Effect of Carvedilol on Reduction in Heart Rate in Patients With Chronic Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Currently, β-blockers are used most frequently for the purpose of heart rate (HR) control in patients with atrial fibrillation (AF) in worldwide. Carvedilol is one of common β-blockers and known to be effective for hypertension and heart failure. However, little can be found the information about the HR-lowering effect of carvedilol in patients with AF without heart failure. Therefore, we conducted this study to investigate the effect of carvedilol on HR in 3-minute electrocardiogram (ECG) and total heart beats (THBs) in 24-hour Holter ECG monitoring in patients with persistent or permanent AF. METHODS: A total of 13 hypertensive patients (73 ± 12 years, 7 males) with AF and HR 90 bpm or more were enrolled. All patients received carvedilol from 5 mg/day. The dose of drug was titrated every 4 weeks and raised to 10 or 20 mg/day if HR was 80 bpm or more. RESULTS: Mean HR was decreased from 101.9 ± 13.9 to 85.2 ± 15.2 bpm (P < 0.05) after treatment with carvedilol. THBs were also significantly decreased from 128 to 115 × 1,000/day (P < 0.001). Percent reduction in HR and THBs were 13.9% and 10.7%, respectively. The scores of Atrial Fibrillation Quality of Life Questionnaire (AFQLQ) did not change. Only one patient was required to discontinue carvedilol due to congestive heart failure. CONCLUSIONS: We observed that carvedilol certainly reduced HR in patients with chronic AF. We believe that the effect of carvedilol on the reduction in HR can contribute to the management of AF patients treated with rate-control strategy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".