Antianginal Efficacy of Ivabradine/Metoprolol Combination in Patients With Stable Angina
Bibliographic record
Abstract
Medical treatment is the main clinical strategy for controlling patients with chronic stable angina and improving their quality of life (QoL). Ivabradine treatment on top of metoprolol decreases angina symptoms and improves QoL in patients with stable angina and coronary artery disease (CAD). This is a post hoc analysis (636 CAD patients given ivabradine/metoprolol free combination) of a prospective, noninterventional study that included 2403 patients with CAD and stable angina. Data were recorded at baseline at 1 and 4 months after inclusion. Patient QoL was assessed using the EQ-5D questionnaire. From baseline to study completion; ivabradine administration on top of metoprolol decreased heart rate (HR) from 80.8 ± 9.6 to 64.2 ± 6.2 bpm (P < 0.001). Mean number of angina attacks decreased from 2.0 ± 2.0/wk to 0.2 ± 0.6/wk (P < 0.001), whereas nitroglycerin consumption decreased from 1.4 ± 1.9 times/wk to 0.1 ± 0.4 times/wk (P < 0.001). The percentage of patients in Canadian Cardiovascular Society angina class III to IV decreased from 15.4% to 1.9% (P < 0.001). The improvement of symptoms and angina class led to a significant 14.7-point increase in EQ-5D questionnaire score (P < 0.001). Patients with increased HR showed greater improvement (P = 0.001). Adherence to treatment during the entire trial was high (98%). Ivabradine combined with metoprolol significantly decreased angina symptoms and use of nitroglycerin in patients with stable angina and CAD, leading to improved QoL. The benefits observed with this combination explain the high rate of adherence to treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".