Heart rate control and its predictors in patients with heart failure and sinus rhythm. Data from the European Society of Cardiology Long-Term Registry
Bibliographic record
Abstract
BACKGROUND: Higher resting heart rate (HR) in patients with heart failure (HF) and sinus rhythm (SR) is associated with increased mortality. In patients hospitalized for HF, the aim herein, was to assess the use and dosage of guideline-recommended HR lowering medications, HR control at discharge and predictors of HR control. METHODS: In the present study, were Polish participants of the European Society of Cardiology HF Long-Term (ESC-HF-LT) Registry. Those selected were hospitalized for HF, with reduced ejection fraction (HFrEF) and SR at discharge (n = 236). The patients were divided in two groups ( < 70 and ≥ 70 bpm). Logistic regression was used to identify the predictors of HR ≥ 70 bpm. RESULTS: Of patients with HFrEF and SR, 59% had HR ≥ 70 bpm at hospital discharge. At discharge, 96% and only 0.5% of the patients with HFrEF and SR received beta-blocker and ivabradine, respectively. In the HF groups < 70 and ≥ 70 bpm, only 11% and 4% of patients received beta-blocker target doses, respectively. There was no difference in the use of other guideline-recommended medications. Age, New York Heart Association class, HR on admission and lack of HR lowering medications were predictors of discharge HR ≥ 70 bpm. CONCLUSIONS: Heart rate control after hospitalization for HFrEF is unsatisfactory, which may be attributed to suboptimal doses of beta-blockers, and negligence in use other HR lowering drugs (including ivabradine).
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".