Relationship Between Heart Rate and Outcomes in Patients in Sinus Rhythm or Atrial Fibrillation with Heart Failure and Reduced Ejection Fraction
Bibliographic record
Abstract
AIMS: To investigate the relationship between heart rate and outcomes in heart failure and reduced ejection fraction (HFrEF) patients in sinus rhythm (SR) and atrial fibrillation (AF) adjusting for natriuretic peptide concentration, a powerful prognosticator. METHODS AND RESULTS: Of 13 562 patients from two large HFrEF trials, 10 113 (74.6%) were in SR and 3449 (25.4%) in AF. The primary endpoint was the composite of cardiovascular death or heart failure hospitalization. Heart rate was analysed as a categorical (tertiles, T1-3) and continuous variable (per 10 bpm), separately in patients in SR and AF. Outcomes were adjusted for prognostic variables, including N-terminal prohormone of B-type natriuretic peptide (NT-proBNP), and also examined using change from baseline heart rate to 1 year (≤ -10 bpm, ≥ +10 bpm, < ±10 bpm). SR patients with a higher heart rate had worse symptoms and quality of life, more often had diabetes and higher NT-proBNP concentrations. They had higher risk of the primary endpoint [T3 vs. T1 adjusted hazard ratio (HR) 1.50, 95% confidence interval (CI) 1.35-1.66; P < 0.001; per 10 bpm: 1.12, 95% CI 1.09-1.16; P < 0.001]. In SR, heart rate was associated with a relatively higher risk of pump failure than sudden death (adjusted HR per 10 bpm 1.17, 95% CI 1.09-1.26; P < 0.001 vs. 1.07, 95% CI 1.02-1.13; P = 0.011). Heart rate was not predictive of any outcome in AF. CONCLUSIONS: In HFrEF, an elevated heart rate was an independent predictor of adverse cardiovascular outcomes in patients in SR, even after adjustment for NT-proBNP. There was no relationship between heart rate and outcomes in AF. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifiers NCT01035255 and NCT00853658.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".