Relationships among achieved heart rate, β-blocker dose and long-term outcomes in patients with heart failure with atrial fibrillation
Bibliographic record
Abstract
OBJECTIVE: Higher β-blocker dose and lower heart rate are associated with decreased mortality in patients with systolic heart failure (HF) and sinus rhythm. However, in the 30% of patients with HF with atrial fibrillation (AF), whether β-blocker dose or heart rate predict mortality is less clear. We assessed the association between β-blocker dose, heart rate and all-cause mortality in patients with HF and AF. METHODS: We performed a retrospective cohort study in 935 patients (60% men, mean age 74, 44.7% with reduced left ventricular ejection fraction (LVEF)) discharged with concurrent diagnoses of HF and AF. We used Cox models to test independent associations between higher versus lower predischarge heart rate (dichotomised at 70/min) and higher versus lower β-blocker dose (dichotomised at 50% of the evidence-based target), with the primary composite end point of mortality or cardiovascular rehospitalisation over a median of 2.9 years. All analyses were stratified by the presence of left ventricular systolic dysfunction (LVEF≤40%). RESULTS: After adjustment for covariates, neither β-blocker dose nor predischarge heart rate was associated with the primary composite end point. However, tachycardia at admission (heart rate >120/min) was associated with a reduced risk of the composite outcome in patients with both reduced LVEF (adjusted HR 0.67, 95% CI 0.52 to 0.88, p<0.01) and preserved LVEF (adjusted HR 0.79, 95% CI 0.64 to 0.98, p=0.04). CONCLUSIONS: We found no associations between predischarge heart rate or β-blocker dosage and clinical outcomes in patients with recent hospitalisations for HF and AF.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".