Real-World Data from the Use of Ranolazine in Patients with Stable Angina Pectoris: The RANGER Study
Bibliographic record
Abstract
Background: Although ranolazine has been available for years as a second-line treatment to reduce angina attacks in patients with stable angina pectoris, real-world data on the effectiveness, tolerability, and safety of ranolazine are limited. Methods: A non-interventional, prospective study was conducted to assess the effectiveness and safety of ranolazine. Patients eligible for enrolment had a baseline assessment between one and fourteen days after initiating ranolazine for the first time and a follow-up visit three months later. The primary endpoints comprised the weekly frequency of angina attacks, total adverse events, and ranolazine discontinuation rate. The secondary endpoints included the use of short-acting nitrates, changes on the Canadian Cardiovascular Society (CCS) angina classification score and quality of life scale score (QoL). Results: In total, 1101 patients were enrolled at 214 sites. Mean weekly angina attacks were reduced from 3.6 ± 2.9 to 0.4 ± 0.9 (p < 0.0001) and the mean weekly consumption of short-acting nitrates decreased by 1.7 ± 2.2 (p < 0.0001). CCS class and QoL were also improved (p < 0.0001). Adverse events were reported by 11 (1%) patients in total, while 2 of them (0.2%) were characterised as serious. Treatment was discontinued for various reasons in 23 patients (2.1%) after the follow-up period. Ranolazine treatment was equally effective in all subgroups tested, with larger benefits observed in patients with more frequent angina and CCS angina class III and IV. Up-titration of ranolazine during the study improved the outcomes. Conclusions: Ranolazine was well tolerated and effectively reduced angina attacks, with simultaneous improvement of the CCS class and QoL score in patients with stable angina.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".