Prognostic Role of β-Blocker Selectivity and Dosage Regimens in Heart Failure Patients. Insights from the MECKI Score Database
Bibliographic record
Abstract
AIMS: The use of β-blockers represents a milestone in the treatment of heart failure with reduced ejection fraction (HFrEF). Few studies have compared β-blockers in HFrEF, and there is little data on the effects of different doses. The present study aimed to investigate in a large database of HFrEF patients (MECKI score database) the association of β-blocker treatment with a composite outcome of cardiovascular death, urgent heart transplantation or left ventricular assist device implantation, addressing the role of β-selectivity and dosage regimens. METHODS AND RESULTS: In 5242 HFrEF patients, we investigated the role of: (i) β-blocker treatment vs. non-β-blocker treatment, (ii) β1-/β2-receptor-blockers vs. β1-selective blockers, and (iii) daily β-blocker dose. Patients were followed for 3.58 years, and 1101 events (18.3%) were observed; 4435 patients (86.8%) were on β-blockers, while 807 (13.2%) were not. At 5 years, β-blocker-patients showed a better outcome than non-β-blocker-subjects [hazard ratio (HR) 0.48, P < 0.0001], while also considering potential confounders. A comparable prognosis was observed at 5 years in the β1-/β2-receptor-blocker (n = 2219) vs. β1-selective group (n = 2216) (HR 0.95, P = ns). A better prognosis was observed in high-dose (>2 5 mg carvedilol equivalent daily dose, n = 1005) patients than in both medium dose (12.5-25 mg, n = 1431) and low dose (<12.5 mg, n = 1960) (HR 1.97, P < 0.001; HR 1.95, P = 0.001, respectively), with no differences between the last two groups (HR 0.84, P = ns). CONCLUSION: In a large population of chronic HFrEF patients, β-blockers were associated with a more favourable prognosis without any difference between β1- and β2-receptor-blockers vs. β1-selective blockers. A better outcome was observed in subjects receiving a high daily dose.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".