Abstract 16279: Does the Number of Antianginals Influence Clinical Outcomes and Quality of Life in Stable Ischemic Heart Disease? Insights From the BARI2D Trial
Bibliographic record
Abstract
Introduction: Medical therapy is essential for managing stable ischemic heart disease (SIHD), including anti-anginals. remains unclear whether combining anti-anginal agents provides benefits beyond monotherapy in terms of QoL and cardiovascular outcomes. Hypothesis: The use of a single anti-anginal (β-Blockers, calcium channel blockers, or nitrates) is non-inferior to ≥2 anti-anginals Methods: We utilized data from the BARI-2D trial, which compared cardiovascular and QoL outcomes in patients with SIHD and diabetes mellitus (DM) randomized to revascularization with intensive medical therapy or intensive medical therapy alone. We categorized patients into three groups: ≥2 vs 1 vs 0 anti-anginals. We compared patient characteristics, QoL metrics, and cardiovascular endpoints at baseline and at 5 years, creating a multivariable model to adjust for key clinical confounders. Results: Among 2,368 patients, 348 patients (14.7%) were on 0 anti-anginals, 1,020 patients (43.1%) were on 1 anti-anginal, and 1,000 patients (42.2%) were on ≥2 anti-anginals at baseline. The most common anti-anginal class was β-Blockers. At baseline, patients on 0 anti-anginals had better QoL metrics than patients on ≥2 anti-anginals. However, at a 1-year follow-up, patients taking only 1 anti-anginal showed greater QoL improvement than those taking 0 anti-anginal; this superiority in QoL metrics was not seen in patients taking ≥2 anti-anginal agent, even after adjusting for multiple covariates such as age, heart failure, diabetes control and myocardial jeopardy index. (Figure 1) Lastly, at 5-year follow-up, after adjustment, there were no differences in all-cause mortality, major adverse cardiovascular events, or myocardial infarction between patients taking different numbers of anti-anginals. Conclusion: Treating adults with SIHD and DM with a single anti-anginal was at least as effective in improving QoL compared to two or more anti-anginal agents at one year of follow-up.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".