Intensity of Guideline-Directed Medical Therapy for Coronary Heart Disease and Ischemic Heart Failure Outcomes
Bibliographic record
Abstract
PURPOSE: The impact of guideline-directed medical therapy for coronary heart disease in those hospitalized with acute heart failure is unknown. METHODS: We studied guideline-directed medical therapies for coronary disease: angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs), beta-adrenoreceptor antagonists, antiplatelet agents or anticoagulants, and statins. Using inverse probability of treatment weighting the propensity score, we examined associations of guideline-directed medical therapy intensity (categorized as low [0-1], high [2-3], or very high [4] number of drugs) with mortality in 1873 patients with angina, troponin elevation, or prior myocardial infarction. RESULTS: At discharge, 0-1, 2-3, and 4 medications were prescribed in 467 (25%), 705 (38%), and 701 (37%) patients, respectively. Relative to those prescribed 0-1 drugs (reference), all-cause mortality was lower with 2-3 (hazard ratio [HR] 0.48, 95% confidence interval [CI] 0.28-0.84, P = 0.009) or all 4 drug classes (HR 0.56, 95% CI 0.33-0.96, P = 0.034) over 181-365 days, with similar reductions present from 0-180 days. In those with heart failure with preserved ejection fraction, mortality trended lower with 2-3 drug classes (HR 0.43, 95% CI 0.18-1.02, P = 0.054) and was significantly reduced with 4 drugs (HR 0.32, 95%CI 0.12-0.84, P = 0.021) during 0-180 day follow-up. In heart failure with reduced ejection fraction, all-cause mortality was reduced during both 0-180 and 181-365 days when discharged on 2-3 (HR 0.30 for 181-365 days, 95%CI 0.14-0.64, P = 0.002) or all 4 drug classes (HR 0.43, 95%CI 0.19-0.95, P = 0.038). CONCLUSIONS: Increasing guideline-directed medical therapy intensity for coronary heart disease resulted in lower mortality in patients with acute ischemic heart failure with both preserved and reduced ejection fractions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".