF. METHODS: The HELP-HF registry enrolled patients with HF and at least one "I NEED HELP" criterion, at four Italian centres between January 2020 and November 2021. The population was stratified by none/mild MR vs. moderate MR vs. severe MR. Outcomes of interest were all-cause, cardiovascular (CV) death, the composite of all-cause death or first HF hospitalization, first HF hospitalization and recurrent HF hospitalizations. RESULTS: Among 1079 patients, 429 (39.8%) had none/mild MR, 443 (41.1%) had moderate MR and 207 (19.2%) had severe MR. Patients with severe MR were most likely to be inpatients, present with cardiogenic shock, need intravenous loop diuretics and inotropes/vasopressors, have lower ejection fraction and higher natriuretic peptides. Estimated rates of all-cause death, CV death, and the composite of all-cause death or first HF hospitalization at 1 year increased with increasing MR severity. Compared with no/mild MR, severe MR was independently associated with an increased risk of CV death (adjusted HR 1.61, 95% CI 1.04-2.51, p = 0.033) and recurrent HF hospitalizations (adjusted HR 1.49, 95% CI 1.08-2.06, p = 0.015), but not with and increased risk of all-cause death, first HF hospitalization and composite outcome. CONCLUSIONS: In unselected patients with advanced HF, severe MR was common and independently associated with an increased risk of CV death and of recurrent HF hospitalizations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".